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The human brain undergoes rapid development during the first years of life. Beginning in utero, a wide array of biological, social, and environmental factors can have lasting impacts on brain structure and function. To understand how prenatal and early life experiences alter neurodevelopmental trajectories and shape health outcomes, several NIH Institutes, Centers, and Offices collaborated to support and launch the HEALthy Brain and Child Development (HBCD) Study. The HBCD Study is a multi-site prospective longitudinal cohort study, that will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Influenced by the success of the ongoing Adolescent Brain Cognitive Development SM Study (ABCD Study®) and in partnership with the NIH Helping to End Addiction Long-term® Initiative, or NIH HEAL Initiative®, the HBCD Study aims to establish a diverse cohort of over 7000 pregnant participants to understand how early life experiences, including prenatal exposure to addictive substances and adverse social environments as well as their interactions with an individual's genes, can affect neurodevelopmental trajectories and outcomes. Knowledge gained from the HBCD Study will help identify targets for early interventions and inform policies that promote resilience and mitigate the neurodevelopmental effects of adverse childhood experiences and environments., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Julia L. Zehr reports a relationship with Jazz Pharmaceuticals Inc that includes: equity or stocks. Co-author’s spouse is employed by Jazz Pharmaceuticals, Inc - JLZ If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2024. Published by Elsevier Ltd.)

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges-an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Hepatitis C virus (HCV) infection significantly contributes to global hepatocellular carcinoma (HCC) incidence. N-Acetylcysteine (NAC), known for its antioxidant properties, is a potential therapeutic agent. However, evidence on its efficacy in reducing HCC risk among HCV patients is limited. A retrospective cohort analysis using Taiwan's National Health Insurance Research Database (2008-2018) included ≥18-year-old HCV patients. NAC usage (≥28 cumulative defined daily doses [cDDDs]) was assessed for its association with HCC risk using Cox regression models and propensity score matching. The study comprised 269,647 HCV patients, with detailed NAC dosage characterization and hazard ratios (HRs) for HCC risk. Post-matching, NAC usage emerged as the significant predictor of reduced HCC risk (adjusted HR: 0.39, 95% CI: 0.37-0.41, P<0.0001). Dose-response analysis showed reduced HCC risk with increasing cDDDs of NAC (P<0.0001). Higher daily NAC dosage (≥1 DDD) was associated with significantly lower HCC risk (adjusted HR: 0.33, 95% CI: 0.31-0.36, P<0.0001). The study provides compelling evidence for NAC's potential in reducing HCC risk among HCV patients. Insights into dose-dependent effects and optimal daily intensity thresholds offer valuable directions for future therapeutic strategies and clinical trials targeting HCC burden in HCV-infected individuals., Competing Interests: None., (AJCR Copyright © 2024.)

Hepatitis B virus (HBV) infection is a leading risk factor for hepatocellular carcinoma (HCC), contributing to cancer development through direct genomic integration and chronic inflammation. N-acetylcysteine (NAC), known for its antioxidant properties, is widely utilized in cancer prevention. However, clinical evidence regarding its protective effect against HCC in HBV carriers remains sparse. In this retrospective cohort study spanning 2008 to 2018, we utilized Taiwan's National Health Insurance Research Database (NHIRD) to include 1,061,174 chronic HBV carriers. Participants were stratified into NAC users and non-users using Propensity Score Matching. We assessed the incidence of HCC in both cohorts, examining the relationship between NAC usage duration and HCC incidence, and evaluating the dose-response effect. NAC users exhibited a significantly lower risk of developing HCC (adjusted hazard ratio [aHR]: 0.38; 95% confidence interval [CI]: 0.36-0.40; P < 0.0001). A dose-response relationship was evident, with higher cumulative defined daily doses (cDDDs) of NAC correlating with reduced HCC risk, revealing a significant trend (P < 0.0001). Notably, a daily NAC intensity of > 1.4 DDDs was associated with a decreased risk of HCC in HBV patients. Our results demonstrate that the use of NAC, in a dose-dependent manner, is intricately linked with a diminished incidence of HCC in individuals chronically infected with the HBV., Competing Interests: None., (AJCR Copyright © 2024.)

This study aimed to evaluate the impact of different pre-transplant local treatments on the survival of liver transplantation (LTx) recipients with BCLC Stage A Hepatocellular Carcinoma (HCC). We analyzed data from the Taiwan Cancer Registry and National Health Insurance Research Databases spanning 2012 to 2018. Employing propensity score matching, patients were categorized into three groups: those receiving local treatments (180 patients), hepatectomy (179 patients), and combined treatments (180 patients). The primary outcomes were overall mortality and HCC-specific death, assessed using time-varying Cox regression models and Kaplan-Meier survival analysis. During a median follow-up period of 3.92 years, all-cause mortality rates were observed as 74.44% for local treatments, 42.46% for hepatectomy, and 65.00% for combined treatments. HCC-specific mortality rates followed a similar pattern at 65.00%, 39.11%, and 59.44%, respectively. Adjusted hazard ratios demonstrated significantly elevated mortality risks associated with local and combined treatments compared to hepatectomy. Notably, the 2-year overall and HCC-specific survival rates were highest in the hepatectomy group, surpassing those observed in both the combined treatment and local treatment groups. The findings of our study highlight that for patients with BCLC Stage A HCC, undergoing hepatectomy prior to LTx is associated with superior survival outcomes compared to solely local treatments. This underscores the importance of considering hepatectomy as a vital component of the treatment strategy in this patient population., Competing Interests: None., (AJCR Copyright © 2024.)

Purpose: To evaluate whether providing clinicians with an artificial intelligence (AI)-based vascular severity score (VSS) improves consistency in the diagnosis of plus disease in retinopathy of prematurity (ROP)., Design: Multireader diagnostic accuracy imaging study., Participants: Eleven ROP experts, 9 of whom had been in practice for 10 years or more., Methods: RetCam (Natus Medical Incorporated) fundus images were obtained from premature infants during routine ROP screening as part of the Imaging and Informatics in ROP study between January 2012 and July 2020. From all available examinations, a subset of 150 eye examinations from 110 infants were selected for grading. An AI-based VSS was assigned to each set of images using the i-ROP DL system (Siloam Vision). The clinicians were asked to diagnose plus disease for each examination and to assign an estimated VSS (range, 1-9) at baseline, and then again 1 month later with AI-based VSS assistance. A reference standard diagnosis (RSD) was assigned to each eye examination from the Imaging and Informatics in ROP study based on 3 masked expert labels and the ophthalmoscopic diagnosis., Main Outcome Measures: Mean linearly weighted κ value for plus disease diagnosis compared with RSD. Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) for labels 1 through 9 compared with RSD for plus disease., Results: Expert agreement improved significantly, from substantial (κ value, 0.69 [0.59, 0.75]) to near perfect (κ value, 0.81 [0.71, 0.86]), when AI-based VSS was integrated. Additionally, a significant improvement in plus disease discrimination was achieved as measured by mean AUC (from 0.94 [95% confidence interval (CI), 0.92-0.96] to 0.98 [95% CI, 0.96-0.99]; difference, 0.04 [95% CI, 0.01-0.06]) and AUPR (from 0.86 [95% CI, 0.81-0.90] to 0.95 [95% CI, 0.91-0.97]; difference, 0.09 [95% CI, 0.03-0.14])., Conclusions: Providing ROP clinicians with an AI-based measurement of vascular severity in ROP was associated with both improved plus disease diagnosis and improved continuous severity labeling as compared with an RSD for plus disease. If implemented in practice, AI-based VSS could reduce interobserver variability and could standardize treatment for infants with ROP., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. All rights reserved.)

