BioMedInformatics最新文献

{"title":"A Smartphone-Based Algorithm for L Test Subtask Segmentation","authors":"Alexis L. McCreath Frangakis, Edward D. Lemaire, Natalie Baddour","doi":"10.3390/biomedinformatics4020069","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020069","url":null,"abstract":"Background: Subtask segmentation can provide useful information from clinical tests, allowing clinicians to better assess a patient’s mobility status. A new smartphone-based algorithm was developed to segment the L Test of functional mobility into stand-up, sit-down, and turn subtasks. Methods: Twenty-one able-bodied participants each completed five L Test trials, with a smartphone attached to their posterior pelvis. The smartphone used a custom-designed application that collected linear acceleration, gyroscope, and magnetometer data, which were then put into a threshold-based algorithm for subtask segmentation. Results: The algorithm produced good results (>97% accuracy, >98% specificity, >74% sensitivity) for all subtasks. Conclusions: These results were a substantial improvement compared with previously published results for the L Test, as well as similar functional mobility tests. This smartphone-based approach is an accessible method for providing useful metrics from the L Test that can lead to better clinical decision-making.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":" 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140991667","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":"Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit","authors":"Dimitrios Rallis, Maria S Baltogianni, K. Kapetaniou, V. Giapros","doi":"10.3390/biomedinformatics4020067","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020067","url":null,"abstract":"Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996877","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":"Selection of the Discriming Feature Using the BEMD’s BIMF for Classification of Breast Cancer Mammography Image","authors":"Fatima Ghazi, A. Benkuider, F. Ayoub, Khalil Ibrahimi","doi":"10.3390/biomedinformatics4020066","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020066","url":null,"abstract":"Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994928","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":"Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models","authors":"Godfrey A. Mills, D. Dey, Mohammed Kassim, Aminu Yiwere, Kenneth Broni","doi":"10.3390/biomedinformatics4020065","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020065","url":null,"abstract":"Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997988","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":"An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors","authors":"S. Iarlori, D. Perpetuini, M. Tritto, D. Cardone, Alessandro Tiberio, Manish Chinthakindi, C. Filippini, L. Cavanini, A. Freddi, F. Ferracuti, A. Merla, Andrea Monteriù","doi":"10.3390/biomedinformatics4020064","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020064","url":null,"abstract":"Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"70 s290","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002290","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":"Recent Advances in Large Language Models for Healthcare","authors":"Khalid Nassiri, Moulay A. Akhloufi","doi":"10.3390/biomedinformatics4020062","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020062","url":null,"abstract":"Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and generation that could indeed be put to good use in the medical field. We also present the main architectures of these models, such as GPT, Bloom, or LLaMA, composed of billions of parameters. We then examine recent trends in the medical datasets used to train these models. We classify them according to different criteria, such as size, source, or subject (patient records, scientific articles, etc.). We mention that LLMs could help improve patient care, accelerate medical research, and optimize the efficiency of healthcare systems such as assisted diagnosis. We also highlight several technical and ethical issues that need to be resolved before LLMs can be used extensively in the medical field. Consequently, we propose a discussion of the capabilities offered by new generations of linguistic models and their limitations when deployed in a domain such as healthcare.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"360 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698148","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":"Investigating the Effectiveness of an IMU Portable Gait Analysis Device: An Application for Parkinson’s Disease Management","authors":"Nikos Tsotsolas, Eleni Koutsouraki, Aspasia Antonakaki, S. Pizanias, Marios Kounelis, Dimitrios D. Piromalis, Dimitrios P. Kolovos, Christos Kokkotis, Themistoklis Tsatalas, George Bellis, D. Tsaopoulos, Paris Papaggelos, George Sidiropoulos, Giannis Giakas","doi":"10.3390/biomedinformatics4020061","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020061","url":null,"abstract":"As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth analysis of the gait characteristics, while the project CPWATCHER aims to assess the quality of hand movement in cerebral palsy patients. The device was mainly developed to serve the first project with additional offline processing, including machine learning algorithms that could potentially be used for the second aim. A key feature of the device is its small size (36 mm × 46 mm × 16 mm, weight: 14 g), which was designed to meet specific requirements in terms of device consumption restrictions due to the small size of the battery and the need for autonomous operation for more than ten hours. This research work describes, on the one hand, the new device with an emphasis on its functions, and on the other hand, its connection with a web platform for reading and processing data from the devices placed on patients’ feet to record the gait characteristics of patients on a continuous basis.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"152 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719922","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 Comprehensive Analysis of Trapezius Muscle EMG Activity in Relation to Stress and Meditation","authors":"Mohammad H. Ahmed, Michael Grillo, Amirtahà Taebi, Mehmet Kaya, Peshala Thibbotuwawa Gamage","doi":"10.3390/biomedinformatics4020058","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020058","url":null,"abstract":"Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. Conclusion: This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140727829","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":"Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology","authors":"Aravinthan Sankar, Kunal Chaturvedi, Al-Akhir Nayan, M. H. Hesamian, Ali Braytee, Mukesh Prasad","doi":"10.3390/biomedinformatics4020059","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020059","url":null,"abstract":"Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722361","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":"Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services","authors":"Jonas Bambi, Yudi Santoso, Ken Moselle, Stan Robertson, Abraham Rudnick, Ernie Chang, Alex Kuo","doi":"10.3390/biomedinformatics4020060","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020060","url":null,"abstract":"Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients’ engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients’ movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140727396","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}