International Journal of Medical Informatics最新文献

{"title":"Online professionalism through the lens of medical students and residents: A focus group study","authors":"Sebastiaan A. Pronk ,&nbsp;Simone L. Gorter ,&nbsp;Scheltus J. van Luijk ,&nbsp;Guy J. Oudhuis ,&nbsp;Pieter C. Barnhoorn ,&nbsp;Walther N.K.A. van Mook","doi":"10.1016/j.ijmedinf.2025.105879","DOIUrl":"10.1016/j.ijmedinf.2025.105879","url":null,"abstract":"<div><h3>Purpose</h3><div>Social media influences the practice of healthcare professionals. Existing studies on online professionalism and social media are scarce, and most used survey-based methods. This qualitative study explores online professionalism in healthcare among medical students and residents and maps their perceived educational needs.</div></div><div><h3>Method</h3><div>Semi-structured focus group interviews were conducted between September 2019 and June 2021 to explore the perceptions of online professionalism among Dutch medical students and residents. Interviews were recorded, transcribed, and thematically analyzed iteratively and independently by two researchers.</div></div><div><h3>Results</h3><div>Seven one-hour focus groups were conducted among 24 medical students and 22 residents. Patient requests from social media directed at students and residents occurred, none were accepted. Upon patient confidentiality breaches on social media, peers tended to speak up to one another. Participants voiced that clarity about the ‘grey areas’ − where distinguishing between right and wrong is difficult − of social media use is needed.</div></div><div><h3>Conclusions</h3><div>Social media use was widespread among participants and patients’ requests directed at students and residents did occur. They were unlikely to speak about online professionalism lapses to their peers unless a breach of patient confidentiality is involved. Educators should focus on enhancing the professional use of social media in both undergraduate and postgraduate training.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105879"},"PeriodicalIF":3.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating AI-generated patient education materials for spinal surgeries: Comparative analysis of readability and DISCERN quality across ChatGPT and deepseek models","authors":"Mi Zhou ,&nbsp;Yun Pan ,&nbsp;Yuye Zhang ,&nbsp;Xiaomei Song ,&nbsp;Youbin Zhou","doi":"10.1016/j.ijmedinf.2025.105871","DOIUrl":"10.1016/j.ijmedinf.2025.105871","url":null,"abstract":"<div><h3>Background</h3><div>Access to patient-centered health information is essential for informed decision-making. However, online medical resources vary in quality and often fail to accommodate differing degrees of health literacy. This issue is particularly evident in surgical contexts, where complex terminology obstructs patient comprehension. With the increasing reliance on AI models for supplementary medical information, the reliability and readability of AI-generated content require thorough evaluation.</div></div><div><h3>Objective</h3><div>This study aimed to evaluate four natural language processing models—ChatGPT-4o, ChatGPT-o3 mini, DeepSeek-V3, and DeepSeek-R1—in generating patient education materials for three common spinal surgeries: lumbar discectomy, spinal fusion, and decompressive laminectomy. Information quality was evaluated using the DISCERN score, and readability was assessed through Flesch-Kincaid indices.</div></div><div><h3>Results</h3><div>DeepSeek-R1 produced the most readable responses, with Flesch-Kincaid Grade Level (FKGL) scores ranging from 7.2 to 9.0, succeeded by ChatGPT-4o. In contrast, ChatGPT-o3 exhibited the lowest readability (FKGL &gt; 10.4). The DISCERN scores for all AI models were below 60, classifying the information quality as “fair,” primarily due to insufficient cited references.</div></div><div><h3>Conclusion</h3><div>All models achieved merely a “fair” quality rating, underscoring the necessity for improvements in citation practices, and personalization. Nonetheless, DeepSeek-R1 and ChatGPT-4o generated more readable surgical information than ChatGPT-o3. Given that enhanced readability can improve patient engagement, reduce anxiety, and contribute to better surgical outcomes, these two models should be prioritized for assisting patients in the clinical.</div></div><div><h3>Limitation &amp; Future direction</h3><div>This study is limited by the rapid evolution of AI models, its exclusive focus on spinal surgery education, and the absence of real-world patient feedback, which may affect the generalizability and long-term applicability of the findings. Future research ought to explore interactive, multimodal approaches and incorporate patient feedback to ensure that AI-generated health information is accurate, accessible, and facilitates informed healthcare decisions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105871"},"PeriodicalIF":3.