Objective: Isolated retinal neovascularization (IRNV) is a common finding in patients with stage 2 and 3 retinopathy of prematurity (ROP). This study aimed to further classify the clinical course and significance of these lesions (previously described as "popcorn" based on clinical appearance) in patients with ROP as visualized with ultrawidefield OCT (UWF-OCT)., Design: Single center, retrospective case series., Participants: Images were collected from 136 babies in the Oregon Health and Science University neonatal intensive care unit., Methods: A prototype UWF-OCT device captured en face scans (>140°), which were reviewed for the presence of IRNV along with standard zone, stage, and plus classification. In a cross-sectional analysis we compared demographics and the clinical course of eyes with and without IRNV. Longitudinally, we compared ROP severity using a clinician-assigned vascular severity score (VSS) and compared the risk of progression among eyes with and without IRNV using multivariable logistic regression., Main Outcome Measures: Differences in clinical demographics and disease progression between patients with and without IRNV., Results: Of the 136 patients, 60 developed stage 2 or worse ROP during their disease course, 22 of whom had IRNV visualized on UWF-OCT (37%). On average, patients with IRNV had lower birth weights (BWs) (660.1 vs. 916.8 g, P = 0.001), gestational age (GA) (24.9 vs. 26.1 weeks, P = 0.01), and were more likely to present with ROP in zone I (63.4% vs. 15.8%, P < 0.001). They were also more likely to progress to stage 3 (68.2% vs. 13.2%, P < 0.001) and receive treatment (54.5% vs. 15.8%, P = 0.002). Eyes with IRNV had a higher peak VSS (5.61 vs. 3.73, P < 0.001) and averaged a higher VSS throughout their disease course. On multivariable logistic regression, IRNV was independently associated with progression to stage 3 (P = 0.02) and requiring treatment (P = 0.03), controlling for GA, BW, and initial zone 1 disease., Conclusions: In this single center study, we found that IRNV occurs in higher risk babies and was an independent risk factor for ROP progression and treatment. These findings may have implications for OCT-based ROP classifications in the future., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. All rights reserved.)

Comprehensive visualization of retina morphology is essential in the diagnosis and management of retinal diseases in pediatric populations. Conventional imaging techniques often face challenges in effectively capturing the peripheral retina, primarily due to the limitations in current optical designs, which lack the necessary field of view to characterize the far periphery. To address this gap, our study introduces a novel ultra-widefield optical coherence tomography angiography (OCTA) system. This system, specifically tailored for pediatric applications, incorporates an ultrahigh-speed 800 kHz swept-source laser. The system's innovative design achieves a 140° field of view while maintaining excellent optical performance. Over the last 15 months, we have conducted 379 eye examinations on 96 babies using this system. It demonstrates marked efficacy in the diagnosis of retinopathy of prematurity, providing detailed and comprehensive peripheral retinal angiography. The capabilities of the ultra-widefield handheld OCTA system in enhancing the clarity and thoroughness of retina vascularization assessments have significantly improved the precision of diagnoses and the customization of treatment strategies. Our findings underscore the system's potential to advance pediatric ophthalmology and broaden the scope of retinal imaging., Competing Interests: SN: OHSU (P); YaJ: Genentech (F, P); Optovue/Visionix (P, R); Optos (P). DH: Visionix (F, P, R); Genentech (P, R); Intalight (F); Canon (F); Cylite (F); JPC: OHSU (P); Siloam Vision (F); YiJ: OHSU (P); Siloam Vision (F). All other authors declare they have no competing interests., (© 2024 Optica Publishing Group.)

Importance: Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening., Objective: To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP., Design, Setting, and Participants: This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023., Exposures: An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine., Main Outcomes and Measures: The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels., Results: The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis., Conclusions and Relevance: Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.

The Epstein-Barr virus (EBV) is accepted as a primary risk factor for certain nasopharyngeal carcinoma (NPC) subtypes, where the virus persists in a latent stage which is thought to contribute to tumorigenesis. Current treatments are sub-optimal, and recurrence occurs in many cases. An alternative therapeutic concept is aimed at triggering the lytic cycle of EBV selectively in tumor cells as a means to add clinical benefit. While compounds able to stimulate the lytic cascade have been identified, their clinical application so far has been limited. We are developing a novel anticancer molecule, NEO212, that was generated by covalent conjugation of the alkylating agent temozolomide (TMZ) to the naturally occurring monoterpene perillyl alcohol (POH). In the current study, we investigated its potential to trigger the lytic cycle of EBV in NPC cells in vitro and in vivo. We used the established C666.1 cell line and primary patient cells derived from the brain metastasis of a patient with NPC, both of which harbored latent EBV. Upon treatment with NEO212, there was an increase in EBV proteins Zta and Ea-D, key markers of the lytic cycle, along with increased levels of CCAAT/enhancer-binding protein homologous protein (CHOP), a marker of endoplasmic reticulum (ER) stress, followed by the activation of caspases. These effects could also be confirmed in tumor tissue from mice implanted with C666.1 cells. Towards a mechanistic understanding of these events, we used siRNA-mediated knockdown of CHOP and inclusion of anti-oxidant compounds. Both approaches blocked lytic cycle induction by NEO212. Therefore, we established a sequence of events, where NEO212 caused reactive oxygen species (ROS) production, which triggered ER stress and elevated the levels of CHOP, which was required to stimulate the lytic cascade of EBV. Inclusion of the antiviral agent ganciclovir synergistically enhanced the cytotoxic impact of NEO212, pointing to a potential combination treatment for EBV-positive cancers which should be explored further. Overall, our study establishes NEO212 as a novel agent able to stimulate EBV's lytic cycle in NPC tumors, with implications for other virus-associated cancers.

Objective: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction., Materials and Methods: We developed and evaluated multimodal deep learning models for multiclass glaucoma trabeculectomy surgery outcomes using both structured EHR data and free-text operative notes. We compare those to baseline models that use structured EHR data exclusively, or neural network models that leverage only operative notes., Results: The multimodal neural network had the highest performance with a macro AUROC of 0.750 and F1 score of 0.583. It outperformed the baseline machine learning model with structured EHR data alone (macro AUROC of 0.712 and F1 score of 0.486). Additionally, the multimodal model achieved the highest recall (0.692) for hypotony surgical failure, while the surgical success group had the highest precision (0.884) and F1 score (0.775)., Discussion: This study shows that operative notes are an important source of predictive information. The multimodal predictive model combining perioperative notes and structured pre- and post-op EHR data outperformed other models. Multiclass surgical outcome prediction can provide valuable insights for clinical decision-making., Conclusions: Our results show the potential of deep learning models to enhance clinical decision-making for postoperative management. They can be applied to other specialties to improve surgical outcome predictions., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10 -8 ) and 9 with significance of p < 5×10 -6 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10 -9 ); Hispanic and European Ancestry infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we identify a novel locus at GLI3 with relevance to retinal biology, supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity., (© 2024. The Author(s).)

