Informatics in Medicine Unlocked最新文献

{"title":"A Lightweight Ultrasound Image Denoiser Using Parallel Attention Modules and Capsule Generative Adversarial Network","authors":"Anparasy Sivaanpu ,&nbsp;Kumaradevan Punithakumar ,&nbsp;Kokul Thanikasalam ,&nbsp;Michelle Noga ,&nbsp;Rui Zheng ,&nbsp;Dean Ta ,&nbsp;Edmond H.M. Lou ,&nbsp;Lawrence H. Le","doi":"10.1016/j.imu.2024.101569","DOIUrl":"10.1016/j.imu.2024.101569","url":null,"abstract":"<div><p>The quality of ultrasound (US) imaging has been constrained by its limited contrast and resolution, inherent speckle noise, and the presence of other artifacts. Existing traditional and deep learning-based US denoising approaches have many limitations, such as reliance on manual parameter configurations, poor performance for unknown noise levels, the requirement for a large number of training data, and high computational expense. To address these challenges, we propose a novel Generative Adversarial Network (GAN) based denoiser. Capsule networks are utilized in both the generator and discriminator of the proposed GAN to capture intricate sparse features with less complexity. In addition, bias components are removed from all neurons of the generator to handle the unknown noise levels. A parallel attention module is also included in the proposed model to further enhance denoising performance. The proposed approach is trained in a semi-supervised manner and can thus be trained with fewer labeled images. Experimental evaluation on publicly available HC18 and BUSI datasets showed that the proposed approach achieved state-of-the-art denoising performance, with PSNR values of 33.86 and 34.16, and SSIM indices of 0.91 and 0.90, respectively. Moreover, experiments showed that the proposed approach is lightweight and more than twice as fast as similar denoisers.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101569"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001254/pdfft?md5=2b794aebffd78d4c605d374f27873417&pid=1-s2.0-S2352914824001254-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006747","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":"WebQuorumChain: A web framework for quorum-based health care model learning","authors":"Xiyan Shao ,&nbsp;Anh Pham ,&nbsp;Tsung-Ting Kuo","doi":"10.1016/j.imu.2024.101590","DOIUrl":"10.1016/j.imu.2024.101590","url":null,"abstract":"<div><h3>Background</h3><div>Institutions interested in collaborative machine learning to enhance healthcare may be deterred by privacy concerns. Decentralized federated learning is a privacy-preserving and security-robust tool to promote cross-institutional learning, however, such frameworks require complex setups and advanced technical expertise. Here, we aim to improve their utilization by offering an intuitive, user-friendly, and secure system that integrates both front-end and back-end functionalities.</div></div><div><h3>Method</h3><div>We develop WebQuorumChain, an integrated system built upon the QuorumChain schema. We test the system on a 2-site network using two publicly available health datasets and measure the average vertical and horizontal-ensemble AUCs per dataset across 30 trials, as well as the average execution time of the system.</div></div><div><h3>Results</h3><div>Our system achieved consistently high AUCs for each dataset (0.94–0.96), with reasonable total execution times ranging from 5 to 20 min, inclusive of modeling and all other system overheads. The front-end displays event logs generated from back-end layers in real time, in sync with the progress of the underlying algorithm.</div></div><div><h3>Conclusions</h3><div>We develop a web-based system that supplies users with visual tools to configure the federated learning network, manage training sessions, and inspect the learning process. WebQuorumChain helps schedule and monitor low-level processes without violating the fundamental security promises of cross-institutional decentralized machine learning. The system also maintains predictive accuracy and runtime efficiency in the presence of additional layers. WebQuorumChain will help promote meaningful collaboration among healthcare institutions, who can retain full control of their data privacy while contributing to data-driven discoveries.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101590"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442194","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":"Enhancing COVID-19 vaccination and medication distribution routing strategies in rural regions of Morocco: A comparative metaheuristics analysis","authors":"Toufik Mzili ,&nbsp;Ilyass Mzili ,&nbsp;Mohammed Essaid Riffi ,&nbsp;Mohamed Kurdi ,&nbsp;Ali Hasan Ali ,&nbsp;Dragan Pamucar ,&nbsp;Laith Abualigah","doi":"10.1016/j.imu.2024.101467","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101467","url":null,"abstract":"<div><p>The optimization of the vaccination campaign and medication distribution in rural regions of Morocco conducted by the Ministry of Health can be significantly improved by employing metaheuristic algorithms in conjunction with a tour planning system. This research proposes the utilization of six metaheuristic algorithms: genetic algorithm, rat swarm optimization, whale optimization, spotted hyena optimizer, penguins search optimization, and particle swarm optimization, to determine the most efficient routes for equipped trucks carrying vaccines and medications. These algorithms consider critical field constraints, such as operating hours of vaccination centers, vaccine availability, and distances between centers while minimizing the overall journey duration. In addition, a comprehensive tour planning system is integrated into the optimization framework accounting for transportation costs such as fuel expenses and truck maintenance costs. By incorporating these factors, the Ministry of Health aims to achieve the maximum efficiency while minimizing the financial burden associated with the vaccination campaign in rural areas. The integration of metaheuristics and the tour planning system presents a robust and data-driven solution for the Ministry of Health to enhance the effectiveness of their vaccination and medication distribution campaigns in rural regions of Morocco. This approach not only minimizes costs but also improves overall efficiency by ensuring timely access to vaccines and medications for the rural population. The findings of this research contribute to the growing body of knowledge in the field of healthcare logistics optimization and provide valuable insights for policymakers and practitioners involved in similar campaigns worldwide.