Informatics in Medicine Unlocked最新文献

{"title":"A stochastic model for evaluating the progression of ductal carcinoma in situ breast cancer using Norwegian breast cancer screening program data","authors":"Xiaoxue Li , Harald Weedon-Fekjær , Bo Zhang , Sandra J. Lee","doi":"10.1016/j.imu.2025.101647","DOIUrl":"10.1016/j.imu.2025.101647","url":null,"abstract":"<div><h3>Background</h3><div>Following widespread mammography screening for breast cancer, the incidence of ductal carcinoma in situ (DCIS) has increased sharply. However, the value of detecting DCIS by screening is uncertain as not all DCIS progresses to invasive breast cancer. Knowledge about the sojourn time in the screen-detectable DCIS state and the progression or regression of DCIS to other stages (i.e., the natural history of DCIS) is essential to treat screen-detected DCIS lesions.</div></div><div><h3>Methods</h3><div>We developed a stochastic model for DCIS natural history, characterized by DCIS states, invasive breast cancer states, and transition probabilities between the states. The model included DCIS lesions in the screen-detectable preclinical state and their progression to clinical DCIS, invasive breast cancer, or regression to a state undetectable by screening. Unlike currently available DCIS Markov models, the proposed model assumed no relationship between the sojourn time and transition probabilities in DCIS states and used age-specific transition probabilities. In the absence of ideal data for DCIS modeling, the Norwegian Breast Cancer Screening Program data, specifically arranged by screening round and mode of detection, was applied to obtain maximum likelihood estimates of DCIS natural history parameters, including transition probabilities and the mean sojourn time in the preclinical screen-detectable DCIS state.</div></div><div><h3>Results</h3><div>By indirectly specifying a range of the proportion of breast lesions in the preclinical undetectable DCIS state (S<sub>du</sub>) that progress through the preclinical screen-detectable DCIS state (S<sub>dp</sub>), <em>P</em><sub><em>d</em></sub><em>(t)</em>, not going directly to preclinical invasive breast cancer (S<sub>p</sub>), plausible sets of DCIS natural history parameters were systematically evaluated. All estimates indicated that the mean sojourn time in S<sub>dp</sub> was relatively short (≤3.5 years). For the age group 50–54 years, the best fitting mean sojourn time in S<sub>dp</sub> was 3.4–3.5 years, with mammography sensitivity 0.60–0.61 when <em>P</em><sub><em>d</em></sub><em>(t)</em> was 0.31–0.34. When <em>P</em><sub><em>d</em></sub><em>(t)</em> was larger, mean sojourn times in S<sub>dp</sub> likely varied by the pathway. In general, assuming higher <em>P</em><sub><em>d</em></sub><em>(t)</em>—that is, a higher proportion of DCIS lesions that progress to from S<sub>dp</sub> to S<sub>p</sub>—the mean sojourn time became shorter. Regression to no cancer or undetectable state might be possible, but the quantified level of regression was associated with great uncertainties.</div></div><div><h3>Conclusion</h3><div>While difficult to point to a unique set of DCIS natural history estimates, identifying broader sets of plausible estimates is possible. Estimates reported here provide a comprehensive view of potential progression paths of DCIS while acknowledging the limit","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101647"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899764","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":"Regression and classification of Windkessel parameters from non-invasive cardiovascular quantities using a fully connected neural network","authors":"Ahmed Gdoura ,&nbsp;Stefan Bernhard","doi":"10.1016/j.imu.2025.101614","DOIUrl":"10.1016/j.imu.2025.101614","url":null,"abstract":"<div><div>Despite their simplicity, three-element Windkessel models (WK-3) provide an effective and straightforward representation of the aortic input impedance. The WK-3 model not only captures valuable information about the mechanical and structural characteristics of the aortic arch but also generates reliable estimations of the central blood pressure (cBP) wave, a significant cardiovascular risk indicator. However, fitting the parameters of the WK-3 model typically requires invasively collected data, which carries substantial risk and high cost for patients.</div><div>This study aims to enable non-invasive impedance estimation of the WK-3 model using cardiovascular signals. As a proof of concept, we developed and trained a fully connected neural network (FCNN) on an in-silico dataset to predict the WK-3 parameters: characteristic impedance, peripheral arterial resistance, and arterial compliance. These predictions are based on non-invasive parameters, including zero-flow pressure intercept, heart rate, stroke volume, and left ventricular ejection time.</div><div>The proposed model achieved an overall accuracy of 80% with an average area under the curve (AUC) of <span><math><mrow><mn>0</mn><mo>.