We introduce a new concept of panoramic retinal (panretinal) optical coherence tomography (OCT) imaging system with a 140° field of view (FOV). To achieve this unprecedented FOV, a contact imaging approach was used which enabled faster, more efficient, and quantitative retinal imaging with measurement of axial eye length. The utilization of the handheld panretinal OCT imaging system could allow earlier recognition of peripheral retinal disease and prevent permanent vision loss. In addition, adequate visualization of the peripheral retina has a great potential for better understanding disease mechanisms regarding the periphery. To the best of our knowledge, the panretinal OCT imaging system presented in this manuscript has the widest FOV among all the retina OCT imaging systems and offers significant values in both clinical ophthalmology and basic vision science.

Purpose: Retinopathy of prematurity (ROP) is one of the leading causes of blindness in children. Although the role of oxygen in the pathophysiology of ROP is well established, a precise understanding of the dynamic relationship between oxygen exposure ROP incidence and severity is lacking. The purpose of this study was to evaluate the correlation between time-dependent oxygen variables and the onset of ROP., Design: Retrospective cohort study., Participants: Two hundred thirty infants who were born at a single academic center and met the inclusion criteria were included. Infants are mainly born between January 2011 and October 2022., Methods: Patient data were extracted from electronic health records (EHRs), with sufficient time-dependent oxygen data. Clinical outcomes for ROP were recorded as none/mild or moderate/severe (defined as type II or worse). Mixed-effects linear models were used to compare the 2 groups in terms of dynamic oxygen variables, such as daily average and the coefficient of variation (COV) fraction of inspired oxygen (FiO 2 ). Support vector machine (SVM) and long-short-term memory (LSTM)-based multimodal models were trained with fivefold cross-validation to predict which infants would develop moderate/severe ROP. Gestational age (GA), birth weight, and time-dependent oxygen variables were used to develop predictive models., Main Outcome Measures: Model cross-validation performance was evaluated by computing the mean area under the receiver operating characteristic (AUROC) curve, precision, recall, and F1 score., Results: We found that both daily average and COV of FiO 2 were associated with more severe ROP (adjusted P  < 0.001). With fivefold cross-validation, the multimodal LSTM models had higher performance than the best static models (SVM using GA and 3 average FiO 2 features) and SVM models trained on GA alone (mean AUROC = 0.89 ± 0.04 vs. 0.86 ± 0.05 vs. 0.83 ± 0.04)., Conclusions: The development of severe ROP might not only be influenced by oxygen exposure but also by its fluctuation, which provides direction for future study of pathophysiological factors associated with severe ROP development. Additionally, we demonstrated that multimodal neural networks can be a method to extract useful information from time-series data, which may be a valuable methodology for the investigation of other diseases using EHR data., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (© 2023 Published by Elsevier Inc. on behalf of the American Academy of Ophthalmology.)

Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic syndrome and poses a significant threat to patients with type 2 diabetes mellitus (T2DM) and metabolic dysregulation. Statins exert anti-inflammatory, antioxidative and antithrombotic effects that target mechanisms underlying NAFLD. However, the protective effects of the different doses, intensities and types of statins on the incidence of NAFLD-related decompensated liver cirrhosis (DLC) in patients with T2DM remain unclear., Methods: This study used the data of patients with T2DM who were non-HBV and non-HCV carriers from a national population database to examine the protective effects of statin use on DLC incidence through propensity score matching. The incidence rate (IR) and incidence rate ratios (IRRs) of DLC in patients with T2DM with or without statin use were calculated., Results: A higher cumulative dose and specific types of statins, namely rosuvastatin, pravastatin, atorvastatin, simvastatin and fluvastatin, reduced the risk of DLC in patients with T2DM. Statin use was associated with a significant reduction in the risk of DLC (HR: .65, 95% CI: .61-.70). The optimal daily intensity of statin use with the lowest risk of DLC was .88 defined daily dose (DDD)., Conclusions: The results revealed the protective effects of specific types of statins on DLC risk in patients with T2DM and indicated a dose-response relationship. Additional studies are warranted to understand the specific mechanisms of action of different types of statins and their effect on DLC risk in patients with T2DM., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

The translation of regenerative therapies to neuronal eye diseases requires a roadmap specific to the nature of the target diseases, patient population, methodologies for assessing outcome, and other factors. This commentary focuses on critical issues for translating regenerative eye therapies relevant to retinal neurons to human clinical trials., Competing Interests: Declaration of interests L.A.L. serves as consultant to Prilenia, Janssen, Roche, Neuroptika, Perfuse, Genentech, UNITY, Eyevensys, Annexon, and Santen., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: To identify the prominent factors that lead to misdiagnosis of retinopathy of prematurity (ROP) by ophthalmologists-in-training in the United States and Canada., Methods: This prospective cohort study included 32 ophthalmologists-in-training at six ophthalmology training programs in the United States and Canada. Twenty web-based cases of ROP using wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Responses were compared to a consensus reference standard diagnosis for accuracy, which was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. The types of diagnostic errors that occurred were analyzed with descriptive and chi-squared analysis. Main outcome measures were frequency of types (category, zone, stage, plus disease) of diagnostic errors; association of errors in zone, stage, and plus disease diagnosis with incorrectly identified category; and performance of ophthalmologists-in-training across postgraduate years., Results: Category of ROP was misdiagnosed at a rate of 48%. Errors in classification of plus disease were most commonly associated with misdiagnosis of treatment-requiring (plus error rate = 16% when treatment-requiring was correctly diagnosed vs 81% when underdiagnosed as type 2 or pre-plus; mean difference: 64.3; 95% CI: 51.9 to 76.7; P < .001) and type 2 or pre-plus (plus error rate = 35% when type 2 or pre-plus was correctly diagnosed vs 76% when overdiagnosed as treatment-requiring; mean difference: 41.0; 95% CI: 28.4 to 53.5; P < .001) disease. The diagnostic error rate of postgraduate year (PGY)-2 trainees was significantly higher than PGY-3 trainees (PGY-2 category error rate = 61% vs PGY-3 = 35%; mean difference, 25.4; 95% CI: 17.7 to 33.0; P < .001)., Conclusions: Ophthalmologists-in-training in the United States and Canada misdiagnosed ROP nearly half of the time, with incorrect identification of plus disease as a leading cause. Integration of structured learning for ROP in residency education may improve diagnostic competency. [ J Pediatr Ophthalmol Strabismus . 2023;60(5):337-343.] .

