Studies in health technology and informatics最新文献

{"title":"Creation of an Animated 3D Model for the Design of an African Virtual Patient.","authors":"Jonathan Kambire, Seydou Golo Barro, Pascal Staccini","doi":"10.3233/SHTI250139","DOIUrl":"https://doi.org/10.3233/SHTI250139","url":null,"abstract":"<p><p>With a view to designing a medical tele-education platform incorporating a teleconsultation simulator, our research initially involved designing a 3D model of a virtual patient. To do this, we first carried out a literature review of existing work and a review of the literature on 3D modeling and animation. Next, we made a selection of modeling and animation tools, materials and techniques. Finally, we drew up scenarios for the signs and symptoms of the diseases we selected for the animation of the character in our study. The result was a 3D model with a skeleton for animation.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"491-495"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic Approaches in Reimbursable German Digital Health Applications (DiHA) for Depression.","authors":"Annabelle Mielitz, Ute von Jan, Urs-Vito Albrecht","doi":"10.3233/SHTI250119","DOIUrl":"https://doi.org/10.3233/SHTI250119","url":null,"abstract":"<p><p>This study analyzed digital health applications (DiHA) available in Germany for treating depression, with 41% (26/64) targeting mental health. Cognitive behavioral therapy (CBT) is the dominant approach, as six DiHA focus on CBT, featuring common components like managing unwanted thoughts, behavioral activation, and relaxation. However, only three apps include cognitive restructuring, a key aspect of CBT. There is a lack of DiHA for alternative therapy methods like depth psychology, limiting options for patients seeking different approaches. The analysis also found fragmented information on app components, suggesting a need for centralizing comprehensive data in the official DiHA directory for better decision-making.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"394-398"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aspects of FAIRness for Research Software Registries.","authors":"Wiebke Herbst, Christian Draeger, Mona Perbix, Alfred Winter, Matthias Löbe","doi":"10.3233/SHTI250112","DOIUrl":"https://doi.org/10.3233/SHTI250112","url":null,"abstract":"<p><p>Research software registries play an integral role in the process of making software findable, accessible, interoperable and reusable (FAIR). However, there are no guidelines available for FAIRifying registries listing research software. Identifying applicable criteria is a necessary step to develop recommendations that apply to software registries and can later be utilized to measure and improve their FAIRness. Principles that relate to FAIR research software were mapped to granular metrics. Afterwards, registry-applicable aspects were selected and summarized. This resulted in 17 aspects directed at metadata listed in research software registries. Each aspect is expressed in three tables, allowing easy access to relevant information as well as resolvable references to previous works regarding FAIRification. These can be used to develop new, quantifiable metrics. They also provide a meaningful reference point when establishing or improving such registries, an increasingly relevant field in light of the upcoming international initiatives, such as the EOSC.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"359-363"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.","authors":"Sunmoo Yoon, Frederick Sun, Peter Broadwell, Robert Crupi, Melissa Patterson, Tess D Pottinger, Milea Kim, Ncole Davis","doi":"10.3233/SHTI250055","DOIUrl":"https://doi.org/10.3233/SHTI250055","url":null,"abstract":"<p><p>We applied machine learning techniques to build models that predict perceived risks and benefits of using artificial intelligence (AI) algorithms to recruit African American informal caregivers for clinical trials and general health disparity research via social media platforms. In a U.S. sample of 572 family caregivers of a person with Alzheimer's disease and related dementias (ADRD), our application of the J48 algorithm (C4.5) revealed an interesting trend. African American family members of a person with ADRD were more likely to see the benefits of using AI on social media to ease the burden of recruitment, regardless of age, ethnicity, gender, and level of education. However, white family caregivers, particularly those aged 25-34 with graduate degrees, were more cautious and prone to perceive risks of using AI on social media for recruitment in research. This caution underscores the need for further research and understanding in this area.