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based personalized real-time risk prediction for behavioral management in psychiatric wards using multimodal data","authors":"Ri-Ra Kang ,&nbsp;Yong-gyom Kim ,&nbsp;Minseok Hong ,&nbsp;Yong Min Ahn ,&nbsp;KangYoon Lee","doi":"10.1016/j.ijmedinf.2025.105870","DOIUrl":"10.1016/j.ijmedinf.2025.105870","url":null,"abstract":"<div><h3>Background</h3><div>Suicide is a major global health issue, with approximately 700,000 deaths annually (WHO). In psychiatric wards, managing harmful behaviors such as suicide, self-harm, and aggression is essential to ensure patient and staff safety. However, psychiatric wards in South Korea face challenges due to high patient-to-psychiatrist ratios and heavy workloads. Current models relying on demographic data struggle to provide real-time predictions. This study introduces the Temporal Fusion Transformer (TFT) model to address these limitations by integrating sensor, location, and clinical data for predicting harmful behaviors. The TFT model’s advanced features, such as Variable Selection Networks and temporal attention mechanisms, make it particularly suitable for capturing complex time-series patterns and providing interpretable results in psychiatric settings.</div></div><div><h3>Methods</h3><div>Data from 145 patients across three hospitals were collected using wearable devices that tracked heart rate, movement, and location. The data were aggregated hourly, preprocessed to handle missing values, and standardized. A binary classification model using TFT was developed and evaluated with accuracy, recall, F1 score, and AUC. Bayesian optimization was employed for hyperparameter tuning, and 5-fold cross-validation was performed to ensure generalizability.</div></div><div><h3>Results</h3><div>The TFT model outperformed Multi-LSTM and Multi-GRU models, achieving 95.1% accuracy, 74.9% recall, an F1 score of 78.1, and an AUC of 0.863. The Variable Selection Network effectively identified key predictive factors, such as daily entropy and heart rate variability, improving interpretability. Incorporating location and biometric data enhanced prediction accuracy and enabled real-time risk assessments.</div></div><div><h3>Conclusion</h3><div>This study is the first to use the TFT model for predicting behavioral risks in psychiatric wards. The model’s ability to integrate diverse data sources, prioritize cirtical variables, and capture temporal dependencies make it highly suitable for psychiatric environments. While the TFT model performed well, challenges remain with recall due to the limited dataset. Future research will focus on expanding datasets, optimizing variable selection, and standardizing data through a multimodal Common Data Model (CDM) to further improve performance and clinical utility.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105870"},"PeriodicalIF":3.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of patients’ personality traits on digital health Adoption Strategies for family practices","authors":"Julian Beerbaum ,&nbsp;Sibylle Robens ,&nbsp;Leonard Fehring ,&nbsp;Achim Mortsiefer ,&nbsp;Sven Meister","doi":"10.1016/j.ijmedinf.2025.105880","DOIUrl":"10.1016/j.ijmedinf.2025.105880","url":null,"abstract":"<div><h3>Background</h3><div>Various governments highlight the relevance of digitalization in family practices; however, still some adoption barriers persist due to an inadequate understanding of why patients engage in digital use cases. Different studies show that personality traits influence how individuals assess digital use cases. Nevertheless, the effect of personality has not yet been tested in a family practice setting, even though family doctors are in an optimal position to use these personality insights via an empathetic communication approach in their direct patient interaction.</div></div><div><h3>Objective</h3><div>This paper aims to assess the impact of different personality traits on patients’ technology acceptance and derive implications for Digital Health Adoption Strategies of family practices – hence, what family doctors need to consider when influencing a patient’s decision to adopt a particular digital use case.</div></div><div><h3>Methods</h3><div>After reviewing the literature regarding the impact of personality on technology acceptance, we combined two established UTAUT and Big-Five questionnaires in a web-based survey. Recruiting a large cross-sectional sample of adults living in Germany, we conducted regression analyses to determine the effect of personality and sociodemographics on technology acceptance of four digital use cases in family practices.</div></div><div><h3>Results</h3><div>Our sample of 1,880 participants indicated that sociodemographics explained technology acceptance better than personality traits. Specifically, digital literacy, age and frequency of doctor visits affected people’s perception of different digital use cases while extraversion appeared as key personality trait in technology acceptance.</div></div><div><h3>Conclusion</h3><div>Family practices only need to consider personality traits selectively in developing Digital Health Adoption Strategies. Nevertheless, we argue that different patient personality profiles can guide family doctors in tailoring their communication while implementing digital use cases.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105880"},"PeriodicalIF":3.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU","authors":"Xiao Luo ,&nbsp;Binghan Li ,&nbsp;Ronghui Zhu ,&nbsp;Yaoyong Tai ,&nbsp;Zongyu Wang ,&nbsp;Qian He ,&nbsp;Yanfang Zhao ,&nbsp;Xiaoying Bi ,&nbsp;Cheng Wu","doi":"10.1016/j.ijmedinf.2025.105874","DOIUrl":"10.1016/j.ijmedinf.2025.105874","url":null,"abstract":"<div><h3>Background</h3><div>Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications are equally crucial. This study aims to develop an interpretable machine learning (IML) model that effectively predicts in-hospital mortality for ischemic stroke patients.