Purpose: To characterize common errors in the diagnosis of retinopathy of prematurity (ROP) among ophthalmologistsin-training in middle-income countries., Methods: In this prospective cohort study, 200 ophthalmologists-in-training from programs in Brazil, Mexico, and the Philippines participated. A secure web-based educational system was developed using a repository of more than 2,500 unique image sets of ROP, and a reference standard diagnosis was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. Twenty web-based cases of wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Trainees' responses were compared to the consensus reference standard diagnosis. Main outcome measures were frequency and types of diagnostic errors were analyzed., Results: The error rate in the diagnosis of any category of ROP was between 48% and 59% for all countries. The error rate in identifying type 2 or pre-plus disease was 77%, with a tendency for overdiagnosis (27% underdiagnosis vs 50% overdiagnosis; mean difference: 23.4; 95% CI: 12.1 to 34.7; P = .005). Misdiagnosis of treatment-requiring ROP as type 2 ROP was most commonly associated with incorrectly identifying plus disease (plus disease error rate = 18% with correct category diagnosis vs 69% when misdiagnosed; mean difference: 51.0; 95% CI: 49.3 to 52.7; P = .003)., Conclusions: Ophthalmologists-in-training from middle-income countries misdiagnosed ROP more than half of the time. Identification of plus disease was the salient factor leading to incorrect diagnosis. These findings emphasize the need for improved access to ROP education to improve competency in diagnosis among ophthalmologists-in-training in middle-income countries. [ J Pediatr Ophthalmol Strabismus . 2023;60(5):344-352.] .

Purpose: Epidemiological changes in retinopathy of prematurity (ROP) depend on neonatal care, neonatal mortality, and the ability to carefully titrate and monitor oxygen. We evaluate whether an artificial intelligence (AI) algorithm for assessing ROP severity in babies can be used to evaluate changes in disease epidemiology in babies from South India over a 5-year period., Design: Retrospective cohort study., Participants: Babies (3093) screened for ROP at neonatal care units (NCUs) across the Aravind Eye Care System (AECS) in South India., Methods: Images and clinical data were collected as part of routine tele-ROP screening at the AECS in India over 2 time periods: August 2015 to October 2017 and March 2019 to December 2020. All babies in the original cohort were matched 1:3 by birthweight (BW) and gestational age (GA) with babies in the later cohort. We compared the proportion of eyes with moderate (type 2) or treatment-requiring (TR) ROP, and an AI-derived ROP vascular severity score (from retinal fundus images) at the initial tele-retinal screening exam for all babies in a district, VSS), in the 2 time periods., Main Outcome Measures: Differences in the proportions of type 2 or worse and TR-ROP cases, and VSS between time periods., Results: Among BW and GA matched babies, the proportion [95% confidence interval {CI}] of babies with type 2 or worse and TR-ROP decreased from 60.9% [53.8%-67.7%] to 17.1% [14.0%-20.5%] (P < 0.001) and 16.8% [11.9%-22.7%] to 5.1% [3.4%-7.3%] (P < 0.001), over the 2 time periods. Similarly, the median [interquartile range] VSS in the population decreased from 2.9 [1.2] to 2.4 [1.8] (P < 0.001)., Conclusions: In South India, over a 5-year period, the proportion of babies developing moderate to severe ROP has dropped significantly for babies at similar demographic risk, strongly suggesting improvements in primary prevention of ROP. These results suggest that AI-based assessment of ROP severity may be a useful epidemiologic tool to evaluate temporal changes in ROP epidemiology., Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references., (Copyright © 2023 American Academy of Ophthalmology. All rights reserved.)

The molecular mechanisms contributing to the regulation of Th17-mediated inflammation remain underexplored. We here report a SUMO-specific protease (SENP)2-mediated pathway induced in pathogenic Th17 cells that restricts the pathogenesis of inflammatory colitis. SENP2 regulates the maturation of small ubiquitin-like modifiers (SUMO) and recycles SUMO from the substrate proteins. We find higher levels of SENP2 in pathogenic Th17 cells. By deleting Senp2 in T-cell lineages in mice, we demonstrate that the lack of Senp2 exacerbates the severity of experimental colitis, which is linked to elevated levels of GM-CSF + IL-17A + pathogenic Th17 cells and more severe dysbiosis of the intestinal microbiome. Adoptive transfer experiments demonstrate the cell-autonomous effect of Senp2 in restraining Th17 differentiation and colitis. The enzymatic activity of SENP2 is important for deSUMOylation of Smad4, which reduces Smad4 nuclear entry and Rorc expression. Our findings reveal a SENP2-mediated regulatory axis in the pathogenicity of Th17 cells., (© 2023. The Author(s).)

Importance: Retinopathy of prematurity (ROP) telemedicine screening programs have been found to be effective, but they rely on widefield digital fundus imaging (WDFI) cameras, which are expensive, making them less accessible in low- to middle-income countries. Cheaper, smartphone-based fundus imaging (SBFI) systems have been described, but these have a narrower field of view (FOV) and have not been tested in a real-world, operational telemedicine setting., Objective: To assess the efficacy of SBFI systems compared with WDFI when used by technicians for ROP screening with both artificial intelligence (AI) and human graders., Design, Setting, and Participants: This prospective cross-sectional comparison study took place as a single-center ROP teleophthalmology program in India from January 2021 to April 2022. Premature infants who met normal ROP screening criteria and enrolled in the teleophthalmology screening program were included. Those who had already been treated for ROP were excluded., Exposures: All participants had WDFI images and from 1 of 2 SBFI devices, the Make-In-India (MII) Retcam or Keeler Monocular Indirect Ophthalmoscope (MIO) devices. Two masked readers evaluated zone, stage, plus, and vascular severity scores (VSS, from 1-9) in all images. Smartphone images were then stratified by patient into training (70%), validation (10%), and test (20%) data sets and used to train a ResNet18 deep learning architecture for binary classification of normal vs preplus or plus disease, which was then used for patient-level predictions of referral warranted (RW)- and treatment requiring (TR)-ROP., Main Outcome and Measures: Sensitivity and specificity of detection of RW-ROP, and TR-ROP by both human graders and an AI system and area under the receiver operating characteristic curve (AUC) of grader-assigned VSS. Sensitivity and specificity were compared between the 2 SBFI systems using Pearson χ2testing., Results: A total of 156 infants (312 eyes; mean [SD] gestational age, 33.0 [3.0] weeks; 75 [48%] female) were included with paired examinations. Sensitivity and specificity were not found to be statistically different between the 2 SBFI systems. Human graders were effective with SBFI at detecting TR-ROP with a sensitivity of 100% and specificity of 83.49%. The AUCs with grader-assigned VSS only were 0.95 (95% CI, 0.91-0.99) and 0.96 (95% CI, 0.93-0.99) for RW-ROP and TR-ROP, respectively. For the AI system, the sensitivity of detecting TR-ROP sensitivity was 100% with specificity of 58.6%, and RW-ROP sensitivity was 80.0% with specificity of 59.3%., Conclusions and Relevance: In this cross-sectional study, 2 different SBFI systems used by technicians in an ROP screening program were highly sensitive for TR-ROP. SBFI systems with AI may be a cost-effective method to improve the global capacity for ROP screening.

