Informatics in Medicine Unlocked最新文献

{"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}

{"title":"On the role of vaccination, health education, and hygiene compliance in the elimination and control of Hepatitis A Virus: An optimal control approach","authors":"Stephen Edward","doi":"10.1016/j.imu.2024.101501","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101501","url":null,"abstract":"<div><p>A deterministic mathematical model for Hepatitis A infection is established and subsequently examined to optimize control strategies. The model incorporates three time-dependent controls: vaccination, health education, and hygiene compliance, focusing on mitigating disease transmission in the community. The derivation of the basic reproduction number was conducted using the Next-Generation Matrix (NGM) technique, which was subsequently utilized to analyze the stability of the equilibria of the model. The optimal control problem is established and analyzed using Pontryagin’s Maximum principle. The numerical simulation of the optimal control problem is achieved via Runge–Kutta fourth-order schemes (forward and backward sweeps). The numerical findings demonstrate a significant reduction in Hepatitis A cases by implementing at least one control measure. Besides that, it has been established that coupling vaccination, health education and hygiene compliance results in the lowest number of cases, making it an optimal option for eradicating Hepatitis A in the community. However, applying this strategy could be more costlier. As such, the cost-effective analysis was carried out via an incremental cost-effectiveness ratio approach to ascertain the most cost-effective strategy. The findings confirmed that the vaccination strategy was the most cost-effective approach among the strategies under consideration because it offers the minimum number of cases at the minimum cost. This approach is particularly applicable in situations with constrained resources, a circumstance prevalent in many developing nations.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101501"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000571/pdfft?md5=c0856ec03e896e00a88b477eb75ef868&pid=1-s2.0-S2352914824000571-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140620760","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":"Multi-ensemble machine learning framework for omics data integration: A case study using breast cancer samples","authors":"Kunal Tembhare,&nbsp;Tina Sharma,&nbsp;Sunitha M. Kasibhatla,&nbsp;Archana Achalere,&nbsp;Rajendra Joshi","doi":"10.1016/j.imu.2024.101507","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101507","url":null,"abstract":"<div><p>Integration of voluminous omics data aids to unravel biological complexities associated with different disease phenotypes. Machine learning (ML) approaches provide insightful techniques for systematic multi-omics data integration. In this study, survival prediction of breast cancer patients was undertaken using omics data of 302 female patients from The Cancer Genome Atlas (TCGA). The data included gene expression, miRNA expression, DNA methylation and copy number variation. Three computational multi-ensemble ML pipelines were tested using Support Vector Machine (SVM), Random Forest (RF) and Partial Least Squares-Discriminant Analysis (PLS-DA) algorithms. To overcome the limitations associated with univariate feature selection criteria, the ML pipelines were built along with latent factors obtained by multivariate dimension reduction method. This facilitated investigation of background genetic networks and identification of potential hub genes. Analysis of the results obtained revealed that SVM with PLS-DA method (integrated with gene expression, DNA methylation, and miRNA expression modalities) was the best-performing model with an Area Under Curve (AUC) of 89% and an accuracy of 83% for survival prediction. This study not only corroborated previously reported breast cancer-specific prognostic biomarkers but also predicted additional potential biomarkers. The work demonstrates the effective use of a multi-ensemble ML model with efficient feature selection methods as a robust protocol for cancer genotype to phenotype correlation.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101507"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000637/pdfft?md5=d0bc5069357cca8ad1607f59098d6c54&pid=1-s2.0-S2352914824000637-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638122","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":"Designing and evaluating a web-based training program for medical record documentation: Insights from a developing country experience","authors":"Navisa Abbasi ,&nbsp;Mohamad Jebraeily ,&nbsp;Shahsanam Gheibi ,&nbsp;Yousef Mohammadpoor","doi":"10.1016/j.imu.2024.101599","DOIUrl":"10.1016/j.imu.2024.101599","url":null,"abstract":"<div><h3>Background</h3><div>The quality of medical documentation is crucial for enhancing patient care, as accurate records reduce medical errors and improve patient safety. Given the pivotal role of medical records in delivering high-quality healthcare services, effective training in documentation skills is essential. Whence, this study aimed to design and evaluate a web-based training program focused on medical record documentation, specifically for medical students in Iran (West Azerbaijan province, Urmia), but can be easily adapted to other pertinent cases.</div></div><div><h3>Method</h3><div>This semi-experimental study was conducted in 2023 and comprised three main phases: pre-intervention, intervention, and post-intervention. In the first phase, an online questionnaire assessing knowledge, attitudes, and performance was developed and integrated into the web-based education program. During the second phase, multimedia electronic content was created and made accessible to students for two months. In the final phase, the same online questionnaire was administered to the students again. The study involved 114 medical students from Urmia University of Medical Sciences. Among the 114 medical students (61 externs and 53 interns), 53.4 % were male, and 46.6 % were female. The data were analyzed using SPSS 16 software.