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Objectives: Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera., Design, Setting: A retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used., Participants: The algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 images (33% referable AMD) captured on Asian eyes using the target device. The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). Following the first training step, the test set consisted of 909 images (49% referable AMD). For the fine-tuning step, the test set consisted of 238 (34% referable AMD) images. The reference standard for the AREDS data set was fundus image grading by the central reading centre, and for the target device, it was consensus image grading by specialists., Outcome Measures: Area under receiver operating curve (AUC), sensitivity and specificity of algorithm., Results: Before fine-tuning, the deep learning (DL) algorithm exhibited a test set (from AREDS) sensitivity of 93.48% (95% CI: 90.8% to 95.6%), specificity of 82.33% (95% CI: 78.6% to 85.7%) and AUC of 0.965 (95% CI:0.95 to 0.98). After fine-tuning, the DL algorithm displayed a test set (from the target device) sensitivity of 91.25% (95% CI: 82.8% to 96.4%), specificity of 84.18% (95% CI: 77.5% to 89.5%) and AUC 0.947 (95% CI: 0.911 to 0.982)., Conclusion: The DL algorithm shows promising results in detecting referable AMD from a portable smartphone-based imaging system. This approach can potentially bring effective and affordable AMD screening to underserved areas., Competing Interests: Competing interests: FMS, DPR, TJK, BO and ASi are employees of Remidio Innovative Solutions. Remidio Innovative Solutions, Inc, USA and Medios Technologies are wholly owned subsidiaries of Remidio Innovative Solutions Pvt Ltd, India. FMS has patents and financial interests in Remidio Innovative Solutions Pvt Ltd (ESOP and Stock). ASi has patents, stock and leadership role. ASh is a consultant for Novartis, Allergan, Bayer and Intas, unrelated to this work., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: Keratitis is the primary cause of corneal blindness worldwide. Prompt identification and referral of patients with keratitis are fundamental measures to improve patient prognosis. Although deep learning can assist ophthalmologists in automatically detecting keratitis through a slit lamp camera, remote and underserved areas often lack this professional equipment. Smartphones, a widely available device, have recently been found to have potential in keratitis screening. However, given the limited data available from smartphones, employing traditional deep learning algorithms to construct a robust intelligent system presents a significant challenge. This study aimed to propose a meta-learning framework, cosine nearest centroid-based metric learning (CNCML), for developing a smartphone-based keratitis screening model in the case of insufficient smartphone data by leveraging the prior knowledge acquired from slit-lamp photographs., Methods: We developed and assessed CNCML based on 13,009 slit-lamp photographs and 4,075 smartphone photographs that were obtained from 3 independent clinical centers. To mimic real-world scenarios with various degrees of sample scarcity, we used training sets of different sizes (0 to 20 photographs per class) from the HUAWEI smartphone to train CNCML. We evaluated the performance of CNCML not only on an internal test dataset but also on two external datasets that were collected by two different brands of smartphones (VIVO and XIAOMI) in another clinical center. Furthermore, we compared the performance of CNCML with that of traditional deep learning models on these smartphone datasets. The accuracy and macro-average area under the curve (macro-AUC) were utilized to evaluate the performance of models., Results: With merely 15 smartphone photographs per class used for training, CNCML reached accuracies of 84.59%, 83.15%, and 89.99% on three smartphone datasets, with corresponding macro-AUCs of 0.96, 0.95, and 0.98, respectively. The accuracies of CNCML on these datasets were 0.56% to 9.65% higher than those of the most competitive traditional deep learning models., Conclusions: CNCML exhibited fast learning capabilities, attaining remarkable performance with a small number of training samples. This approach presents a potential solution for transitioning intelligent keratitis detection from professional devices (e.g., slit-lamp cameras) to more ubiquitous devices (e.g., smartphones), making keratitis screening more convenient and effective., Competing Interests: Declaration of competing interest The authors 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 Elsevier Inc. All rights reserved.)