Importance: Although race is a social construct, it is associated with variations in skin and retinal pigmentation. Image-based medical artificial intelligence (AI) algorithms that use images of these organs have the potential to learn features associated with self-reported race (SRR), which increases the risk of racially biased performance in diagnostic tasks; understanding whether this information can be removed, without affecting the performance of AI algorithms, is critical in reducing the risk of racial bias in medical AI., Objective: To evaluate whether converting color fundus photographs to retinal vessel maps (RVMs) of infants screened for retinopathy of prematurity (ROP) removes the risk for racial bias., Design, Setting, and Participants: The retinal fundus images (RFIs) of neonates with parent-reported Black or White race were collected for this study. A u-net, a convolutional neural network (CNN) that provides precise segmentation for biomedical images, was used to segment the major arteries and veins in RFIs into grayscale RVMs, which were subsequently thresholded, binarized, and/or skeletonized. CNNs were trained with patients' SRR labels on color RFIs, raw RVMs, and thresholded, binarized, or skeletonized RVMs. Study data were analyzed from July 1 to September 28, 2021., Main Outcomes and Measures: Area under the precision-recall curve (AUC-PR) and area under the receiver operating characteristic curve (AUROC) at both the image and eye level for classification of SRR., Results: A total of 4095 RFIs were collected from 245 neonates with parent-reported Black (94 [38.4%]; mean [SD] age, 27.2 [2.3] weeks; 55 majority sex [58.5%]) or White (151 [61.6%]; mean [SD] age, 27.6 [2.3] weeks, 80 majority sex [53.0%]) race. CNNs inferred SRR from RFIs nearly perfectly (image-level AUC-PR, 0.999; 95% CI, 0.999-1.000; infant-level AUC-PR, 1.000; 95% CI, 0.999-1.000). Raw RVMs were nearly as informative as color RFIs (image-level AUC-PR, 0.938; 95% CI, 0.926-0.950; infant-level AUC-PR, 0.995; 95% CI, 0.992-0.998). Ultimately, CNNs were able to learn whether RFIs or RVMs were from Black or White infants regardless of whether images contained color, vessel segmentation brightness differences were nullified, or vessel segmentation widths were uniform., Conclusions and Relevance: Results of this diagnostic study suggest that it can be very challenging to remove information relevant to SRR from fundus photographs. As a result, AI algorithms trained on fundus photographs have the potential for biased performance in practice, even if based on biomarkers rather than raw images. Regardless of the methodology used for training AI, evaluating performance in relevant subpopulations is critical.

Mutations in IDH1, IDH2 , and TET2 are recurrently observed in myeloid neoplasms. IDH1 and IDH2 encode isocitrate dehydrogenase isoforms, which normally catalyze the conversion of isocitrate to α-ketoglutarate (α-KG). Oncogenic IDH1/2 mutations confer neomorphic activity, leading to the production of D-2-hydroxyglutarate (D-2-HG), a potent inhibitor of α-KG-dependent enzymes which include the TET methylcytosine dioxygenases. Given their mutual exclusivity in myeloid neoplasms, IDH1 , IDH2 , and TET2 mutations may converge on a common oncogenic mechanism. Contrary to this expectation, we observed that they have distinct, and even opposite, effects on hematopoietic stem and progenitor cells in genetically engineered mice. Epigenetic and single-cell transcriptomic analyses revealed that Idh2 R172K and Tet2 loss-of-function have divergent consequences on the expression and activity of key hematopoietic and leukemogenic regulators. Notably, chromatin accessibility and transcriptional deregulation in Idh2 R172K cells were partially disconnected from DNA methylation alterations. These results highlight unanticipated divergent effects of IDH1/2 and TET2 mutations, providing support for the optimization of genotype-specific therapies.

Importance: One of the biggest challenges when using anti-vascular endothelial growth factor (VEGF) agents to treat retinopathy of prematurity (ROP) is the need to perform long-term follow-up examinations to identify eyes at risk of ROP reactivation requiring retreatment., Objective: To evaluate whether an artificial intelligence (AI)-based vascular severity score (VSS) can be used to analyze ROP regression and reactivation after anti-VEGF treatment and potentially identify eyes at risk of ROP reactivation requiring retreatment., Design, Setting, and Participants: This prognostic study was a secondary analysis of posterior pole fundus images collected during the multicenter, double-blind, investigator-initiated Comparing Alternative Ranibizumab Dosages for Safety and Efficacy in Retinopathy of Prematurity (CARE-ROP) randomized clinical trial, which compared 2 different doses of ranibizumab (0.12 mg vs 0.20 mg) for the treatment of ROP. The CARE-ROP trial screened and enrolled infants between September 5, 2014, and July 14, 2016. A total of 1046 wide-angle fundus images obtained from 19 infants at predefined study time points were analyzed. The analyses of VSS were performed between January 20, 2021, and November 18, 2022., Interventions: An AI-based algorithm assigned a VSS between 1 (normal) and 9 (most severe) to fundus images., Main Outcomes and Measures: Analysis of VSS in infants with ROP over time and VSS comparisons between the 2 treatment groups (0.12 mg vs 0.20 mg of ranibizumab) and between infants who did and did not receive retreatment for ROP reactivation., Results: Among 19 infants with ROP in the CARE-ROP randomized clinical trial, the median (range) postmenstrual age at first treatment was 36.4 (34.7-39.7) weeks; 10 infants (52.6%) were male, and 18 (94.7%) were White. The mean (SD) VSS was 6.7 (1.9) at baseline and significantly decreased to 2.7 (1.9) at week 1 (P < .001) and 2.9 (1.3) at week 4 (P < .001). The mean (SD) VSS of infants with ROP reactivation requiring retreatment was 6.5 (1.9) at the time of retreatment, which was significantly higher than the VSS at week 4 (P < .001). No significant difference was found in VSS between the 2 treatment groups, but the change in VSS between baseline and week 1 was higher for infants who later required retreatment (mean [SD], 7.8 [1.3] at baseline vs 1.7 [0.7] at week 1) vs infants who did not (mean [SD], 6.4 [1.9] at baseline vs 3.0 [2.0] at week 1). In eyes requiring retreatment, higher baseline VSS was correlated with earlier time of retreatment (Pearson r = -0.9997; P < .001)., Conclusions and Relevance: In this study, VSS decreased after ranibizumab treatment, consistent with clinical disease regression. In cases of ROP reactivation requiring retreatment, VSS increased again to values comparable with baseline values. In addition, a greater change in VSS during the first week after initial treatment was found to be associated with a higher risk of later ROP reactivation, and high baseline VSS was correlated with earlier retreatment. These findings may have implications for monitoring ROP regression and reactivation after anti-VEGF treatment.

