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Backgrounds: Establishment of quantitative, objective evaluation tool for facial palsy has been a big issue for clinicians and researchers, and in an era of artificial intelligence, AI-driven video analysis can be considered a reasonable solution for this long-discussed issue. We introduced facial keypoint detection, which detects facial landmarks with 68 points, but existing models had been organized almost solely with images of healthy individuals and low accuracy was presumed in the prediction of asymmetric faces of facial palsy patients.The accuracy of existing model was assessed through applying it to the movies of 30 facial palsy patients. Qualitative review clearly showed its insufficiency; prone to detect patients' faces as symmetric, and unable to detect closure of the eyes. Thus, we decided to enhance the model through machine learning process of annotation (fine-tuning)., Methods: 1181 images extracted from the movies of 196 patients were enrolled in the training, and these images underwent manual correction of 68 keypoints. The annotated data was integrated into the previous model with a stack of two Hourglass networks combined with Channel Aggregation Block., Results: The post-annotation model showed improvement in normalized mean error from 0.026 to 0.018, and also qualitatively keypoint detection on each facial unit revealed improvements., Conclusion: Our strict control of the inter- and intra-annotator variability successfully fine-tuned the presenting model, and we consider our new model as one of promising solutions for objective assessment of facial palsy., (Copyright © 2024 by the American Society of Plastic Surgeons.)

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science., (© 2024 American Association of Physicists in Medicine.)

Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating enterprise imaging. It offers an overview of the process undertaken by the Health Information Management Systems Society (HIMSS) and Society for Imaging Informatics in medicine (SIIM) Enterprise Imaging Community Data Standards Evaluation workgroup to assess the viability of existing ontologies for supporting cross-disciplinary medical imaging workflows. The report analyzes the responses received from representatives of three significant ontologies: SNOMED CT, LOINC, and ICD, and delves into their suitability for the complex landscape of enterprise imaging. It highlights the strengths and limitations of each ontology, ultimately concluding that SNOMED CT is the most viable solution for standardizing anatomy terminology across the medical imaging community., Competing Interests: Declarations. Competing Interests: The following authors declare competing interests: Dawn Cram—Global Product Manager, PaxeraHealth; Mitchell Goldburgh—Sr. Director, Industry Solution, NTT DATA Services; Jason Nagels—CEO, Founder, Nagels Consulting; Lawrence Yudkovitch—Sr. Product Manager, Cleerly; and Alexander J. Towbin—(a) author royalties (Elsevier), (b) funded travel (Merative), and (c) consultant (Applied Radiology), while Monief Eid declares none., (© 2024. The Author(s).)

Detailed literature search and writing is very important for the success of long research projects, publications and theses. Search engines provide significant convenience in research processes. However, conducting a comprehensive and systematic research on the web requires a long working process. In order to make literature searches effective, simple and comprehensive, various libraries and development tools have been created and made available. By using these development tools, research processes that may take days can be reduced to hours or even minutes. Literature review is not only necessary for academic studies, but it is a process that should be used and performed in every field where new approaches are adopted. Literature review is a process that gives us important ideas about whether similar studies have been conducted before, which methods have been used before and what has not been addressed in previous studies. It is also of great importance in terms of preventing possible copyright problems in future studies. The main purpose of this study is to propose an application that will facilitate, speed up and increase the efficiency of literature searches. In existing systems, literature searches are performed by browsing search sites or various article sites one by one and using the search tools provided by these sites. It is simple to use, allows the entire World Wide Web environment to be searched, and provides the user with the search findings. In this study, we have implemented an application that allows the crawling of the entire World Wide Web environment, is very simple to use, and quickly presents the crawl findings to the user., Competing Interests: Muhammed Ali Mutlu, a graduate of our Master’s Program, is employed by NTT DATA Business Solutions. He is currently working as Head of Data Platforms at NTT DATA Business Solutions., (© 2024 Mutlu et al.)