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101467"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000236/pdfft?md5=27925e447cac8f7a48f5b9cd88ba08e1&pid=1-s2.0-S2352914824000236-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188025","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":"Enhancing patient treatment through automation: The development of an efficient scribe and prescribe system","authors":"Muhammad Nazrul Islam,&nbsp;Sazia Tabasum Mim,&nbsp;Tanha Tasfia,&nbsp;Md Mushfique Hossain","doi":"10.1016/j.imu.2024.101456","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101456","url":null,"abstract":"<div><p>Making scribes and prescriptions are the primary activities for a health professional to serve the patients. Although in most of the cases these tasks are pursued manually, a few studies focused on developing digital scribe generation and prescription systems. Moreover, to enhance the effectiveness and adoption of such digital scribe and prescription systems, these systems should be intelligent and useable enough. Therefore, the objective of this research is to understand the user requirements for developing an automated scribes and intelligent prescribing system for health professionals and to develop the automated scribes and intelligent prescribing system based on the revealed users' requirements. And finally, to evaluate the performance of the proposed system. To attain these objectives, a requirement elicitation study was carried out following the semi-structured interviews to reveal the user requirements for an intelligent scribe and prescription system. The study proposed an automated digital scribe that can record medical information adopting the LSTM model; and also be able to generate automated prescriptions based on a doctor's voice command. Finally, the system was evaluated through an empirical study where participants (doctors) were asked to generate scribes and provide prescriptions manually and also by using the proposed system. The study found that the scribes and prescriptions generated using the proposed system are highly similar to the scribes (87.5 %) and prescriptions (96.2 %) generated manually. Analysis of the evaluation results also showed that the system provides a user-friendly, easy-to-use, intuitive, and interactive interface to facilitate the doctors and clinicians.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101456"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000121/pdfft?md5=7d641da8561428b2107c12dd1510ed49&pid=1-s2.0-S2352914824000121-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139709079","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":"A bibliometric analysis of global trends in internet addiction publications from 1996 to 2022","authors":"Suhaib Muflih ,&nbsp;Sayer I. Al-Azzam ,&nbsp;Karem H. Alzoubi ,&nbsp;Reema Karasneh ,&nbsp;Sahar Hawamdeh ,&nbsp;Waleed M. Sweileh","doi":"10.1016/j.imu.2024.101484","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101484","url":null,"abstract":"<div><h3>Background</h3><p>As the use of technology has increased, a set of problematic psychological behaviors associated with internet use has evolved. This bibliometric research aims to discover and analyze internet addiction (IA) articles trends from 1996 to 2022.</p></div><div><h3>Methods</h3><p>This research is based on a bibliometric examination of internet addiction papers published between 1996 and 2022. The Scopus database was utilized to extract the needed documents, examine citation patterns and publication growth, and identify prolific authors and institutions.</p></div><div><h3>Results</h3><p>There were 9692 publications on internet addiction from 1996 to 2022, with an average of 359 documents each year. A total of 21906 authors contributed to the literature, with the majority of publications (86.9%) being multi-authored. The United States (US) ranked first in terms of volume of publications (18.8%, n = 1819), followed by China (12.3%, n = 1194), the United Kingdom (8.3%, n = 808), and Turkey (6.2%, n = 602). However, the majority of the productive institutions were located in East Asia.</p></div><div><h3>Conclusion</h3><p>There is a substantial body of literature on internet addiction, with numerous worldwide collaborations. As IA will be a lingering problem with the increased digitization of all sectors, future research should focus on emerging topics such as social media and gaming addiction. Internet addiction among adolescents in particular is a key research area.