Purpose: H1-antihistamines (AHs) may exert protective effects against cancer. We investigated the association of AH use with hepatocellular carcinoma (HCC) risk in type 2 diabetes mellitus (T2DM) patients without hepatitis B virus (HBV) or hepatitis C virus (HCV) infection., Methods: The data of patients with T2DM enrolled from Taiwan's National Health Insurance Research Database were examined for the period of January 1, 2008, to December 31, 2018. We used the Kaplan-Meier method and Cox proportional hazards regression to evaluate the AH use-HCC risk association., Results: After 1:1 propensity score matching was performed, the two cohorts were each divided into AH users (n = 47,990) and nonusers (n = 47,990). The risk of HCC was significantly lower in AH users than in AH nonusers (adjusted hazard ratio [aHR]: 0.55 95% confidence interval [95% CI], 0.46 to 0.67; IRR: 0.70; 95% CI, 0.60 to 0.84), respectively. The dose-response relationship between AH use and HCC risk was also observed (aHRs: 0.58, 0.56, 0.50, and 0.41 for 28-35, 36-49, 50-77, and >77 cumulative defined daily doses of AH, respectively)., Conclusion: AH use can reduce HCC risk in T2DM patients without HBV or HCV infection in a dose-dependent manner., Competing Interests: Declaration of Competing Interest The authors have no potential conflicts of interest to declare. The datasets supporting the study conclusions are included within the manuscript., (Copyright © 2022 Elsevier Masson SAS. All rights reserved.)

Competing Interests: J.P.C. Financial support from Genentech; Founder, Siloam Vision, LLC; R.V.P.C. Consultant, Alcon; Financial support, Genentech, Regeneron; Founder, Siloam Vision, LLC. The remaining authors declare that they have no conflicts of interest to disclose.

Purpose: To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images., Design: Cohort study., Participants: Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders., Methods: Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise., Main Outcome Measures: Grading severity of ROP as defined by plus, preplus, or no ROP., Results: Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen's kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease., Conclusions: Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment., (Copyright © 2022. Published by Elsevier Inc.)

Purpose of Review: This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goals defined in the NEI Strategic Plan as well as opportunities for future activities and breakthroughs in ophthalmology., Recent Findings: Ophthalmology is at the forefront of artificial intelligence-based innovations in biomedical research that may lead to improvement in early detection and surveillance of ocular disease, prediction of progression, and improved quality of life. Technological advances have ushered in an era where unprecedented amounts of information can be linked that enable scientific discovery. However, there remains an unmet need to collect, harmonize, and share data in a machine actionable manner. Similarly, there is a need to ensure that efforts promote health and research equity by expanding diversity in the data and workforce., Summary: The NIH/NEI has supported the development artificial intelligence-based innovations to advance biomedical research. The NIH/NEI has defined activities to achieve these goals in the NIH Strategic Plan for Data Science and the NEI Strategic Plan and have spearheaded initiatives to facilitate research in these areas., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: To describe the methods involved in processing and characteristics of an open dataset of annotated clinical notes from the electronic health record (EHR) annotated for glaucoma medications., Methods: In this study, 480 clinical notes from office visits, medical record numbers (MRNs), visit identification numbers, provider names, and billing codes were extracted for 480 patients seen for glaucoma by a comprehensive or glaucoma ophthalmologist from January 1, 2019, to August 31, 2020. MRNs and all visit data were de-identified using a hash function with salt from the deidentifyr package. All progress notes were annotated for glaucoma medication name, route, frequency, dosage, and drug use using an open-source annotation tool, Doccano. Annotations were saved separately. All protected health information (PHI) in progress notes and annotated files were de-identified using the published de-identifying algorithm Philter. All progress notes and annotations were manually validated by two ophthalmologists to ensure complete de-identification., Results: The final dataset contained 5520 annotated sentences, including those with and without medications, for 480 clinical notes. Manual validation revealed 10 instances of remaining PHI which were manually corrected., Conclusions: Annotated free-text clinical notes can be de-identified for upload as an open dataset. As data availability increases with the adoption of EHRs, free-text open datasets will become increasingly valuable for "big data" research and artificial intelligence development. This dataset is published online and publicly available at https://github.com/jche253/Glaucoma&#95;Med&#95;Dataset., Translational Relevance: This open access medication dataset may be a source of raw data for future research involving big data and artificial intelligence research using free-text.

Importance: Accurate diagnosis of retinopathy of prematurity (ROP) is essential to provide timely treatment and reduce the risk of blindness. However, the components of an ROP examination are subjective and qualitative., Objective: To evaluate whether optical coherence tomography (OCT)-derived retinal thickness measurements at the vascular-avascular junction are associated with clinical diagnosis of ROP stage., Design, Setting, and Participants: This cross-sectional longitudinal study compared OCT-based ridge thickness calculated from OCT B-scans by a masked examiner to the clinical diagnosis of 2 masked examiners using both traditional stage classifications and a more granular continuous scale at the neonatal intensive care unit (NICU) of Oregon Health & Science University (OHSU) Hospital. Infants who met ROP screening criteria in the OHSU NICU between June 2021 and April 2022 and had guardian consent were included. One OCT volume and en face image per patient per eye showing at least 1 to 2 clock hours of ridge were included in the final analysis., Main Outcomes and Measures: Comparison of OCT-derived ridge thickness to the clinical diagnosis of ROP stage using an ordinal and continuous scale. Repeatability was assessed using 20 repeated examinations from the same visit and compared using intraclass correlation coefficient (ICC) and coefficient of variation (CV). Comparison of ridge thickness with ordinal categories was performed using generalized estimating equations and with continuous stage using Spearman correlation., Results: A total of 128 separate OCT eye examinations from 50 eyes of 25 patients were analyzed. The ICC was 0.87 with a CV of 7.0%. Higher ordinal disease classification was associated with higher axial ridge thickness on OCT, with mean (SD) thickness measurements of 264.2 (11.2) μm (P < .001), 334.2 (11.4) μm (P < .001), and 495.0 (32.2) μm (P < .001) for stages 1, 2, and 3, respectively and with continuous stage labels (ρ = 0.739, P < .001)., Conclusions and Relevance: These results suggest that OCT-based quantification of peripheral stage in ROP may be an objective and quantitative biomarker that may be useful for clinical diagnosis and longitudinal monitoring and may have implications for disease classification in the future.

Importance: Retinopathy of prematurity (ROP) is a leading cause of preventable blindness that disproportionately affects children born in low- and middle-income countries (LMICs). In-person and telemedical screening examinations can reduce this risk but are challenging to implement in LMICs owing to the multitude of at-risk infants and lack of trained ophthalmologists., Objective: To implement an ROP risk model using retinal images from a single baseline examination to identify infants who will develop treatment-requiring (TR)-ROP in LMIC telemedicine programs., Design, Setting, and Participants: In this diagnostic study conducted from February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an Indian ROP telemedicine screening program. An artificial intelligence (AI)-derived vascular severity score (VSS) was obtained from images from the first examination after 30 weeks' postmenstrual age. Using 5-fold cross-validation, logistic regression models were trained on 2 variables (gestational age and VSS) for prediction of TR-ROP. The model was externally validated on test data sets from India, Nepal, and Mongolia. Data were analyzed from October 20, 2021, to April 20, 2022., Main Outcomes and Measures: Primary outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value for predictions of future occurrences of TR-ROP; the number of weeks before clinical diagnosis when a prediction was made; and the potential reduction in number of examinations required., Results: A total of 3760 infants (median [IQR] postmenstrual age, 37 [5] weeks; 1950 male infants [51.9%]) were included in the study. The diagnostic model had a sensitivity and specificity, respectively, for each of the data sets as follows: India, 100.0% (95% CI, 87.2%-100.0%) and 63.3% (95% CI, 59.7%-66.8%); Nepal, 100.0% (95% CI, 54.1%-100.0%) and 77.8% (95% CI, 72.9%-82.2%); and Mongolia, 100.0% (95% CI, 93.3%-100.0%) and 45.8% (95% CI, 39.7%-52.1%). With the AI model, infants with TR-ROP were identified a median (IQR) of 2.0 (0-11) weeks before TR-ROP diagnosis in India, 0.5 (0-2.0) weeks before TR-ROP diagnosis in Nepal, and 0 (0-5.0) weeks before TR-ROP diagnosis in Mongolia. If low-risk infants were never screened again, the population could be effectively screened with 45.0% (India, 664/1476), 38.4% (Nepal, 151/393), and 51.3% (Mongolia, 266/519) fewer examinations required., Conclusions and Relevance: Results of this diagnostic study suggest that there were 2 advantages to implementation of this risk model: (1) the number of examinations for low-risk infants could be reduced without missing cases of TR-ROP, and (2) high-risk infants could be identified and closely monitored before development of TR-ROP.