This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been progress in adopting AI across multiple uses, and there continues to be an optimistic forecast for the impact on workflow and clinical outcomes. This report, as in prior years, should be seen as a snapshot of the use of AI in imaging. Compared to our 2021 survey, the 2023 respondents expressed wider AI adoption but felt this was behind the potential. Specifically, the adoption has increased as sources of return on investment with AI in radiology are better understood as documented by vendor/client use case studies. Generally, the discussions of AI solutions centered on workflow triage, visualization, detection, and characterization. Generative AI was also mentioned for improving productivity in reporting. As payor reimbursement remains elusive, the ROI discussions expanded to look at other factors, including increased hospital procedures and admissions, enhanced radiologist productivity for practices, and improved patient outcomes for integrated health networks. When looking at the longer-term horizon for AI adoption, respondents frequently mentioned that the opportunity for AI to achieve greater adoption with more complex AI and a more manageable/visible ROI is outside the USA. Respondents focused on the barriers to trust in AI and the FDA processes., (© 2024. The Author(s).)

Objectives: Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis., Materials and Methods: This multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities., Results: Overall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9-17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88-0.91), 0.85 (0.84-0.87), 0.83 (0.81-0.85), and 0.85 (0.82-0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84-0.92), 0.84 (0.78-0.88), 0.84 (0.76-0.89), and 0.84 (0.75-0.90) at 180, 360, 720, and 1,080 days, respectively., Conclusion: This study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings., 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. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Daichi Fujimoto reported receiving grants and personal fees from AstraZeneca KK and Boehringer Ingelheim Japan Inc. and personal fees from Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., Takeda Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., Chugai Pharmaceutical Co., Ltd., MSD KK, Eli Lilly Japan K.K., Kyowa Kirin, Daiichi Sankyo, and Novartis Pharma KK outside the submitted work. Hidetoshi Hayashi reported receiving grants from PAREXEL International Corp., Daiichi Sankyo Co., Ltd., Taiho Pharmaceutical Co., Ltd., MSD K.K., Ono Pharmaceutical Co., Ltd., SYNEOS HEALTH CLINICAL K.K., Covance Japan Inc., Sanofi K.K., Novartis Pharma K.K., Bristol Myers Squibb Company, Janssen Pharmaceutical K.K., Eisai Co., Ltd., Japan Clinical Research Operations, AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., Sysmex Corporation, Takeda Pharmaceutical Co., Ltd., IQVIA Services JAPAN K.K., GlaxoSmithKline K.K., EPS Corporation, Medical Research Support, PRA Health Science, Inc., Eisai., Inc., Nippon Boehringer Ingelheim Co., Ltd., and PPD-SNBL K.K. and personal fees from AstraZeneca K.K., Bristol Myers Squibb Company, Amgen, Inc., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Ono Pharmaceutical Co., Ltd., and Pfizer Japan Inc outside the submitted work. Yukihiro Toi reported receiving personal fees from Bristol-Myers Squibb Company, Ono Pharmaceutical Co., Ltd., MSD K.K., AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., Pfizer, Inc., and Kyowa Kirin Co., Ltd., outside the submitted work. Toshihide Yokoyama reported receiving personal fees from AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Pfizer Japan Inc, Bristol-Myers Squibb Co., Ltd., Ono Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., Nippon Kayaku Co., Ltd., MSD, Novartis, and Merck Biopharma Co., Ltd. outside the submitted work. Terufumi Kato reported receiving grants from Abbvie, Amgen, AstraZeneca, BeiGene, BluePrint, Chugai, Daiichi-Sankyo, Eli Lilly, Haihe, Merck KGaA, MSD, Novartis, Pfizer, Regeneron, Takeda, and TurningPoint and personal fees from Amgen, AstraZeneca, BeiGene, Boehringer Ingelheim, Chugai, Daiichi-Sankyo, Eli Lilly, GlaxoSmithKline, Janssen, Merck KGaA, MSD, Novartis, Ono, Pfizer, Taiho, and Takeda outside the submitted work, and his wife is an employee of Eli Lilly. Teppei Yamaguchi reported receiving personal fees from Ono Pharmaceutical, Chugai Pharmaceutical, Eli Lilly, Taiho Pharmaceutical, AstraZeneca, MSD, Bristol-Meyers Squibb, and Novartis outside the submitted work. Kaoru Tanaka reported receiving personal fees from AstraZeneca, Merck Biopharma, Eisai, Bristol-Myers Squibb, Ono Pharmaceutical, MSD, Chugai Pharmaceutical, Takeda Pharmaceutical Co., Ltd., Taiho Pharmaceutical, and Novartis Pharma outside the submitted work. Satoru Miura reported receiving personal fees from AstraZeneca KK, Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., Taiho Pharmaceutical Co., Ltd., Chugai Pharmaceutical Co., Ltd., MSD K.K., Boehringer Ingelheim Japan Inc, Eli Lilly Japan K.K., Novartis Pharma K.K., Pfizer, Kyowa Hakko Kirin, Daiichi Sankyo, Abbive, Nippon Kayaku, AMGEN, Merck, and Takeda Pharmaceutical Co., Ltd. outside the submitted work. Motohiro Tamiya reported