</mo><mn>91</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>11</mn></mrow></math></span>. The implementation and best-fitting model are available for download from <span><span>this link</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101614"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography","authors":"Germán Enrique Galvis Ruiz ,&nbsp;Johana Benavides-Cruz ,&nbsp;Daniela Muñoz Corredor ,&nbsp;Esteban Morales-Mendoza ,&nbsp;Héctor Daniel Alejandro Cotrino Palma ,&nbsp;Andrés Cely-Jiménez","doi":"10.1016/j.imu.2024.101605","DOIUrl":"10.1016/j.imu.2024.101605","url":null,"abstract":"<div><div>Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it difficult for physicians to interpret opacities. Artificial intelligence models are frequently employed by physicians for diagnostic support in healthcare, especially to evaluate aspects of radiographs that are not visible with the naked eye. In this study, a prediction model based on deep learning was used to differentiate between atelectasis and consolidations in pediatric chest radiographs from a clinical perspective. The radiologist can assist pediatricians in diagnosing respiratory pathologies based on the type of opacities using the machine learning model. We used 1297 chest X-ray images of pediatric patients with opacities including consolidations (<span><math><mrow><mi>n</mi><mo>=</mo><mn>500</mn></mrow></math></span>), atelectasis (<span><math><mrow><mi>n</mi><mo>=</mo><mn>499</mn></mrow></math></span>); and images without opacities (<span><math><mrow><mi>n</mi><mo>=</mo><mn>298</mn></mrow></math></span>). The images were preprocessed, and various deep learning models were applied to determine the model with the best metrics. The InceptionV3 model demonstrated a significant improvement over its initial results.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101605"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust assessment of cervical precancerous lesions from pre- and post-acetic acid cervicography by combining deep learning and medical guidelines","authors":"Siti Nurmaini ,&nbsp;Patiyus Agustiyansyah ,&nbsp;Muhammad Naufal Rachmatullah ,&nbsp;Firdaus Firdaus ,&nbsp;Annisa Darmawahyuni ,&nbsp;Bambang Tutuko ,&nbsp;Ade Iriani Sapitri ,&nbsp;Anggun Islami ,&nbsp;Akhiar Wista Arum ,&nbsp;Rizal Sanif ,&nbsp;Irawan Sastradinata ,&nbsp;Legiran Legiran ,&nbsp;Radiyati Umi Partan","doi":"10.1016/j.imu.2024.101609","DOIUrl":"10.1016/j.imu.2024.101609","url":null,"abstract":"<div><div>Cervical cancer remains a major public health challenge, particularly in low-resource settings where access to regular screening and expert medical evaluation is limited. Traditional visual inspection with acetic acid (VIA) has been widely used for cervical cancer screening but is subjective and highly dependent on the expertise of the healthcare provider. This study presents a comprehensive methodology for decision-making regarding cervical precancerous lesions using cervicograms taken before and after the application of acetic acid. By leveraging the power of the deep learning (DL) model with You Only Look Once (Yolo) version 8, Slicing Aided Hyper Inference (SAHI), and oncology medical guidelines, the system aims to improve the accuracy and consistency of VIA assessments. The method involves training a Yolov8xl model on our cervicogram dataset, annotated by two oncologists using VIA screening results, to distinguish between the cervical area, columnar area, and lesions. The model is designed to process cervicography images taken both before and after the application of acetic acid, capturing the dynamic changes in tissue appearance indicative of precancerous conditions. The automated evaluation system demonstrated high sensitivity and specificity in detecting cervical lesions with 90.78 % accuracy, 91.67 % sensitivity, and 90.96 % specificity, outperforming other existing methods. This work represents a significant step towards deploying AI-driven solutions in cervical cancer screening, potentially reducing the global burden of the disease. It can be integrated into existing screening programs, providing a valuable tool for early detection and intervention, especially in regions with limited access to trained medical personnel.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101609"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the onset of the RSV epidemic with meteorological data using deep neural networks","authors":"Kazuo Yonekura ,&nbsp;Miya Nishio ,&nbsp;Momoko Kashiwado ,&nbsp;Takuya Naruto ,&nbsp;Masaaki Mori","doi":"10.1016/j.imu.2025.101659","DOIUrl":"10.1016/j.imu.2025.101659","url":null,"abstract":"<div><h3>Background</h3><div>Respiratory syncytial virus (RSV) is a contagious virus that infects nearly all children by the age of two and is a leading cause of hospitalization and mortality among young children. Despite the recent approval of RSV vaccines for elderly and pregnant individuals, immune prophylaxis remains essential for pediatric cases. In Japan, the typical RSV season has shifted, making timely prediction crucial for effective clinical intervention.</div></div><div><h3>Objective</h3><div>This study aims to predict the onset of RSV epidemics in Japan using meteorological data, based on the hypothesis that meteorological data affect the spread of RSV.</div></div><div><h3>Methods</h3><div>We collected weekly RSV case counts from the Japanese National Institute of Infectious Diseases and daily meteorological data from the Japan Meteorological Agency for the period 2012–2023. Using aggregated weather features (mean, max, min), we constructed a binary classification task to identify the onset of RSV spread. Machine learning models including a support vector machine (SVM), XGBoost, and a deep neural network (DNN) were evaluated.