</div></div><div><h3>Methods</h3><div>In this study, an IML model was developed and validated using multicenter cohorts of 3225 ischemic stroke patients admitted to the ICU. Nine machine learning (ML) models, including logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), decision tree (DT), support vector machine (SVM), random forest (RF), XGBoost, LightGBM, and artificial neural network (ANN), were developed to predict in-hospital mortality using data from the MIMIC-IV and externally validated in Shanghai Changhai Hospital. Feature selection was conducted using three algorithms. Model’s performance was assessed using area under the receiver operating characteristic (AUROC), accuracy, sensitivity, specificity and F1 score. Calibration curve and Brier score were used to evaluate the degree of calibration of the model, and decision curve analysis were generated to assess the net clinical benefit. Additionally, the SHapley Additive exPlanations (SHAP) method was employed to evaluate the risk of in-hospital mortality among ischemic stroke patients admitted to the ICU.</div></div><div><h3>Results</h3><div>Mechanical ventilation, age, statins, white blood cell, blood urea nitrogen, hematocrit, warfarin, bicarbonate and systolic blood pressure were selected as the nine most influential variables. The RF model demonstrated the most robust predictive performance, achieving AUROC values of 0.908 and 0.858 in the testing set and external validation set, respectively. Calibration curves also revealed a high consistency between observations and predictions. Decision curve analysis showed that the model had the greatest net benefit rate when the prediction probability threshold is 0.10 ∼ 0.80. SHAP was employed to interpret the RF model. In addition, we have developed an online prediction calculator for ischemic stroke patients.</div></div><div><h3>Conclusion</h3><div>This study develops a machine learning–based calculator to predict the probability of in-hospital mortality among patients with ischemic stroke in ICU. The calculator has the potential to guide clinical decision-making and improve the care of patients with ischemic stroke by identifying patients at a higher risk of in-hospital mortality.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105874"},"PeriodicalIF":3.7,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A qualitative study exploring electronic health record optimisation activities in English hospitals","authors":"Kathrin Cresswell ,&nbsp;Susan Hinder ,&nbsp;Robin Williams","doi":"10.1016/j.ijmedinf.2025.105868","DOIUrl":"10.1016/j.ijmedinf.2025.105868","url":null,"abstract":"<div><h3>Background</h3><div>Hospitals increasingly implement complex electronic health record (EHR) systems to improve quality, safety and efficiency. Whilst many aspects surrounding implementation and adoption processes have been researched, the benefits of such enterprise-wide systems may take decades to materialise. Existing work on optimisation processes has focused on technological, workflow and organisational aspects of optimisation within individual clinical settings, mostly in the United States of America. We here sought to explore how a range of hospitals with different EHR systems have approached the optimisation of EHRs over time and in relation to technology, socio-organisational and health system factors.</div></div><div><h3>Methods</h3><div>We conducted an in-depth qualitative interview study with technology leads from purposefully sampled hospitals across the country who had implemented a range of EHRs. We explored reflections on the journey of implementing and optimising systems over time, optimisation activities, and perceived lessons learned. Data were transcribed and analysed with NVivo 14 software, using the Technology, People, Organizations, and Macroenvironmental factors (TPOM) framework<!--> <!-->to facilitate coding.</div></div><div><h3>Results</h3><div>We interviewed 28 individuals from 21 sites with eight different types of EHRs. We observed various optimisation activities across different technological, social, organisational and health system factors. These included improving usability and information technology infrastructures; process optimisation of clinical and administrative workflows; organisational optimisation strategies and relationships with suppliers; and wider system factors such as the need for overall strategic direction and allocation of associated funding. Optimisation activities within these areas stood in some instances in contrast to one another. For example, national activities inhibited local optimisation efforts and organisational optimisation in some instances impacted adversely on usability.</div></div><div><h3>Conclusions</h3><div>This work emphasises that EHRs are not finished solutions but components of broader information systems needing continuous technological and organisational development. Effective optimisation requires a delicate balance between navigating technological affordances and characteristics to improve usability and organisational processes, as well as regional and national integration to achieve larger-scale interoperability.