receiving personal fees from Pfizer, Chugai Pharmaceutical, Ono Pharmaceutical, MSD, Boehringer Ingelheim, AstraZeneca, Taiho Pharmaceutical, Eli Lilly, Bristol-Myers Squibb, and Amgen outside the submitted work. Motoko Tachihara reported receiving grants and personal fees from AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., and Eli Lilly Japan Co., Ltd., and personal fees from Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., MSD K.K., Novartis pharmaceuticals K.K., Takeda Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., Nippon Boehringer Ingelheim Co., Ltd., Daiichi Sankyo, Pfizer Japan, Inc., and Janssen Pharmaceutical K.K. Takehito Shukuya reported receiving grants and personal fees from AstraZeneca, Chugai Pharmaceutical, Boehringer Ingelheim, Novartis, and MSD and personal fees from Taiho Pharma, Daiichi-Sankyo, Ono Pharmaceutical, Bristol-Myers Squibb, Nippon Kayaku, Pfizer, Takeda Pharmaceutical Co., Ltd., and Eli Lilly outside the submitted work. Yuko Tsuchiya-Kawano reported receiving personal fees from Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, AstraZeneca, Kyowa Hakko Kirin, MSD, and Ono Pharmaceutical outside the submitted work. Yuki Sato reported receiving personal fees from AstraZeneca, MSD, Novartis, Chugai Pharmaceutical, Ono Pharmaceutical, Pfizer, Taiho Pharmaceutical, Nippon Kayaku, Bristol Myers Squibb, Eli Lilly, Takeda Pharmaceutical Co., Ltd., Kyowa Kirin, and Boehringer Ingelheim outside the submitted work. Satoshi Ikeda reported receiving personal fees from AstraZeneca, Bristol Myers Squibb, Ono Pharmaceutical, Chugai Pharmaceutical, Taiho Pharmaceutical, Boehringer Ingelheim, Eli Lilly, Takeda Pharmaceutical Co., Ltd., Daiichi-Sankyo, and Pfizer outside the submitted work. Shinya Sakata reported receiving personal fees from AstraZeneca, Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical, and Takeda Pharmaceutical outside the submitted work. Takeshi Masuda reported receiving personal fees from MSD K.K., Eli Lilly Japan K.K., Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., AstraZeneca K.K., Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., and Kyowa Kirin outside the submitted work. Isamu Okamoto reported receiving personal fees from Nippon Boehringer Ingelheim Co., Ltd., AstraZeneca Plc., Eli Lilly Japan K.K., Takeda Pharmaceutical Co., Ltd., and Novartis Pharma K.K. outside the submitted work. Nobuyuki Yamamoto reported receiving grants from AstraZeneca K.K., MSD K.K., Amgen Astellas BioPharma, Eisai Co., Ltd., Sanofi K.K., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Novartis Pharma K.K., Janssen Pharmaceutical K.K., Amgen K.K., Taiho Pharmaceutical Co., Ltd., Toppan USA, Inc., Nippon Boehringer Ingelheim Co., Ltd., and Daiichi Sankyo Co., Ltd. and personal fees from MSD K.K., Amgen K.K., Takeda Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Nippon Boehringer Ingelheim Co., Ltd., AstraZeneca K.K., Ono Pharmaceutical Co., Ltd., Chugai Pharmaceutical Co., Ltd., Novartis Pharma K.K., and Merck Biopharma Co., Ltd., outside the submitted work. The remaining authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Learning through perceptual training using the Gabor patch (GP) has attracted attention as a new vision restoration technique for myopia and age-related deterioration of visual acuity (VA). However, the task itself is monotonous and painful and requires numerous training sessions and some time before being effective, which has been a challenge for its widespread application. One effective means of facilitating perceptual learning is the empowerment of EEG alpha rhythm in the sensory cortex before neurofeedback (NF) training; however, there is a lack of evidence for VA., Methods: We investigated whether four 30-min sessions of GP training, conducted over 2 weeks with/without EEG NF to increase alpha power (NF and control group, respectively), can improve vision in myopic subjects. Contrast sensitivity (CS) and VA were measured before and after each GP training., Results: The NF group showed an improvement in CS at the fourth training session, not observed in the control group. In addition, VA improved only in the NF group at the third and fourth training sessions, this appears as a consolidation effect (maintenance of the previous training effect). Participants who produced stronger alpha power during the third training session showed greater VA recovery during the fourth training session., Discussion: These results indicate that enhanced pretraining alpha empowerment strengthens the subsequent consolidation of perceptual learning and that even a short period of GP training can have a positive effect on VA recovery. This simple protocol may facilitate use of a training method to easily recover vision., Competing Interests: MC was employed by Vie, Inc. TK was employed by ROHTO Pharmaceutical Co., Ltd. TI and SS were employed by the companies NTT Data Institute of Management Consulting, Inc. and Vie, Inc. The authors would like to declare that a patent associated with this research is pending and that products related to this research will be developed in the future. This does not alter our adherence to Frontiers' policies on sharing data and materials., (Copyright © 2024 Chang, Suzuki, Kurose and Ibaraki.)