</div></div><div><h3>Results</h3><div>The DNN outperformed other models, achieving the highest F1 score (0.71) and recall (0.83), particularly with a 3-week-ahead prediction horizon. The model demonstrated early detection capability across multiple prefectures, although performance varied geographically, with lower F1 scores in some northern regions.</div></div><div><h3>Conclusion</h3><div>Meteorological data can be effectively utilized to predict the onset of RSV epidemics in Japan. The proposed DNN-based model offers a promising tool for supporting timely prophylactic measures, although further refinement and integration of additional factors are needed to improve generalizability.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101659"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240370","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":"Enhancing diabetes risk prediction: A comparative evaluation of bagging, boosting, and ensemble classifiers with SMOTE oversampling","authors":"Rabia Asif ,&nbsp;Darshana Upadhyay ,&nbsp;Marzia Zaman ,&nbsp;Srini Sampalli","doi":"10.1016/j.imu.2025.101661","DOIUrl":"10.1016/j.imu.2025.101661","url":null,"abstract":"<div><div>Diabetes is a major global health concern, with millions of individuals at risk of developing this chronic condition. Early prediction and intervention are essential for effective diabetes management. This study explores advanced machine learning techniques, specifically bagging, boosting, and ensemble methods to improve diabetes risk prediction. Using three diverse datasets, namely, the Centers for Disease Control and Prevention (CDC) Diabetes Health Indicators dataset, the Early Stage Diabetes Risk Prediction System (ESDRP) dataset, and the PIMA Indian Diabetes dataset are utilized to evaluate the adaptability and robustness of the proposed models. Our approach addresses critical gaps in existing research, including the handling of highly imbalanced datasets through the Synthetic Minority Over-sampling Technique (SMOTE), the necessity of feature selection, and the underutilization of the CDC dataset in diabetes studies. We find that applying SMOTE to the CDC dataset significantly enhances model performance, with the CATBoost algorithm achieving an accuracy of 91 %. For the ESRPS dataset, ensemble methods demonstrate even stronger results, achieving 98 % accuracy using the top five features. This study not only contributes to the development of more accurate predictive models for diabetes risk but also provides insights into enhancing the robustness of machine learning methods in healthcare.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101661"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535756","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":"Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations","authors":"Maryem Ouhmouk ,&nbsp;Shakuntala Baichoo ,&nbsp;Mounia Abik","doi":"10.1016/j.imu.2025.101679","DOIUrl":"10.1016/j.imu.2025.101679","url":null,"abstract":"<div><div>Artificial intelligence, particularly deep learning, is becoming increasingly prominent in multi-omics research, especially since traditional statistical models struggle to handle the complexity and high dimensionality of such data. By effectively combining different types of omics data, AI techniques can unveil hidden connections, detect biomarkers, and improve disease prediction through the integration of multi-omics layers and modalities, which can lead to significant advancements in precision medicine. In this review, we gathered published methods of deep learning-based multi-omics integration specialized in oncology since 2020. We concentrated exclusively on studies utilizing cancer omics data mainly sourced from The Cancer Genome Atlas (TCGA) database. As a result, we identified 32 articles that generally fulfilled the criteria. We studied their techniques and their ability to handle challenges in analyzing multi-omics data, particularly regarding missing data, dimensionality, and processing workflows. We also discuss how well these methods consider explainability, interpretability, and ethical aspects in developing solutions that treat private medical and sensitive information.</div><div>From the 32 studies, we can divide deep learning-based multi-omics integration methods into two types: non-generative and generative models. Non-generative approaches, such as feedforward neural networks (FFNs), graph convolutional networks (GCNs), and autoencoders, are designed to extract features and perform classification directly. On the other hand, generative methods such as variational autoencoders (VAEs), generative adversarial networks (GANs), and generative pretrained transformers (GPTs) focus on creating adaptable representations that can be shared across multiple modalities. These methods have advanced the handling of missing data and dimensionality, outperforming traditional approaches. However, most reviewed models remain at the proof-of-concept stage, with limited clinical validation or real-world deployment.