Objective: This study evaluated the diagnostic performances of a tablet-based hearing screening test by assisted-test and self-test modes., Design/method: Measurements were performed with the SoTone tests in normal hearing and hearing-impaired adult participants using an Android tablet and calibrated Bluetooth headphones. The duration of assisted- and self-test modes were compared. Receiver operating characteristic (ROC) curves were plotted after calculations of sensitivity and specificity at 20, 30, and 35 dB HL cut-off values., Study Sample: 217 participants performed the tests. The effect of test mode (assisted versus self) was compared in a sample of 103 participants., Results: Self-test duration (89 s) was significantly longer than the assisted-test duration (75 s) ( p  = 0.003, Wilcoxon test). For the 20, 30, and 35 dB HL cut-off values, sensitivity was between 92% and 96%, and specificity was between 79 and 90%. Concordance of results between assisted-test and self-test modes was excellent (Cohen's kappa = 0.81, p  < 0.001)., Conclusions: The SoTone hearing screening test is accurate for identifying the presence of a suspected hearing loss at 20 dB HL or more in adults. It can be used either in assisted-test or self-test modes.

A number of countries now recommend population-wide depression screening for perinatal women, using validated tools. A stepped-approach to screening - involving universal screening with a brief measure, followed by targeted screening using a longer measure for those women identified as at greater risk - is used in some settings. This brief report describes the test performance characteristics of a 3-item mood screening instrument, developed for use within a digital parenting program. Participants (n = 404) in this cross-sectional study were mothers of children aged up to 3 years. The majority (65.5 %) were first-time mothers, and their mean age was 32.8 years. Data were collected using an online survey. The test performance of the brief 3-item mood screening instrument (possible score range = 0-300) was examined using Receiver Operating Characteristic (ROC) analysis, with a score of 13 or more on the Edinburgh Postnatal Depression Scale (EPDS) used as the reference standard. The mood screening instrument demonstrated excellent range when compared to the reference standard. Optimal balance between sensitivity (0.77) and specificity (0.78), was achieved at a cut-point of 160 or less. Analysis was limited by using only the EPDS as the reference standard. This preliminary data supports the use of this 3-item mood screening instrument to screen for postnatal depression symptoms and may be integrated into a mobile Health or online tool. Future research should examine the test performance of the 3-item mood screening instrument against a diagnostic tool., Competing Interests: Declaration of competing interest This research was funded by a grant to Sleepfit and Tresillian from private health insurer HCF (Hospitals Combined Fund) for a partnership to develop and validate a digital parenting program. Jessica Appleton was a Tresillian employee at the time of conducting this research. All other authors declare that they have no conflicts of interest. Analysis were performed by Nicole Reilly and Jessica Appleton. The first draft of the manuscript was written by Jessica Appleton and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly., Method: The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis., Results: The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects., Conclusion: Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration., Competing Interests: The authors declare that they have no competing interests in this article., (Copyright: © 2024 Adhibai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Diabetic neuropathy is frequently underdiagnosed and undertreated. Logistic problems accompany the routine use of the biothesiometer. Hence, we attempted to find a more easily available alternative., Research Design and Methods: 149 patients with diabetes visiting the outpatient endocrinology clinic were assessed for vibration sense using a 128-Hz tuning fork (absolute timing method) and a biothesiometer. A reading of >25 V on the biothesiometer (known as vibration perception threshold or VPT) was taken as the diagnostic criterion for severe neuropathy while >15 V was used as an indicator of the mild form. The sensitivity and specificity were calculated by constructing the receiver operating characteristic curve (ROC). A p value of <0.05 was considered as statistically significant., Results: The timed tuning fork (TTF) test showed a statistically significant correlation with the VPT measurements (r=-0.5, p=0.000). Using the VPT findings as a reference, a timed tuning fork cut-off of 4.8 s was 76% sensitive and 77% specific in diagnosing mild neuropathy while absent tuning fork sensation demonstrated 70% sensitivity and 90% specificity in detecting severe