Study Design: Retrospective diagnostic study., Objective: To automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model., Summary of Background Data: Radiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients' quality of life at the end stage of cancer., Materials and Methods: This study included CT scans acquired at Tokyo Medical and Dental University (TMDU) Hospital between 2016 and 2022. A total of 263 positive CT scans that included at least one osteolytic bone metastasis lesion in the thoracolumbar spine and 172 negative CT scans without bone metastasis were collected for the datasets to train and validate the DL algorithm. As a test data set, 20 positive and 20 negative CT scans were separately collected from the training and validation datasets. To evaluate the performance of the established artificial intelligence (AI) model, sensitivity, precision, F1-score, and specificity were calculated. The clinical utility of our AI model was also evaluated through observer studies involving six orthopaedic surgeons and six radiologists., Results: Our AI model showed a sensitivity, precision, and F1-score of 0.78, 0.68, and 0.72 (per slice) and 0.75, 0.36, and 0.48 (per lesion), respectively. The observer studies revealed that our AI model had comparable sensitivity to orthopaedic or radiology experts and improved the sensitivity and F1-score of residents., Conclusion: We developed a novel DL-based AI model for detecting osteolytic bone metastases in the thoracolumbar spine. Although further improvement in accuracy is needed, the current AI model may be applied to current clinical practice., Level of Evidence: Level III., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

[Purpose] The aim of this study was to develop a novel wearable surface electromyograph called NOK, and compare its reliability and validity to an existing electromyograph. [Participants and Methods] The study participants were 23 healthy university students (Seven males and 16 females; age 20.3 ± 1.1 years [mean ± standard deviation]; height 162.0 ± 6.7 cm; weight 58.4 ± 10.1 kg) who all gave informed written consent. The newly developed electromyograph (NOK) features a rubberized skin contact surface that requires no electrodes and allows the acquisition of up to 10 channels of muscle waveforms on a portable personal computer. After measuring maximal isometric elbow extension and flexion, we examined muscle waveforms during isometric contractions of elbow joint flexion and extension at approximately 50% of maximal voluntary contraction using both NOK and Delsys electromyographs and compared the results of the two devices. [Results] We found a significant moderate correlation between the measurements by the two devices for biceps and triceps. The measurements by the two devices also showed strong measure-retest reliability. Systematic errors were observed for elbow flexion and extension in the two measurements, indicating limited agreement between the two measurement methods. [Conclusion] Although the new device also has high repeatability and reliability, it is unsuitable for analyzing detailed muscle activity. However, since it can measure up to 10 channels of muscle activity, it is expected to be used in the rehabilitation and sports field in the future., Competing Interests: None., (2024©by the Society of Physical Therapy Science. Published by IPEC Inc.)