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101679"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766898","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":"A longitudinal analysis of morphological shape variation of spleen in patients with fontan surgery","authors":"Varatharajan Nainamalai ,&nbsp;Håvard Bjørke Jenssen ,&nbsp;Mostafa Rezaeitaleshmahalleh ,&nbsp;Djinaud Prophete ,&nbsp;Jordan Gosnell ,&nbsp;Sarah Khan ,&nbsp;Marcus Haw ,&nbsp;Jingfeng Jiang ,&nbsp;Joseph Vettukattil","doi":"10.1016/j.imu.2025.101665","DOIUrl":"10.1016/j.imu.2025.101665","url":null,"abstract":"<div><h3>Background</h3><div>Splenic size serves as a surrogate biomarker for predicting portal vein hyper-tension and liver abnormalities in subjects with Fontan Associated Liver Disease (FALD). We analyze the long-term shape variation of the spleen in FALD subjects using morphological shape features of radiomic features.</div></div><div><h3>Methods</h3><div>We used 154 (84 from computed tomography and 70 from magnetic resonance) image volumes obtained from 36 individuals who underwent stage 3 Fontan procedure and 145 computed tomography images from controls to assess splenomegaly. To understand the splenomegaly, thirteen shape features of the spleen over three 10-year intervals, and variations between controls and FALD subjects were analyzed.</div></div><div><h3>Results</h3><div>The spleen enlargement was observed in all intervals of the post-surgery period. Also, a significant difference (level of significance <em>α</em> = 0.05, <em>p &lt; α</em>) was observed between the morphological shape features of controls and the Fontan Associated Liver Disease subjects.</div></div><div><h3>Conclusion</h3><div>Morphological shape features clearly distinguish between controls and subjects after Fontan stage 3 correction.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101665"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679522","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":"Quantum computing research in medical sciences","authors":"Saleh Alrashed ,&nbsp;Nasro Min-Allah","doi":"10.1016/j.imu.2024.101606","DOIUrl":"10.1016/j.imu.2024.101606","url":null,"abstract":"<div><div>With the emergence of ever-improving quantum computers, technology is making its way to revolutionize many fields, and the medical sector is no exception. Recent efforts have explored applications of quantum computing in areas such as drug discovery, patient privacy, and information security. It is expected that, with improved and stable quantum computing technologies, the medical sector will benefit significantly in many areas, including efficient patient care, reduced clinical trial durations, enhanced imaging technologies, and post-quantum cryptography, to name a few.</div><div>In this work, we highlight recent advancements in the medical sector driven by quantum computing, encompassing computation, optimization, security, machine learning, data processing, simulation, and healthcare perspectives. We also discuss the limitations of current technologies, and the challenges associated with the quantum computing revolution.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101606"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of walking-tracking apps and chronic medical conditions for adult students’ quality of life: A cross-sectional study from Saudi Arabia","authors":"Manal Almalki","doi":"10.1016/j.imu.2024.101610","DOIUrl":"10.1016/j.imu.2024.101610","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic significantly altered health behaviors, particularly among adult students in Saudi Arabia. The increased use of walking-tracking apps and the challenges faced by individuals with chronic medical conditions have influenced overall quality of life (QOL).</div></div><div><h3>Objective</h3><div>To assess the influence of having a medical condition and the use of walking-tracking apps on QOL among adult students in Saudi Arabia.</div></div><div><h3>Methods</h3><div>An online questionnaire was utilized in June 2024 to measure QOL using the WHOQOL-BREF scale, which covers physical health, psychological well-being, social relationships, and environmental health. Participants were grouped based on their use of walking-tracking apps and the presence of a chronic medical condition. Statistical analysis included independent t-tests, Pearson correlations, and chi-square tests to determine significant associations (p &lt; 0.05).</div></div><div><h3>Results</h3><div>The sample consisted of 412 participants. The chi-square test revealed a significant association between having a medical condition and using a walking-tracking app (p = 0.037), with individuals without medical conditions being more likely to use these apps. However, despite the high prevalence of app usage (65.3 %), no significant improvements in QOL were observed for app users across any of the QOL domains. Participants with medical conditions reported significantly higher QOL scores in all domains, particularly in psychological health (p &lt; 0.001) and social relationships (p = 0.001). Positive correlations were observed for factors like meaningful life, concentration, and access to healthcare among those with medical conditions.</div></div><div><h3>Conclusion</h3><div>Students with chronic medical conditions reported higher QOL whereas the use of walking-tracking apps had limited direct impact on their QOL. Future studies should explore factors that play a critical role in enhancing QOL beyond physical health and technology usage, including social support and the Saudi healthcare system.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101610"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}