Objective: To compare the performance of deep learning classifiers for the diagnosis of plus disease in retinopathy of prematurity (ROP) trained using 2 methods for developing models on multi-institutional data sets: centralizing data versus federated learning (FL) in which no data leave each institution., Design: Evaluation of a diagnostic test or technology., Subjects: Deep learning models were trained, validated, and tested on 5255 wide-angle retinal images in the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP study. All images were labeled for the presence of plus, preplus, or no plus disease with a clinical label and a reference standard diagnosis (RSD) determined by 3 image-based ROP graders and the clinical diagnosis., Methods: We compared the area under the receiver operating characteristic curve (AUROC) for models developed on multi-institutional data, using a central approach initially, followed by FL, and compared locally trained models with both approaches. We compared the model performance (κ) with the label agreement (between clinical and RSD), data set size, and number of plus disease cases in each training cohort using the Spearman correlation coefficient (CC)., Main Outcome Measures: Model performance using AUROC and linearly weighted κ., Results: Four settings of experiment were used: FL trained on RSD against central trained on RSD, FL trained on clinical labels against central trained on clinical labels, FL trained on RSD against central trained on clinical labels, and FL trained on clinical labels against central trained on RSD (P = 0.046, P = 0.126, P = 0.224, and P = 0.0173, respectively). Four of the 7 (57%) models trained on local institutional data performed inferiorly to the FL models. The model performance for local models was positively correlated with the label agreement (between clinical and RSD labels, CC = 0.389, P = 0.387), total number of plus cases (CC = 0.759, P = 0.047), and overall training set size (CC = 0.924, P = 0.002)., Conclusions: We found that a trained FL model performs comparably to a centralized model, confirming that FL may provide an effective, more feasible solution for interinstitutional learning. Smaller institutions benefit more from collaboration than larger institutions, showing the potential of FL for addressing disparities in resource access., (Copyright © 2022 American Academy of Ophthalmology. All rights reserved.)

Objective: To utilize a deep learning (DL) model trained via federated learning (FL), a method of collaborative training without sharing patient data, to delineate institutional differences in clinician diagnostic paradigms and disease epidemiology in retinopathy of prematurity (ROP)., Design: Evaluation of a diagnostic test or technology., Subjects and Controls: We included 5245 patients with wide-angle retinal imaging from the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP study. Images were labeled with the clinical diagnoses of plus disease (plus, preplus, no plus), which were documented in the chart, and a reference standard diagnosis was determined by 3 image-based ROP graders and the clinical diagnosis., Methods: Demographics (birth weight, gestational age) and clinical diagnoses for all eye examinations were recorded from each institution. Using an FL approach, a DL model for plus disease classification was trained using only the clinical labels. The 3 class probabilities were then converted into a vascular severity score (VSS) for each eye examination, as well as an "institutional VSS," in which the average of the VSS values assigned to patients' higher severity ("worse") eyes at each examination was calculated for each institution., Main Outcome Measures: We compared demographics, clinical diagnoses of plus disease, and institutional VSSs between institutions using the McNemar-Bowker test, 2-proportion Z test, and 1-way analysis of variance with post hoc analysis by the Tukey-Kramer test. Single regression analysis was performed to explore the relationship between demographics and VSSs., Results: We found that the proportion of patients diagnosed with preplus disease varied significantly between institutions (P < 0.001). Using the DL-derived VSS trained on the data from all institutions using FL, we observed differences in the institutional VSS and the level of vascular severity diagnosed as no plus (P < 0.001) across institutions. A significant, inverse relationship between the institutional VSS and mean gestational age was found (P = 0.049, adjusted R 2  = 0.49)., Conclusions: A DL-derived ROP VSS developed without sharing data between institutions using FL identified differences in the clinical diagnoses of plus disease and overall levels of ROP severity between institutions. Federated learning may represent a method to standardize clinical diagnoses and provide objective measurements of disease for image-based diseases., (Copyright © 2022 American Academy of Ophthalmology. All rights reserved.)

Tumor suppressor WWOX inhibits cancer growth and retards Alzheimer's disease (AD) progression. Supporting evidence shows that the more strongly WWOX binds intracellular protein partners, the weaker is cancer cell growth in vivo. Whether this correlates with retardation of AD progression is unknown. Two functional forms of WWOX exhibit opposite functions. pY33-WWOX is proapoptotic and anticancer, and is essential for maintaining normal physiology. In contrast, pS14-WWOX is accumulated in the lesions of cancers and AD brains, and suppression of WWOX phosphorylation at S14 by a short peptide Zfra abolishes cancer growth and retardation of AD progression. In parallel, synthetic Zfra4-10 or WWOX7-21 peptide strengthens the binding of endogenous WWOX with intracellular protein partners leading to cancer suppression. Indeed, Zfra4-10 is potent in restoring memory loss in triple transgenic mice for AD (3xTg) by blocking the aggregation of amyloid beta 42 (Aβ42), enhancing degradation of aggregated proteins, and inhibiting activation of inflammatory NF-κB. In light of the findings, Zfra4-10-mediated suppression of cancer and AD is due, in part, to an enhanced binding of endogenous WWOX and its binding partners. In this perspective review article, we detail the molecular action of WWOX in the HYAL-2/WWOX/SMAD4 signaling for biological effects, and discuss WWOX phosphorylation forms in interacting with binding partners, leading to suppression of cancer growth and retardation of AD progression.