Real-world evidence for clinical outcomes and treatment patterns in patients with hormone receptor-positive(HR+)and human epidermal growth factor receptor 2-negative(HER2-)early breast cancer(EBC)in Japan is limited. We aimed to provide recent evidence in this population using the National Database of Health Insurance Claims and Specific Health Check-ups of Japan(NDB). Adults ≥20 years old who were diagnosed with HR+/HER2- breast cancer and underwent breast resection surgery were followed up. Patient characteristics and treatment patterns were evaluated. Durations of overall post-operative endocrine therapy(ET)and luteinizing hormone-releasing hormone(LH-RH)agonist therapy, and time to metastasis/recurrence after surgery were analyzed using Kaplan-Meier method. Overall, 294,904 patients were included. Cyclophosphamide and tamoxifen were the most common peri-operative chemotherapeutic and ET drugs. Median(95% confidence interval[CI])duration of post-operative ET and LH-RH agonist therapy was 5.01(5.01-5.01)years and 2.13 (2.12-2.14)years, respectively. Five-year cumulative rate(95% CI)of any recurrence was 8.6%(8.5-8.7), visceral metastasis being the most common. Nation-wide treatment patterns were described, which were consistent with guideline recommendations for patients with HR+, HER2- EBC. Further discussion is required to delay metastasis/recurrence and improve clinical outcomes(Fig. 1: Plain language summary of the study).

Introduction: Technology Enhanced Learning (TEL) can provide the tools to safely master minimally invasive surgery (MIS) skills in patient-free environments and receive immediate objective feedback without the constant presence of an instructor. However, TEL-based systems tend to work isolated from one another, focus on different skills, and fail to provide contents without a sound pedagogical background., Objective: The objective of this descriptive study is to present in detail EASIER, an innovative TEL platform for surgical and interventional training, as well as the results of its validation., Methods: EASIER provides a Learning Management System (LMS) for institutions and content creators that can connect and integrate TEL "external assets" (virtual reality simulators, augmented box trainers, augmented videos, etc.) addressing different skills. The platform integrates all skills under an Assessment Module that measures skills' progress in different courses. Finally, it provides content creators with a pedagogical model to scaffold contents while retaining flexibility to approach course design with different training philosophies in mind. Three courses were developed and hosted in the platform to validate it with end-users in terms of usability, performance, learning results in the courses and student self-perception on learning., Results: In total 111 volunteers completed the validation. The study was limited due to the COVID-19 pandemic, which limited access to external assets (virtual reality simulators). Nevertheless, usability was rated with 73.1 in the System Usability Scale. Most positive aspects on performance were easiness to access the platform, easiness to change the configuration and not requiring additional plug-ins to use the platform. The platform was rated above average in the six scales of the User Experience Questionnaire. Overall, student results improved significantly across the three courses (p < 0.05)., Conclusions: This study provides, within its limitations, evidence on the usefulness of the EASIER platform for distance learning of MIS skills. Results show the potential impact of the platform and are an encouraging boost for the future, especially in the aftermath of the COVID-19 pandemic., Competing Interests: Declaration of Competing Interest Project EASIER (588404-EPP-1–2017-1-ES-EPPKA2-KA) is funded with support from the European Commission. The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This research examines the feasibility of using synchronization signals broadcasted by currently deployed fifth generation (5G) cellular networks to determine the position of a static receiver. The main focus lies on the analysis of synchronization among the base stations of a real 5G network in Milan, Italy, as this has a major impact on the accuracy of localization based on time of arrival measurements. Understanding such properties, indeed, is fundamental to characterize the clock drifts and implement compensation strategies as well as to identify the direct communication beam. The paper shows how the clock errors, i.e., inaccurate synchronization, among 5G base stations exhibit a significant bias, which is detrimental for precise cellular positioning. By compensating the synchronization errors of devices' clocks, we demonstrate that it is in principle possible to localize a static user with an accuracy of approximately 8-10 m in non-obstructed visibility conditions, for urban and rural scenarios, using the deployed 5G network operating at 3.68 GHz and relying on broadcast signals as defined by 5G Release 15 standard. This work has been funded by the European Space Agency (ESA) Navigation Innovation and Support Program (NAVISP) Element 2 pillar which aims at improving the competitiveness of the industry of the participating States in the global Positioning, Navigation and Timing (PNT) market., (© 2023. Springer Nature Limited.)