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101484"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000406/pdfft?md5=e7d08583cd79b1c49478d7a7fb1298f5&pid=1-s2.0-S2352914824000406-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328559","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":"A novel brain EEG clustering based on Minkowski distance to improve intelligent epilepsy diagnosis","authors":"Dhiah Al-Shammary ,&nbsp;Ekram Hakem ,&nbsp;Ahmed M. Mahdi ,&nbsp;Ayman Ibaida ,&nbsp;Khandakar Ahmed","doi":"10.1016/j.imu.2024.101492","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101492","url":null,"abstract":"<div><p>This paper introduces a novel clustering approach based on Minkowski's mathematical similarity to improve EEG feature selection for classification and have efficient Particle Swarm Optimization (PSO) in the context of machine learning. Given the intricacy of high-dimensional medical datasets, feature selection plays a critical role in preventing disease and promoting public health. By employing Minkowski clustering, the objective is to group dataset records into two clusters exhibiting high feature coherence, thereby improving accuracy by applying optimization techniques like PSO to select the most optimal features. Furthermore, the proposed model can be extended to intelligent datasets, including EEG and others. As fewer features are needed for precise categorization, intelligent feature selection is an advanced step of machine learning. This paper investigates the key factors influencing feature selection in the EEG Bonn University dataset. The proposed system is compared against various optimization and feature selection methods, demonstrating superior performance in analyzing and classifying EEG signals based on accuracy measures. The experimental results have confirmed the effectiveness of the suggested model as a valuable tool for EEG data classification, achieving up to 100% accuracy. The outcomes of this research have the potential to benefit medical experts in related specialties by streamlining the process of identifying and diagnosing brain disorders. Technically, the machine learning algorithms RF, KNN, SVM, NB, and DT are employed to classify the selected features.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101492"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000480/pdfft?md5=807f932a500c248b634203967b394485&pid=1-s2.0-S2352914824000480-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140344681","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":"Ensemble-based feature engineering mechanism to decode imagined speech from brain signals","authors":"Uzair Shah,&nbsp;Mahmood Alzubaidi,&nbsp;Farida Mohsen,&nbsp;Tanvir Alam,&nbsp;Mowafa Househ","doi":"10.1016/j.imu.2024.101491","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101491","url":null,"abstract":"<div><p>Speech impairments, resulting from brain injuries, mental disorders, or vocal abuse, substantially affect an individual’s quality of life and can lead to social isolation. Brain–Computer Interfaces (BCIs), particularly those based on EEG, offer a promising support mechanism by harnessing brain signals. Owing to their clinical efficacy, cost-effective EEG devices, and expanding applications in the medical and social spheres, their usage has surged. This study introduces an ensemble-based feature engineering mechanism to pinpoint the optimal brain rhythm, channel subset, and feature set for accurately predicting imagined words from EEG signals via machine learning models. Leveraging the 2020 International BCI competition dataset, we employed bandpass filtering, channel wrapping, and ranking methods were applied to discern suitable brain rhythms and features associated with imagined speech. Subsequent application of kernel-based principal component analysis enabled us to compress the feature space dimensionality. We then trained various machine learning models, among which the kNN model excelled, achieving an average accuracy of 73% in a 10-fold cross-validation scheme ,surpassing 18% higher than the existing literature. The Gamma rhythm was identified as the most predictive of imagined speech from EEG brain signals. These advancements herald a new era of more precise and effective BCIs, poised to significantly improve the lives of those with speech impairments.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101491"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000479/pdfft?md5=b7426fcd2dc0c80cde42c9585f90d202&pid=1-s2.0-S2352914824000479-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140533647","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":"Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study","authors":"Aditi Chopra ,&nbsp;Rohini R. Rao ,&nbsp;Shobha U. Kamath ,&nbsp;Sanjana Akhila Arun ,&nbsp;Laasya Shettigar","doi":"10.1016/j.imu.2024.101523","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101523","url":null,"abstract":"<div><h3>Introduction</h3><p>There is a need for designing non-invasive methods to predict blood glucose levels to ensure timely diagnosis of Diabetes Mellitus. Needle anxiety and bleeding disorders preclude many from undertaking blood tests.</p></div><div><h3>Objectives</h3><p>The primary objective of this study was to assess if biomarkers like saliva can be used to estimate blood glucose levels. The second objective was to develop and evaluate Machine Learning (ML) models to predict blood glucose levels based on salivary glucose and associated features. An insight into the patient's features, which was important for predicting blood glucose levels, was also required.</p></div><div><h3>Methods</h3><p>A cross-sectional study was conducted, and blood and saliva samples, along with patient-related data, were collected from healthy and diabetic patients. ML techniques were applied to the data to develop a tool for predicting blood glucose levels using patient features. The prediction intervals were computed, clinical accuracy was assessed, and important features for the prediction were identified.