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105868"},"PeriodicalIF":3.7,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient consent for the secondary use of health data in artificial intelligence (AI) models: A scoping review","authors":"Khadijeh Moulaei ,&nbsp;Saeed Akhlaghpour ,&nbsp;Farhad Fatehi","doi":"10.1016/j.ijmedinf.2025.105872","DOIUrl":"10.1016/j.ijmedinf.2025.105872","url":null,"abstract":"<div><h3>Background</h3><div>The secondary use of health data for training Artificial Intelligence (AI) models holds immense potential for advancing medical research and healthcare delivery. However, ensuring patient consent for such utilization is paramount to uphold ethical standards and data privacy. Patient informed consent means patients are fully informed about how their data will be collected, used, and protected, and they voluntarily agree to allow their data to be used for AI models. In addition to formal consent frameworks, establishing a social license is critical to foster public trust and societal acceptance for the secondary use of health data in AI systems. This study examines patient consent practices in this domain.</div></div><div><h3>Method</h3><div>In this scoping review, we searched Web of Science, PubMed, and Scopus. We included studies in English that addressed the core issues of interest, namely, privacy, security, legal, and ethical issues related to the secondary use of health data in AI models. Articles not addressing the core issues, as well as systematic reviews, <em>meta</em>-analyses, books, letters, conference abstracts, and study protocols were excluded. Two authors independently screened titles, abstracts, and full texts, resolving disagreements with a third author. Data was extracted using a data extraction form.</div></div><div><h3>Results</h3><div>After screening 774 articles, a total of 38 articles were ultimately included in the review. Across these studies, a total of 178 barriers and 193 facilitators were identified. We consolidated similar codes and extracted 65 barriers and 101 facilitators, which we then categorized into four themes: “Structure,” “People,” “Physical system,” and “Task.” We identified notable emphasis on “Legal and Ethical Challenges” and “Interoperability and Data Governance.” Key barriers included concerns over privacy and security breaches, inadequacies in informed consent processes, and unauthorized data sharing. Critical facilitators included enhancing patient consent procedures, improving data privacy through anonymization, and promoting ethical standards for data usage.</div></div><div><h3>Conclusion</h3><div>Our study underscores the complexity of patient consent for the secondary use of health data in AI models, highlighting significant barriers and facilitators within legal, ethical, and technological domains. We recommend the development of specific guidelines and actionable strategies for policymakers, practitioners, and researchers to improve informed consent, ensuring privacy, trust, and ethical use of data, thereby facilitating the responsible advancement of AI in healthcare.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105872"},"PeriodicalIF":3.7,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis","authors":"Yuan Zhang ,&nbsp;Huan Liu ,&nbsp;Qingxia Huang ,&nbsp;Wantong Qu ,&nbsp;Yanyu Shi ,&nbsp;Tianyang Zhang ,&nbsp;Jing Li ,&nbsp;Jinjin Chen ,&nbsp;Yuqing Shi ,&nbsp;Ruixue Deng ,&nbsp;Ying Chen ,&nbsp;Zepeng Zhang","doi":"10.1016/j.ijmedinf.2025.105875","DOIUrl":"10.1016/j.ijmedinf.2025.105875","url":null,"abstract":"<div><h3>Background</h3><div>Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, the predictive accuracy of these models remains controversial.</div></div><div><h3>Objective</h3><div>This study aimed to review the accuracy of ML in predicting in-hospital mortality risk in MI patients and to provide evidence-based advices for the development or updating of clinical tools.</div></div><div><h3>Methods</h3><div>PubMed, Embase, Cochrane, and Web of Science databases were searched, up to June 4, 2024. PROBAST and ChAMAI checklist are utilized to assess the risk of bias in the included studies. Since the included studies constructed models based on severely unbalanced datasets, subgroup analyses were conducted by the type of dataset (balanced data, unbalanced data, model type).</div></div><div><h3>Results</h3><div>This meta-analysis included 32 studies. In the validation set, the pooled C-index, sensitivity, and specificity of prediction models based on balanced data were 0.83 (95 % CI: 0.795–0.866), 0.81 (95 % CI: 0.79–0.84), and 0.82 (95 % CI: 0.78–0.86), respectively. In the validation set, the pooled C-index, sensitivity, and specificity of ML models based on imbalanced data were 0.815 (95 % CI: 0.789–0.842), 0.66 (95 % CI: 0.60–0.72), and 0.84 (95 % CI: 0.83–0.85), respectively.</div></div><div><h3>Conclusions</h3><div>ML models such as LR, SVM, and RF exhibit high sensitivity and specificity in predicting in-hospital mortality in MI patients. However, their sensitivity is not superior to well-established scoring tools. Mitigating the impact of imbalanced data on ML models remains challenging.