Background: Depression is frequently seen among pregnant women. This is called antenatal depression (AND). Aim: Our aim was to identify clusters of AND and its core symptoms. Methods: The Patient Health Questionnaire-9 (PHQ-9), Pregnancy-Unique Quantification of Emesis and Nausea (PUQE-24), and Nausea and Vomiting of Pregnancy Quality of Life Questionnaire (NVP-QOL) were distributed to 382 pregnant women with a gestational age of 10 to 13 weeks who were attending antenatal clinics. The two PHQ-9 subscale scores were entered into a 2-step cluster analysis. The PHQ-9 items' capacity to identify AND were examined in terms of the area under curve (AUC) of a receiver operating characteristic (ROC) analysis. The selected symptom items were examined for their diagnostic capability in terms of the graded response model (GRM) in the item response theory (IRT) analysis. Results: Three clusters emerged. Cluster 3 scored highly in the scores of the two PHQ-9 subscales and the two emesis scales. In the ROC, five items showed an AUC > 0.80. The GRM identified four items with high information: 'loss of interest', 'depressed mood', 'self-esteem', and 'poor concentration'. Conclusions: The core symptoms of antenatal depression were four non-somatic symptoms; particularly, 'depressed mood' and 'loss of interest'. AND did not exist alone, but was accompanied by nausea and vomiting. Hence, we propose a new category: emesis-depression complex among pregnant women.

Insomnia is associated with adverse outcomes in women in the perinatal period; thus, the assessment of insomnia is important for pregnant women. The Insomnia Severity Index (ISI) is an instrument used globally to assess the severity of insomnia. However, its factor structure and structural invariance for pregnant women have not been studied. Therefore, we aimed to conduct factor analyses to search for the best model to fit its structural invariance. A cross-sectional study with the ISI was conducted at one hospital and five clinics in Japan from January 2017 to May 2019. A set of questionnaires was administered on two occasions with a one-week interval. The study included 382 pregnant women ranging in gestational age from 10 to 13 weeks. One week later, 129 participants answered the retest. After exploratory and confirmatory factor analyses, the measurement and structural invariance between parity and two time points was tested. The two-factor structure model showed an acceptable fit for the ISI in pregnant women ( χ 2 (12) = 28.516, CFI = 0.971, RMSEA = 0.089). The model also showed satisfactory measurement and structure invariance between parity and time points. The findings indicate that the ISI's use would be appropriate for pregnant women as a two-factor subscale of "severity" and "impact", regardless of the parity or time point. The ISI's factor structure may vary by subject; hence, it is necessary to confirm the measurement and structural invariance of the subject for whom the ISI will be used. Furthermore, interventions that focus not only on total scores and cutoff points but also on the phenomenon of subscales should be considered.

Introduction: A framework that extracts oncological outcomes from large-scale databases using artificial intelligence (AI) is not well established. Thus, we aimed to develop AI models to extract outcomes in patients with lung cancer using unstructured text data from electronic health records of multiple hospitals., Methods: We constructed AI models (Bidirectional Encoder Representations from Transformers [BERT], Naïve Bayes, and Longformer) for tumor evaluation using the University of Miyazaki Hospital (UMH) database. This data included both structured and unstructured data from progress notes, radiology reports, and discharge summaries. The BERT model was applied to the Life Data Initiative (LDI) data set of six hospitals. Study outcomes included the performance of AI models and time to progression of disease (TTP) for each line of treatment based on the treatment response extracted by AI models., Results: For the UMH data set, the BERT model exhibited higher precision accuracy compared to the Naïve Bayes or the Longformer models, respectively (precision [0.42 vs. 0.47 or 0.22], recall [0.63 vs. 0.46 or 0.33] and F1 scores [0.50 vs. 0.46 or 0.27]). When this BERT model was applied to LDI data, prediction accuracy remained quite similar. The Kaplan-Meier plots of TTP (months) showed similar trends for the first (median 14.9 [95% confidence interval 11.5, 21.1] and 16.8 [12.6, 21.8]), the second (7.8 [6.7, 10.7] and 7.8 [6.7, 10.7]), and the later lines of treatment for the predicted data by the BERT model and the manually curated data., Conclusion: We developed AI models to extract treatment responses in patients with lung cancer using a large EHR database; however, the model requires further improvement., (© 2022. The Author(s).)