</p></div><div><h3>Results</h3><p>The Random Forest Regressor Model, with features identified using the wrapper method, was selected as the best, with an average RMSE of 43.28. The prediction intervals were computed for point estimate, MAE = 23.821, and coverage was 100 percent, the clinical accuracy was compared with that of glucometers and continuous monitoring systems. All predicted values are in Zones A and B of the Clarke error grid, and the bias was 6.41. The most important feature for predicting blood glucose level is salivary glucose level, followed by known risk factors like Family History, BMI, etc. The study found that salivary glucose levels are insufficient to classify blood glucose levels as high or normal.</p></div><div><h3>Conclusion</h3><p>The study concluded that salivary glucose with associated patient features could be a potential non-invasive biomarker for predicting blood glucose levels in patients. The developed ML model could be deployed in a device that inputs patient features, analyzes salivary glucose, and can monitor blood glucose levels in a non-invasive manner. Further research is needed to validate the findings of this study and develop a proof of concept.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"48 ","pages":"Article 101523"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000790/pdfft?md5=4a9c0bd5b5ca8b62fe997281e3cad676&pid=1-s2.0-S2352914824000790-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077743","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":"MONTRA2: A web platform for profiling distributed databases in the health domain","authors":"João Rafael Almeida ,&nbsp;José Luís Oliveira","doi":"10.1016/j.imu.2024.101447","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101447","url":null,"abstract":"<div><h3>Background:</h3><p>Data catalogues are used in multiple domains to provide an overview of databases’ characteristics without releasing the actual data. Despite the existence of several web-based catalogues, they do not always meet the needs of certain domains. In the healthcare field, they need to give multiple and iterative views to the data, from high-level metadata up to low-level samples or patient data. This approach is critical to help researchers find relevant datasets for their studies.</p></div><div><h3>Methods:</h3><p>In this paper, we present MONTRA2, a web platform for profiling distributed databases. The users’ requirements were designed in the context of the EHDEN European project, in close collaboration with medical researchers, data owners, and pharmaceutical companies, leading to a rich set of functionalities to support databases and cohorts discovery. The platform was developed with a modular architecture which simplifies the integration of internal and external services.</p></div><div><h3>Results:</h3><p>MONTRA2 is successfully being used in several European projects and research initiatives, focused on the dissemination and sharing of biomedical databases. In this paper, we present three health data catalogues that were built upon the core of this framework. MONTRA2 is publicly available under the MIT license at <span>https://github.com/bioinformatics-ua/montra2</span><svg><path></path></svg>.</p></div><div><h3>Conclusions:</h3><p>The execution of federated studies on a large scale and involving multiple centres is only possible if adequate tools for data management and discovery are available. By providing tools for study management, database characterisation and publishing, among others, MONTRA2 simplifies the process of setting up a workspace for a community to expose the characteristics of datasets and provide multiple strategies for data analysis.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101447"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000030/pdfft?md5=321e094f8f4fd42cb0d7c13a2baecca8&pid=1-s2.0-S2352914824000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139503795","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":"Deep learning for medical image segmentation: State-of-the-art advancements and challenges","authors":"Md. Eshmam Rayed ,&nbsp;S.M. Sajibul Islam ,&nbsp;Sadia Islam Niha ,&nbsp;Jamin Rahman Jim ,&nbsp;Md Mohsin Kabir ,&nbsp;M.F. Mridha","doi":"10.1016/j.imu.2024.101504","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101504","url":null,"abstract":"<div><p>Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in higher layers and basic edge identification in lower layers, has markedly improved the quality and accuracy of image segmentation. Consequently, DL using picture segmentation has become commonplace, video analysis, facial recognition, etc. Grasping the applications, algorithms, current performance, and challenges are crucial for advancing DL-based medical image segmentation. However, there is a lack of studies delving into the latest state-of-the-art developments in this field. Therefore, this survey aimed to thoroughly explore the most recent applications of DL-based medical image segmentation, encompassing an in-depth analysis of various commonly used datasets, pre-processing techniques and DL algorithms. This study also investigated the state-of-the-art advancement done in DL-based medical image segmentation by analyzing their results and experimental details. Finally, this study discussed the challenges and future research directions of DL-based medical image segmentation. Overall, this survey provides a comprehensive insight into DL-based medical image segmentation by covering its application domains, model exploration, analysis of state-of-the-art results, challenges, and research directions—a valuable resource for multidisciplinary studies.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101504"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000601/pdfft?md5=fe81e44fe1f75c7162c9d0f2a8875844&pid=1-s2.0-S2352914824000601-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140647066","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}