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105875"},"PeriodicalIF":3.7,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PlasmaCell CAD: A computer-aided diagnosis software tool for plasma cell recognition and characterization in microscopic images","authors":"Rasoul Kasbgar ,&nbsp;Alireza Vard","doi":"10.1016/j.ijmedinf.2025.105869","DOIUrl":"10.1016/j.ijmedinf.2025.105869","url":null,"abstract":"<div><h3>Background and objective</h3><div>In the traditional diagnostic process for multiple myeloma cancer, a pathologist screens prepared blood samples using a microscope to detect, classify, and count plasma cells. This manual approach is time-consuming, exhausting, and prone to human errors. Consequently, medical experts and researchers are highly interested in any tool that partially or entirely automates this process. To achieve this goal, we developed a software tool called PlasmaCell CAD to analyze effective cells for diagnosing multiple myeloma cancers through microscopic images.</div></div><div><h3>Methods</h3><div>In the proposed software, to detect and segment cells, we exploit the Mask-RCNN model that has been enhanced by leveraging the circlet transform for the anchor generation. Also, we use the SVM classifier to identify normal and abnormal plasma cells in this software. Moreover, we designed and developed a graphical user interface (GUI) for the PlasmaCell CAD so that users would be able to work with it more easily.</div></div><div><h3>Results</h3><div>we considered the performance of the proposed software on both a publicly available dataset and a locally collected dataset. The experimental results demonstrated the capability and efficiency of PlasmaCell CAD software in segmenting and classifying plasma cells as well as its ease of use.</div></div><div><h3>Conclusions</h3><div>PlasmaCell CAD is a free software tool that can easily be downloaded and installed on any computers running Windows. PlasmaCell CAD provides a user-friendly GUI with several image processing and visualization facilities for the user that can accelerate the diagnosis process. In light of promising results, PlasmaCell CAD software can be useful to pathologists in helping to diagnose multiple myeloma cancer.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105869"},"PeriodicalIF":3.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stress monitoring using low-cost electroencephalogram devices: A systematic literature review","authors":"Gideon Vos ,&nbsp;Maryam Ebrahimpour ,&nbsp;Liza van Eijk ,&nbsp;Zoltan Sarnyai ,&nbsp;Mostafa Rahimi Azghadi","doi":"10.1016/j.ijmedinf.2025.105859","DOIUrl":"10.1016/j.ijmedinf.2025.105859","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Introduction&lt;/h3&gt;&lt;div&gt;The use of low-cost, consumer-grade wearable health monitoring devices has become increasingly prevalent in mental health research, including stress studies. While cortisol response magnitude remains the gold standard for stress assessment, an expanding body of research employs low-cost EEG devices as primary tools for recording biomarker data, often combined with wrist and ring-based wearables. However, the technical variability among low-cost EEG devices, particularly in sensor count and placement according to the 10-20 Electrode Placement System, poses challenges for reproducibility in study outcomes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This review aims to provide an overview of the growing application of low-cost EEG devices and machine learning techniques for assessing brain function, with a focus on stress detection. It also highlights the strengths and weaknesses of various machine learning methods commonly used in stress research, and evaluates the reproducibility of reported findings along with sensor count and placement importance.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;A comprehensive review was conducted of published studies utilizing EEG devices for stress detection and their associated machine learning approaches. Searches were performed across databases including Scopus, Google Scholar, ScienceDirect, Nature, and PubMed, yielding 69 relevant articles for analysis. The selected studies were synthesized into four thematic categories: stress assessment using EEG, low-cost EEG devices, datasets for EEG-based stress measurement, and machine learning techniques for EEG-based stress analysis. For machine learning-focused studies, validation and reproducibility methods were critically assessed. Study quality was evaluated and scored using the IJMEDI checklist.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The review identified several studies employing low-cost EEG devices to monitor brain activity during stress and relaxation phases, with many reporting high predictive accuracy using various machine learning validation techniques. However, only 54% of the studies included health screening prior to experimentation, and 58% were categorized as low-powered due to limited sample sizes. Additionally, few studies validated their results using an independent validation set or cortisol response as a correlating biomarker and there was a lack of consensus on data pre-processing and sensor placement as a key contributor to improving model generalization and accuracy.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;Low-cost consumer-grade wearable devices, including EEG and wrist-based monitors, are increasingly utilized in stress-related research, offering promising avenues for non-invasive biomarker monitoring. However, significant gaps remain in standardizing EEG signal processing and sensor placement, both of which are critical for enhancing model generalization and accuracy. Furthermore, the limited use of independent validation ","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"198 ","pages":"Article 105859"},"PeriodicalIF":3.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}