Background: The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such as machine learning (ML) are used in many productive sectors, including construction, and have proved significant in process optimizations and decision-making. Thus, advanced studies are required to comprehend the best way of using this technology to enhance construction site safety., Objective: This research developed a systematic literature review using ten scientific databases to retrieve relevant publications and fill the knowledge gaps regarding ML applications in construction accident prevention., Methods: This study examined 73 scientific articles through bibliometric research and descriptive analysis., Results: The results showed the publications timeline and the most recurrent journals, authors, institutions, and countries-regions. In addition, the review discovered information about the developed models, such as the research goals, the ML methods used, and the data features. The research findings revealed that USA and China are the leading countries regarding publications. Also, Support Vector Machine - SVM was the most used ML method. Furthermore, most models used textual data as a source, generally related to inspection reports and accident narratives. The data approach was usually related to facts before an accident (proactive data)., Conclusion: The review highlighted improvement proposals for future works and provided insights into the application of ML in construction safety management.

Background: Many pregnant women experience impairments in social, occupational, or other important functioning. Aim: This study aimed to confirm measurement and structural invariance of the Sheehan Disability Scale (SDS) and its validity during early pregnancy. Design: Longitudinal study with two observations. Methods: Questionnaires were distributed to pregnant women attending antenatal clinics at gestational weeks 10-13. Of 382 respondents, 129 responded to the SDS again 1 week later. Results: Confirmatory factor analysis shows good fit with the data: χ 2 / df = 0, comparative fit index (CFI) = 1.000, standardized root mean square residual (SRMR) = 0, and root mean square error of approximation (RMSEA) = 0.718. There is acceptable configural, measurement, and structural invariance of the factor structure between primiparas and multiparas as well as between two observation occasions. The Pregnancy-Unique Quantification of Emesis and Nausea, Patient Health Questionnaire-9, and Insomnia Severity Index subscales explain 47% of the variance in SDS scores. Conclusion: Perinatal health care professionals should pay more attention to the difficulties and disabilities that pregnant women face.

Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model's use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains., (© 2022. The Author(s).)

Melanoma is the cutaneous neoplasm responsible for more patient deaths in all countries. BRAF mutations are the most common driver mutation and with the development of molecular targeted therapy, the precise knowledge of BRAF status has become increasingly important. Evaluation of BRAF mutation status has routinely been performed by polymerase chain reaction, a time consuming and expensive technique. Immunohistochemistry has been suggested as a cheaper alternative, but it has not gained general acceptance. A retrospective observational study in a cohort of 106 patients with invasive melanoma was conducted in order to develop and evaluate a machine learning approach to predict BRAF status using clinical and histologic variables. We compared the performance of different common machine learning algorithms and use SHapley Additive exPlanations (SHAP) to explain individual predictions and extract medical insights to define a heuristic model to estimate BRAF mutation probability. The Extreme Gradient Boosting algorithms obtained the best performance. Interpretability of models shows that the most important variables to estimate BRAF mutation probability are: age, Breslow thickness, and Breslow density. Based in this interpretation and medical knowledge, a simplify heuristic model is proposed to predict BRAF status using only 7 variables and obtain a performance of 0.878 of area under the curve. We propose a heuristic model that could be used by clinicians to obtain a good estimator of BRAF mutation probability., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: The challenges of an aging population worldwide are the increased number of people needing medical and nursing care and inadequate medical resources. Information and communication technologies have progressed remarkably, leading to innovations in various areas. 5G communication systems are capable of high-capacity, high-speed communication with low latency and are expected to transform medicine. We aimed to report a demonstration experiment of telerehabilitation and telemedicine using a mobile ultrasound system in a depopulated area in a mountainous terrain, where 32% of the population are 65 years or older., Methods: At the core hospital, a physician or physical therapist remotely performed ultrasonography or rehabilitation on a subject in a clinic. Five general residents participated in the telerehabilitation as subjects. The delay time and video quality transmitted with 5G and long-term evolution (LTE) communication systems were compared. The physician or physical therapist subjectively evaluated the quality and delay of the transmitted images and subject acceptability., Results: Of seven physical therapists, six and three responded that the video quality was "good" for telerehabilitation with 5G/4K resolution and LTE, respectively. Five physical therapists and one physical therapist reported that the delay time was "acceptable" with 5G/4K resolution and LTE, respectively. For telemedicine using a mobile ultrasound system, the responses for 5G were "the delay was acceptable" and "rather acceptable." In contrast, both respondents' responses for LTE were "not acceptable.", Conclusions: Multiple high-definition images can be transmitted with lower latency in telerehabilitation and telemedicine using mobile ultrasound imaging systems with a 5G communication system. These differences affected the subjective evaluation of the doctors and physical therapists., (© The Author(s) 2022.)