neuropathy., Conclusions: The tuning fork test demonstrated significant sensitivity and specificity in diagnosing diabetic peripheral neuropathy when compared against the biothesiometer. A cut-off of 4.8 s can be a useful indicator of the early stages of onset of the condition., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Dutch hospitals are required to screen older patients for the risk of developing functional decline using the Safety Management System (VMS) which assesses four domains associated with functional decline; fall risk, risk of delirium, malnutrition, and physical impairment., Purpose: The aim is twofold, first to compare the VMS frailty instrument as a frailty screener with existing frailty instruments and second to provide an overview of the available evidence., Methods: We performed a literature search to identify studies that used the VMS instrument as frailty screener to asses frailty or to predict adverse health outcomes in older hospitalized patients. Pubmed, Cinahl, and Embase were searched from January 1 st 2008 to December 11th 2023., Results: Our search yielded 603 articles, of which 17 studies with heterogenous populations and settings were included. Using the VMS, frailty was scored in six different ways. The agreement between VMS and other frailty instruments ranged from 57 to 87%. The highest sensitivity and specificity of VMS for frailty were 90% and 67%, respectively. The association of the VMS with outcomes was studied in 14 studies, VMS was predictive for complications, delirium, falls, length of stay, and adverse events. Conflicting results were found for hospital (re)admission, complications, change in living situation, functional decline, and mortality., Conclusion: The VMS frailty instrument were studied as a frailty screening instrument in various populations and settings. The value of the VMS instrument as a frailty screener looks promising. Our results suggest that the scoring method of the VMS could be adapted to specific requirements of settings or populations., (© 2024. The Author(s).)

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.

Background: Atrial fibrillation (AF) increases the risk of death, stroke, heart failure, cognitive decline, and healthcare costs but is often asymptomatic and undiagnosed. There is currently no national screening program for AF. The advent of validated hand-held devices allows AF to be detected in non-healthcare settings, enabling screening to be undertaken within the community., Method and Results: In this novel observational study, we embedded a MyDiagnostick single lead ECG sensor into the handles of shopping trolleys in four supermarkets in the Northwest of England: 2155 participants were recruited. Of these, 231 participants either activated the sensor or had an irregular pulse, suggesting AF. Some participants agreed to use the sensor but refused to provide their contact details, or consent to pulse assessment. In addition, some data were missing, resulting in 203 participants being included in the final analyses. Fifty-nine participants (mean age 73.6 years, 43% female) were confirmed or suspected of having AF; 20 were known to have AF and 39 were previously undiagnosed. There was no evidence of AF in 115 participants and the remaining 46 recordings were non-diagnostic, mainly due to artefact. Men and older participants were significantly more likely to have newly diagnosed AF. Due to the number of non-diagnostic ECGs (n = 46), we completed three levels of analyses, excluding all non-diagnostic ECGs, assuming all non-diagnostic ECGs were masking AF, and assuming all non-diagnostic ECGs were not AF. Based on the results of the three analyses, the sensor's sensitivity (95% CI) ranged from 0.70 to 0.93; specificity from 0.15 to 0.97; positive predictive values (PPV) and negative predictive values (NPV) ranged from 0.24 to 0.56 and 0.55 to 1.00, respectively. These values should be interpreted with caution, as the ideal reference standard on 1934 participants was imperfect., Conclusion: The study demonstrates that the public will engage with AF screening undertaken as part of their daily routines using hand-held devices. Sensors can play a key role in identifying asymptomatic patients in this way, but the technology must be further developed to reduce the quantity of non-diagnostic ECGs., Competing Interests: Disclosures The study is independently led by the academic team and is supported by educational grant funding from Bristol Myers Squibb. Deirdre Lane received investigator-initiated educational grants from Bristol-Myers Squibb (BMS) and Pfizer, has been a speaker for Bayer, Boehringer Ingelheim, and BMS/Pfizer and has consulted for BMS and Boehringer Ingelheim; all outside the submitted work. Peter Penson owns four shares in AstraZeneca PLC and has received honoraria and/or travel reimbursement for events sponsored by AKCEA, Amgen, AMRYT, Link Medical, Mylan, Napp, Sanofi. Lis Neubeck has received an