Optical coherence tomography (OCT) has changed the standard of care for diagnosis and management of macular diseases in adults. Current commercially available OCT systems, including handheld OCT for pediatric use, have a relatively narrow field of view (FOV), which has limited the potential application of OCT to retinal diseases with primarily peripheral pathology, including many of the most common pediatric retinal conditions. More broadly, diagnosis of all types of retinal detachment (exudative, tractional, and rhegmatogenous) may be improved with OCT-based assessment of retinal breaks, identification of proliferative vitreoretinopathy (PVR) membranes, and the pattern of subretinal fluid. Intraocular tumors both benign and malignant often occur outside of the central macula and may be associated with exudation, subretinal and intraretinal fluid, and vitreoretinal traction. The development of wider field OCT systems thus has the potential to improve the diagnosis and management of myriad diseases in both adult and pediatric retina. In this paper, we present a case series of pediatric patients with complex vitreoretinal pathology undergoing examinations under anesthesia (EUA) using a portable widefield (WF) swept-source (SS)-OCT device., Competing Interests: Oregon Health and Science University (OHSU), DH, and YaJ have significant financial interests in Optovue, a company that may have a commercial interest in the results of this research and technology. These potential conflicts of interest have been reviewed and managed by OHSU. DH and YaJ have received royalties for patent files through OHSU, as well as loaned equipment for research from Optovue. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nguyen, Ni, Liang, Khan, Wei, Skalet, Ostmo, Chiang, Jia, Huang, Jian and Campbell.)

Purpose: To validate a vascular severity score as an appropriate output for artificial intelligence (AI) Software as a Medical Device (SaMD) for retinopathy of prematurity (ROP) through comparison with ordinal disease severity labels for stage and plus disease assigned by the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), committee., Design: Validation study of an AI-based ROP vascular severity score., Participants: A total of 34 ROP experts from the ICROP3 committee., Methods: Two separate datasets of 30 fundus photographs each for stage (0-5) and plus disease (plus, preplus, neither) were labeled by members of the ICROP3 committee using an open-source platform. Averaging these results produced a continuous label for plus (1-9) and stage (1-3) for each image. Experts were also asked to compare each image to each other in terms of relative severity for plus disease. Each image was also labeled with a vascular severity score from the Imaging and Informatics in ROP deep learning system, which was compared with each grader's diagnostic labels for correlation, as well as the ophthalmoscopic diagnosis of stage., Main Outcome Measures: Weighted kappa and Pearson correlation coefficients (CCs) were calculated between each pair of grader classification labels for stage and plus disease. The Elo algorithm was also used to convert pairwise comparisons for each expert into an ordered set of images from least to most severe., Results: The mean weighted kappa and CC for all interobserver pairs for plus disease image comparison were 0.67 and 0.88, respectively. The vascular severity score was found to be highly correlated with both the average plus disease classification (CC = 0.90, P < 0.001) and the ophthalmoscopic diagnosis of stage (P < 0.001 by analysis of variance) among all experts., Conclusions: The ROP vascular severity score correlates well with the International Classification of Retinopathy of Prematurity committee member's labels for plus disease and stage, which had significant intergrader variability. Generation of a consensus for a validated scoring system for ROP SaMD can facilitate global innovation and regulatory authorization of these technologies., (Copyright © 2022. Published by Elsevier Inc.)

Physicians can reduce their documentation time by working with a scribe. However, what scribes document and how their actions affect existing documentation workflows is unclear. This study leverages electronic health record (EHR) audit logs to observe how scribes affected the documentation workflows of seven physicians and their staff across 13,000 outpatient ophthalmology visits. In addition to editing progress notes, scribes routinely edited exam findings and diagnoses. Scribes with clinical training also edited items such as vital signs that a scribe without clinical training did not. Every physician edited patient records later in the day when working with a scribe and those who deferred their editing the most had some of the largest reductions in EHR time. These results suggest that what scribes document, how physicians work with scribes, and scribe impact on documentation time are all highly variable, highlighting the need for evidence-based best practices.

Causes of blindness differ across the globe; in higher-income countries, most blindness results from the degeneration of specific classes of cells in the retina, including retinal pigment epithelium (RPE), photoreceptors, and retinal ganglion cells. Advances over the past decade in retinal regenerative medicine have allowed each of these cell types to be produced ex vivo from progenitor stem cells. Here, we review progress in applying these technologies to cell replacement - with the goal of vision restoration in degenerative disease. We discuss the landscape of human clinical trials for RPE transplantation and advanced preclinical studies for other cell types. We also review progress toward in situ repair of retinal degeneration using endogenous progenitor cells. Finally, we provide a high-level overview of progress toward prosthetic ocular vision restoration, including advanced photovoltaic devices, opsin-based gene therapy, and small-molecule photoswitches. Progress in each of these domains is at or near the human clinical-trial stage, bringing the audacious goal of vision restoration within sight., (© 2022. Springer Nature America, Inc.)

Abstract: Accessibility to the Internet and computer systems has prompted the gravitation towards digital learning in medicine, including ophthalmology. Using the PubMed database and Google search engine, current initiatives in ophthalmology that serve as alternatives to traditional in-person learning with the purpose of enhancing clinical and surgical training were reviewed. This includes the development of teleeducation modules, construction of libraries of clinical and surgical videos, conduction of didactics via video communication, and the implementation of simulators and intelligent tutoring systems into clinical and surgical training programs. In this age of digital communication, teleophthalmology programs, virtual ophthalmological society meetings, and online examinations have become necessary for conducting clinical work and educational training in ophthalmology, especially in light of recent global events that have prevented large gatherings as well as the rural location of various populations. Looking forward, web-based modules and resources, artificial intelligence-based systems, and telemedicine programs will augment current curricula for ophthalmology trainees., (Copyright © 2021 Asia-Pacific Academy of Ophthalmology. Published by Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology.)

The incidence of retinopathy of prematurity (ROP) continues to rise due to the improved survival of very low birth weight infants in developed countries. This epidemic is also fueled by increased survival of preterm babies with variable use of oxygen and a lack of ROP awareness and screening services in resource-limited regions. Improvements in technology and a basic understanding of the disease pathophysiology have changed the way we screen and manage ROP, educate providers and patients, and improve ROP awareness. Advancements in imaging techniques, expansion of telemedicine services, and the potential for artificial intelligence-assisted ROP screening programs have created opportunities to improve ROP care in areas with a shortage of ophthalmologists trained in ROP. To address the gap in provider knowledge regarding ROP, the Global Education Network for Retinopathy of Prematurity (GEN-ROP) created a web-based tele-education training module that can be used to educate all providers involved in ROP, including non-physician ROP screeners. Over the past 50 years, the treatment of severe ROP has evolved from limited treatment modalities to cryotherapy and laser photocoagulation. More recently, there has been growing evidence to support the use of anti-vascular endothelial growth factor (VEGF) agents for the treatment of severe ROP. However, VEGF is known to be important in organogenesis and microvascular maintenance, and given that intravitreal anti-VEGF treatment can result in systemic VEGF suppression over a period of at least 1-12 weeks, there are concerns regarding adverse effects and long-term ocular and systemic developmental consequences of anti-VEGF therapy. Future research in ophthalmology to address the growing burden of ROP should focus on cost-effective fundus imaging devices, implementation of artificial intelligence platforms, updated treatment algorithms with optimal use of anti-VEGF and careful investigation of its long-term effects, and surgical options in advanced ROP. Addressing these unmet needs will aid the global effort against the ROP epidemic and optimize our understanding and treatment of this blinding disease., (Copyright © 2021. Published by Elsevier Ltd.)