Purpose: The purpose of this study was to clarify whether the novel lateral transfer assist robot facilitates easier transfers compared with a wheelchair in post-stroke hemiparesis patients., Methods: This cross-sectional study enrolled 20 post-stroke hemiparesis patients, and the task difficulty of transfers was compared between a wheelchair and lateral transfer assist robot. All participants were asked to transfer from either wheelchair or lateral transfer assist robot to a platform table and back. The primary outcome was the transfer score of the Functional Independence Measure. The secondary outcome was the time required for transfer., Results: The transfer score of the Functional Independence Measure was significantly higher with lateral transfer assist robot than with wheelchair ( p  < .001). The transfer times from these devices to a platform table and back showed no significant differences (to device from platform table: 7.8 s, lateral transfer assist robot vs 7.6 s, wheelchair, p  > .05: device to platform table: 7.1 s, lateral transfer assist robot vs 8.0 s, wheelchair, p > .05)., Conclusions: Transfer with a lateral transfer assist robot is easier than with wheelchair and facilitates independence in post-stroke hemiparesis patients.IMPLICATIONS FOR REHABILITATIONTransfer skill influences the functional independence and quality of life of a wheelchair userA novel structural mobility device-the lateral transfer assist robot (LTAR)-can facilitate transfersThe LTAR could improve the degree of independence for transfers than the wheelchair, without any time loss, in post-stroke hemiparesis patientsThe LTAR could potentially reduce the risk for falls in various medical and care facilities.

The chloroquine resistance transporter, PfCRT, is an essential factor during intraerythrocytic development of the human malaria parasite Plasmodium falciparum. PfCRT resides at the digestive vacuole of the parasite, where hemoglobin taken up by the parasite from its host cell is degraded. PfCRT can acquire several mutations that render PfCRT a drug transporting system expelling compounds targeting hemoglobin degradation from the digestive vacuole. The non-drug related function of PfCRT is less clear, although a recent study has suggested a role in oligopeptide transport based on studies conducted in a heterologous expression system. The uncertainty about the natural function of PfCRT is partly due to a lack of a null mutant and a dearth of functional assays in the parasite. Here, we report on the generation of a conditional PfCRT knock-down mutant in P. falciparum. The mutant accumulated oligopeptides 2 to at least 8 residues in length under knock-down conditions, as shown by comparative global metabolomics. The accumulated oligopeptides were structurally diverse, had an isoelectric point between 4.0 and 5.4 and were electrically neutral or carried a single charge at the digestive vacuolar pH of 5.2. Fluorescently labeled dipeptides and live cell imaging identified the digestive vacuole as the compartment where oligopeptides accumulated. Our findings suggest a function of PfCRT in oligopeptide transport across the digestive vacuolar membrane in P. falciparum and associated with it a role in nutrient acquisition and the maintenance of the colloid osmotic balance. IMPORTANCE The chloroquine resistance transporter, PfCRT, is important for the survival of the human malaria parasite Plasmodium falciparum. It increases the tolerance to many antimalarial drugs, and it is essential for the development of the parasite within red blood cells. While we understand the role of PfCRT in drug resistance in ever increasing detail, the non-drug resistance functions are still debated. Identifying the natural substrate of PfCRT has been hampered by a paucity of functional assays to test putative substrates in the parasite system and the absence of a parasite mutant deficient for the PfCRT encoding gene. By generating a conditional PfCRT knock-down mutant, together with comparative metabolomics and uptake studies using fluorescently labeled oligopeptides, we could show that PfCRT is an oligopeptide transporter. The oligopeptides were structurally diverse and were electrically neutral or carried a single charge. Our data support a function of PfCRT in oligopeptide transport.