Informatics in Medicine Unlocked最新文献

{"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":"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":"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":"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}

{"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":"Corrigendum to “Early detection of coronary heart disease using ensemble techniques” [Inform Med Unlocked 26 (2021) 100655]","authors":"Vardhan Shorewala ,&nbsp;Shivam Shorewala","doi":"10.1016/j.imu.2024.101598","DOIUrl":"10.1016/j.imu.2024.101598","url":null,"abstract":"","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101598"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178836","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":"Large language models aided patient progression documentation according to the ICD standard","authors":"Nuria Lebeña ,&nbsp;Arantza Casillas ,&nbsp;Alicia Pérez","doi":"10.1016/j.imu.2025.101637","DOIUrl":"10.1016/j.imu.2025.101637","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Healthcare documentation processing is becoming more and more efficient and effective as a result of advances in machine learning and natural language processing (NLP). One challenge in clinical practice is the early detection of future patient potential diagnoses, which is crucial for preventive medicine. Estimating the potential future diagnoses, helps to speed up the management of Electronic Health Records (EHRs) and opens a path towards clinical prevention. It is a challenging task, as there are thousands of possible diseases, and, in general, there is limited data available to train systems due to privacy concerns.</div><div>The objective of his study is to infer future probable diagnoses given patients diagnosis history. In previous works, this task has been carried out using structured data, such as, ICD-coded diagnoses, overlooking unstructured textual information in EHRs. Unlike traditional methods, this study aims to enhance next-diagnosis prediction by integrating patient diagnosis information codified according to the International Classification of Diseases (ICD) with unstructured clinical text.</div></div><div><h3>Methods:</h3><div>We propose a multi-faceted model that integrates structured ICD-encoded patient histories with unstructured EHR text for future diagnosis prediction. Our approach consists of (1) a sequential model trained on structured diagnosis timelines, (2) a Clinical Longformer-based model trained on unstructured EHRs, and (3) an ensemble strategy to combine predictions from both components.</div></div><div><h3>Results:</h3><div>Our proposed ensemble strategy significantly outperforms current state-of-the-art approaches in predicting future diagnoses, achieving a Precision@5 of 72.34% and a Precision@20 of 77.49%. Additionally, it showed high robustness and reliability across different demographic groups and a varying scope of medical history.</div></div><div><h3>Conclusion:</h3><div>This research demonstrates that the integration of structured ICD diagnoses timelines with unstructured EHRs achieves improved results compared to just using structured diagnosis timelines. Notably, the proposed model also maintained high accuracy even with a short-term history of diagnoses.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101637"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739798","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":"Towards trustworthy AI-driven leukemia diagnosis: A hybrid Hierarchical Federated Learning and explainable AI framework","authors":"Khadija Pervez ,&nbsp;Syed Irfan Sohail ,&nbsp;Faiza Parwez ,&nbsp;Muhammad Abdullah Zia","doi":"10.1016/j.imu.2025.101618","DOIUrl":"10.1016/j.imu.2025.101618","url":null,"abstract":"<div><div>Accurate detection and classification of microscopic cells from acute lymphoblastic leukemia remain challenging due to the difficulty of differentiating between cancerous and healthy cells. This paper proposes a novel approach to identify and categorize acute lymphoblastic leukemia that uses explainable artificial intelligence and federated learning to train models across multiple institutions while keeping patient information decentralized and encrypted. The framework trains EfficientNetB3 for the classification of leukemia cells and incorporates explainability techniques to make decisions of the underlying model transparent and interpretable. The framework employs a hierarchical federated learning approach that allows distributed learning across clinical centers, ensuring that sensitive patient data remain localized. Explainability techniques such as saliency maps, occlusion sensitivity, and randomized input sampling for explanation with relevant evaluation scores are integrated in the framework to provide visual and textual explanations of model’s predictions to enhance interpretability. The experiments were carried out on a publicly available dataset consisting of 15,135 microscopic images. The performance of the proposed model was benchmarked against traditional centralized models and classical federated learning techniques. The proposed model demonstrated a 2.5% improvement in accuracy (96.5%) and a 5.4% increase in F1-score (94.4%) compared to baseline models. Hierarchical federated learning reduced communication costs by 15% while maintaining data privacy. The integration of explainable artificial intelligence improved the transparency of model decisions, with a high area under the ROC curve (AUC) of 0.98 for the classification of leukemia cells. These results suggest that the proposed framework offers a robust solution for intelligent systems for medical diagnostics and can also be extended to other medical imaging tasks.