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"91-95"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Gensim TFIDF and LSI Models to Retrieve Potential Answers to Clinical Questions from Clinical Practice Guidelines.","authors":"Mohammadreza Azarpira","doi":"10.3233/SHTI250101","DOIUrl":"https://doi.org/10.3233/SHTI250101","url":null,"abstract":"<p><p>General Practitioners often encounter unanswered medical questions about patient symptoms or treatments at the point of care. Despite advances in information technology and the availability of the Internet, it is estimated that half of these questions remain unanswered. This proportion has remained stable over time. International Clinical Practice Guidelines (CPGs), which contain recently updated evidence, are an optimal source of information; more than 90% of relevant clinical questions can be answered using these guidelines. However, the large volume of these CPGs limits their accessibility at the point of care. We developed an Information Retrieval System using serialized Gensim TFIDF and LSI models to extract relevant answers to clinical questions. The true answer to clinical questions can be found in the first six answers of the algorithm in 98% of cases. This algorithm can be helpful for general practitioners to take greater advantage of CPGs at the point of care.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"307-311"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Microinstrument Motion Metrics of Distance, Speed, and Acceleration Indicate Surgical Task Complexity? An AI-Driven Study.","authors":"Gleb Danilov, Vasiliy Kostyumov, Oleg Pilipenko, Sergey Trubetskoy, Bulat Nutfullin, Oleg Titov, Eugeniy Ilyushin, David Pitskhelauri, Andrey Panteleev, Andrey Bykanov","doi":"10.3233/SHTI250059","DOIUrl":"https://doi.org/10.3233/SHTI250059","url":null,"abstract":"<p><p>Objectifying the quality of microsurgical technique is both crucial and challenging. The aim of this study was to evaluate whether microinstrument motion metricscan reflect the complexity of microsurgical tasks. The laboratory experiment involved 13 right-handed neurosurgeons tasked with using microsurgical scissors to cut a white thread at a spot marked by a purple dot under the microscope. Each participant completed the task under four consecutive conditions: with or without wrist stabilization on a support, both before and after muscle load. Using the promptable transformer model, we segmented microsurgical instruments from video recordings and extracted their skeletons and centers of mass. From the time series of the center of mass X and Y coordinates, we derived seven additional time series for velocity, acceleration, and the jerk along the X and Y axes, as well as the smoothness metric. We generated thirty-three statistical features for each time series using the feasts R package. These motion features were then compared pairwise across various tasks. Of the 1782 tests conducted, 164 (or 9.2%) revealed statistically significant differences in 66 motion features. Our results provide a proof-of-concept, showing that AI-derived microsurgical motion features can reflect the complexity of conditions encountered by the microsurgeon during surgery.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"111-115"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimizing STOPP and Beers Criteria Risks in PIM Treatments Using PM-TOM and ChatGPT: A Case Study.","authors":"Adnan Kulenovic, Azra Lagumdzija-Kulenovic","doi":"10.3233/SHTI250067","DOIUrl":"https://doi.org/10.3233/SHTI250067","url":null,"abstract":"<p><p>PM-TOM (Personalized Medicine-Therapy Optimization Method) is a clinical decision-support tool designed to optimize polypharmacy treatments by minimizing their adverse drug reactions (ADRs) caused by individual drugs or drug interactions (DDIs, DCIs, DFIs, DGIs), along with the risks identified by the STOPP and Beers criteria. On the other hand, AI tools like ChatGPT 4.0, trained on medical literature texts, can provide broader clinical reasoning and insights tailored to individual patient contexts. By referring to a documented deprescribing case, this study demonstrates the synergistic power of PM-TOM and ChatGPT in optimizing potentially inappropriate medication (PIM) treatments. A malnourished older woman was admitted to a deprescribing facility with recurrent falls, hypertension, ischemic heart disease, depression, osteoarthritis, osteoporosis, and GERD. She was initially prescribed acetaminophen, alendronate, omeprazole, lisinopril, metoprolol, aspirin, citalopram, and vitamin D, which were assessed as inadequate. While the discharge regimen improved some conditions by replacing alendronate with zoledronic acid and reducing some drug dosages, PM-TOM revealed that key risks, stemming primarily from omeprazole, aspirin, and citalopram, remained unaddressed. The discharge treatment was optimized with PM-TOM after considering alternative drug classes suggested by ChatGPT and elaborated in the available medical literature. In the optimized treatment, omeprazole (PPI) was replaced with famotidine (H2-blocker), citalopram (SSRI) with agomelatine (atypical antidepressant), zoledronic acid (bisphosphonate) with denosumab (RANK ligand inhibitor), aspirin (NSAID) with ticagrelor (antiplatelet), and lisinopril with benazepril (ACE inhibitor). These changes significantly reduced possible ADRs and the geriatric care criteria risks. Finally, ChatGPT validated the proposed adjustments, confirming their alignment with the guidelines and highlighting the potential for longer-term benefits. This case study illustrates how a combined use of PM-TOM and AI tools can effectively support the clinical decision-making process by optimizing polypharmacy treatments and minimizing their PIMs, major contributors to morbidity in older adults and high healthcare costs.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"149-153"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the Performance of a Large Language Model to Extract Signs and Symptoms from Clinical Notes.","authors":"C Mahony Reategui-Rivera, Joseph Finkelstein","doi":"10.3233/SHTI250051","DOIUrl":"https://doi.org/10.3233/SHTI250051","url":null,"abstract":"<p><p>Large language models (LLMs) have increasingly been used to extract critical information from unstructured clinical notes, which often include important details not captured in the structured sections of electronic health records (EHRs). This study assesses the performance of the GPT-4o LLM in extracting signs and symptoms (S&S) from clinical notes, focusing on both general and organ-specific (urological and cardiorespiratory) contexts. Clinical notes from the MTSamples corpora were manually annotated for comparison with the S&S extraction results using LLM. GPT-4o was applied to extract S&S using named entity recognition techniques. Key performance metrics-precision, recall, and F1-score-were used to evaluate and compare general and organ-specific results. The model showed high precision in general S&S extraction (78%) and achieved the highest precision for organ-specific tasks in the cardiorespiratory dataset (87%). For the urinary dataset, precision was also strong (81%), with balanced recall and F1-scores across analyses. These findings underscore GPT-4o's effectiveness in both general and domain-specific S&S extraction but highlight the need for domain-specific tuning and optimization to further improve recall and generalizability in specialized medical contexts.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"71-75"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Echocardiography Correlation with Seismocardiography-Systematic Review.","authors":"Dennis Lawin, Ulf Kulau, Urs-Vito Albrecht","doi":"10.3233/SHTI250113","DOIUrl":"https://doi.org/10.3233/SHTI250113","url":null,"abstract":"<p><p>A methodological systematic review included literature retrieved from Scopus, PubMed, and IEEE-Explore. 61 studies on seismocardiography (SCG) and echocardiography (ECHO) were found. After screening for aortic and mitral valve timing events, 12 studies were selected. These studies focused on correlating SCG signals with ECHO using M-mode, PW-Doppler, CW-Doppler, and TDI. Variations in sensor placement and subject positioning highlighted the need for standardization. Our review stresses the importance of clear objectives, standardized protocols, and recording disease-specific impacts on heart mechanics for future research.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"364-368"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Usability of a Mobile Platform for Scoliosis Screening Assessment.","authors":"Efstathios Sidiropoulos, Panagiotis David, Theodoros P Vagenas, Theodore Economopoulos, George Matsopoulos, Efstathios Kenanidis, Michael Potoupnis, Elefterios Tsiridis, Panagiotis Bamidis","doi":"10.3233/SHTI250121","DOIUrl":"https://doi.org/10.3233/SHTI250121","url":null,"abstract":"<p><p>Scoliosis is curvature of the spine, often found in adolescents, which can impact on their quality of life. In recent years, smartphone applications (apps) and web-based applications may help the parents with the doctors' supervision in scoliosis screening and monitoring, thereby reducing the number of in-person visits. This paper suggests the usage of the SCOLIOSIS system to detect the onset of scoliosis. This tool is being developed for simple use on mobile devices and as a web-based monitoring system for doctors, which will be an interactive tool for the patients and doctors that will provide data, information, and knowledge. The study conducts a usability assessment of the mobile application by doctors and non-clinician users. User test application developed for the android platform and the results show that this has the potential to be applied in medical practice.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"404-408"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}