investigator-initiated grant from Daiichi Sankyo, payment from Bristol Myers Squib for participation in a Global Medical Advisory Board and is the Former President of the Association for Cardiovascular Nursing and Allied Professionals of the ESC. Gregory Lip has been Consultant and speaker for BMS/Pfizer, Boehringer Ingelheim, Daiichi-Sankyo, Anthos. No fees are received personally. He is co-principal investigator of the AFFIRMO project on multimorbidity in AF, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 899871. No further conflicts of interest declared., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Electroretinography (ERG) provides crucial insights into retinal function and the integrity of the visual pathways. However, ERG assessments classically require a complicated technical background with costly equipment. In addition, the placement of corneal or conjunctival electrodes is not always tolerated by the patients, which restricts the measurement for pediatric evaluations. In this short review, we give an overview of the use of the RETeval portable ERG device (LKC Technologies, Inc., Gaithersburg, MD, USA), a modern portable ERG device that can facilitate screening for diseases involving the retina and the optic nerve. We also review its potential to provide ocular biomarkers in systemic pathologies, such as Alzheimer's disease and central nervous system alterations, within the framework of oculomics., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: Up to 20% of youth have impairing mental health problems as early as age 3. Early identification and intervention of mental health risks in pediatric primary care could mitigate this crisis via prevention prior to disease onset. The purpose of this study was to establish the feasibility and acceptability of implementing a brief transdiagnostic screening instrument in pediatric primary care for irritability and corollary impairment., Method: Five pediatric clinicians in a Midwest clinic implemented the Multidimensional Assessment Profiles-Early Assessment Screener of Irritability (MAPS-EASI) for toddlers (24-30 months) and their families. MAPS-EASI (psychometrically derived from the well-validated MAPS-Scales) includes six items (scored 0-5) about symptoms (e.g., tantrums, grumpy mood), context, and frequency and two items (scored 0-3) assessed impairment. Positive screens (MAPS-EASI ≥ 5 plus impairment ≥ 2) were referred to an evidence-based parenting intervention. We assessed reach and outcomes of MAPS-EASI screening. Follow-up interviews with clinicians assessed perspectives on irritability screening and MAPS-EASI implementation., Results: Of 201 eligible families, 100 (49.8%) completed the screener for a 24- or 30-month well-child visit. Mean MAPS-EASI scores were 5.8 (SD = 3.2), mean impairment scores were 0.9 (SD = 0.9), and 24 (24.0%) screened positive. Clinicians indicated that irritability screening for toddlers was aligned with their prevention-oriented, developmentally based practice. MAPS-EASI had face validity and increased clinician decision-making confidence. Finally, clinicians identified barriers and facilitators to large-scale implementation., Conclusions: MAPS-EASI proved to be feasible and acceptable in pediatric primary care. Further tailoring will be needed as the MAPS-EASI processes are scaled out to new contexts and populations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

With the growing significance of artificial intelligence in healthcare, new perspectives are emerging in primary care. Diabetic retinopathy, a microvascular complication of diabetes mellitus, often remains unnoticed until patient is facing complications. Artificial intelligence presents a promising solution that can enhance the accessibility of diabetic retinopathy screening for a broader range of patients. The key challenge lies in successfully integrating the solution into clinical practice, a demanding process with multiple phases to ensure the resulting medical device is effective and safe for patient use. Aireen software uses artificial intelligence to perform diabetic retinopathy screening on retinal images captured by optical fundus cameras. The medical device complies with European Medical Device Regulation 2017/745 and was introduced to the market in 2023. Collaboration between physicians and the development team played a crucial role throughout the entire lifecycle of the medical device. Physicians were engaged in defining the intended use of the medical device, risk analysis, data annotation for training and software validation, as well as throughout a clinical trial. A clinical trial was conducted on 1,274 patients with type 1 and type 2 diabetes mellitus, where Aireen medical device achieved a sensitivity of 94.0% and a specificity of 90.7% compared to the reference evaluation. This clinical trial confirmed the potential of Aireen to enhance the availability of diabetic retinopathy screening and early disease detection.