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101618"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103459","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":"Advancing the Sensitivity Frontier in digital contact tracing: Comparative analysis of proposed methods toward maximized utility","authors":"Junko Ami ,&nbsp;Yanbo Pang ,&nbsp;Hiroshi Masui ,&nbsp;Takashi Okumura ,&nbsp;Yoshihide Sekimoto","doi":"10.1016/j.imu.2025.101622","DOIUrl":"10.1016/j.imu.2025.101622","url":null,"abstract":"<div><div>During the COVID-19 pandemic, many countries adopted Digital Contact Tracing (DCT) technology to control infections. However, the widely-used Bluetooth Low Energy (BLE)-based DCT requires both the infected individual and the contact to have the application activated to detect exposure. Forcing citizens to install the DCT application could compromise their privacy. Therefore, to make DCT a truly usable tool, it is crucial to develop a DCT system that possesses high sensitivity, without depending on the application usage rate.</div><div>The Computation of Infection Risk via Confidential Locational Entries (CIRCLE) is a DCT method that utilizes connection logs from mobile phone base stations, theoretically offering much higher sensitivity than BLE-based DCT. However, its real performance has not been proven, and thus, this paper estimates the sensitivity and specificity of both BLE-based DCT and CIRCLE in a comparative setting. The estimation combines simulated movement patterns of residents with real-world data from app usage in Japan, utilizing both simulation and numerical modeling, with missing data supplemented through sensitivity analysis.</div><div>The sensitivity of BLE-based DCT is severely limited by the application’s usage rate, with an estimated baseline of just 10.9%, and even under highly optimistic assumptions, it only reaches 27.0%. In contrast, CIRCLE demonstrated a significantly higher sensitivity of 85.6%, greatly surpassing BLE-based DCT. The specificity of CIRCLE, though, decreased as the number of infected individuals increased, dropping to less than half of BLE-based DCT’s specificity during widespread infection. The BLE-based DCT used during the pandemic suffers from low sensitivity. While CIRCLE has specificity challenges, it provides exceptionally high sensitivity. Integrating these methods could redefine the design of digital contact tracing, leading to better utility for future infection control.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101622"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508494","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":"Customer relationship management systems in clinical laboratories: A systematic review","authors":"Jonel Bation ,&nbsp;Mary Ann Jaro ,&nbsp;Lheyniel Jane Nery ,&nbsp;Mudjahidin Mudjahidin ,&nbsp;Andre Parvian Aristio ,&nbsp;Eddie Bouy Palad ,&nbsp;Jason Chavez ,&nbsp;Lemuel Clark Velasco","doi":"10.1016/j.imu.2025.101628","DOIUrl":"10.1016/j.imu.2025.101628","url":null,"abstract":"<div><div>The implementation of Customer Relationship Management (CRM) Systems in clinical laboratories is crucial in improving customer relationships, service quality, and operational efficiency that aligns with a patient-centric care model. This study utilizes the PRISMA guidelines in reviewing and synthesizing 26 journal articles using the People, Process, Technology (PPT) framework to analyze the roles of people involved in clinical settings, the processes by which laboratory services were delivered, and the technological considerations enhancing patient care. Results revealed that the successful implementation of CRM systems in clinical laboratories depends on the aligned efforts of both developers and end-users. Subsequently, marketing processes and customer service were then found out to be crucial for the successful utilization of CRM systems in clinical laboratories. The features and the system integration techniques of CRM systems were found out to be vital in developing efficient operations, enhancing data analysis, and extending accessibility. The research gap analysis, on the one hand, shows that the effectiveness of CRM systems on the patients, the lack of qualitative methods, and the development of corrective actions to increase patient satisfaction are relevant areas of research concerns to optimize the effectiveness of implementing different CRM systems.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101628"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487851","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}