</div></div><div><h3>Results</h3><div>Following the intervention, students’ knowledge scores are seen increase from 76.50 to 86.30, attitudes improved from 79.33 to 85, and performance enhanced from 74.92 to 81.40. Further statistical analysis reveals that the web-based training significantly impacted the knowledge, attitudes, and performance of the medical students regarding documentation, with a p-value less than 0.05.</div></div><div><h3>Conclusion</h3><div>The findings of this specific study indicate that web-based education, supplemented with multimedia content, has led to significant improvements in the knowledge, attitudes, and performance of medical students in medical record documentation. While these positive outcomes suggest that the course characteristics played an important role, further investigation is no doubt needed to establish a direct causal relationship. Ongoing studies are surely recommendable. Nonetheless, implementing such educational approaches appears to be an essential ingredient for enhancing the documentation skills of future healthcare professionals. The study may open educational perspectives and inspire further ad hoc research in nearby domains making use of complex documentation.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"51 ","pages":"Article 101599"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700567","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 cancer care: How artificial intelligence is transforming oncology pharmacy","authors":"","doi":"10.1016/j.imu.2024.101529","DOIUrl":"10.1016/j.imu.2024.101529","url":null,"abstract":"<div><p>This article explores the transformative impact of Artificial Intelligence (AI) in oncology pharmacy. Oncology pharmacists, traditionally pivotal to cancer management, are now navigating a landscape revolutionized by AI advancements, including machine learning and predictive analytics. Their role has expanded beyond conventional boundaries to encompass data-driven decision-making, AI-guided clinical support, and comprehensive patient counseling on AI-based treatment protocols. This evolution necessitates an augmented skill set encompassing technological proficiency, data interpretation, and ethical considerations in patient care. Despite the promise of AI in personalizing treatment and enhancing patient outcomes, challenges persist, including data privacy concerns, integration complexities, and ethical quandaries. Oncology pharmacy is transitioning to a more patient-focused practice, driven by continuous innovation and adaptation to AI technologies. This shift underscores the critical role of oncology pharmacists in shaping an AI-integrated future in healthcare, pivotal for advancing cancer treatment and improving patient care.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101529"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000856/pdfft?md5=5ae6f7a34e8981fbb4f0fed62e161cc2&pid=1-s2.0-S2352914824000856-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141411256","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":"Deciphering the impact of diversity in CNN-based ensembles on overcoming data imbalance and scarcity in medical datasets: A case study on diabetic retinopathy","authors":"Inamullah ,&nbsp;Saima Hassan ,&nbsp;Samir Brahim Belhaouari ,&nbsp;Ibrar Amin","doi":"10.1016/j.imu.2024.101557","DOIUrl":"10.1016/j.imu.2024.101557","url":null,"abstract":"<div><p>Early detection of diabetic retinopathy (DR) is critical in preventing vision loss. However, building accurate Artificial intelligence (AI) models for multiple classes, including early-stage (Class-1) detection, is challenging due to limited and imbalanced medical datasets. The availability of such datasets is restricted due to ethical and privacy concerns. Traditional ensemble models also struggle with raw medical images, further complicating the issue as they require structured data. This study presents a novel deep learning-based ensemble model (EM) designed for multiple and specifically for precise early stage (Class 1) DR classification. The EM uses eight diverse Convolutional Neural Networks (CNNs) with carefully crafted strategies to enhance diversity. Data augmentation and generation techniques address imbalanced data through data diversity, while parameter and architectural diver-sity within CNNs-based EM maximize predictive performance. Evaluation on the publicly available Kaggle APTOS DR dataset demonstrates significant improvement over individual models and existing approaches. The proposed EM achieves multi-class accuracy (93.00 %), precision (93.00 %), sensitivity (98.00 %), and specificity (99.00 %). This research highlights the effectiveness of diversified CNNs ensembles in overcoming challenges posed by imbalanced and scarce data for multiple-class DR classification. This approach paves the way for developing robust and accurate AI-powered diagnostic tools for improved diabetic retinopathy screening.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101557"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001138/pdfft?md5=7536f15c388ac8fc93a888c571ef8ae7&pid=1-s2.0-S2352914824001138-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841161","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":"An ECG Deep Learning user identification architecture using ECG sex recognition as a selective parameter","authors":"Jose-Luis Cabra López ,&nbsp;Carlos Parra ,&nbsp;Gonzalo Forero","doi":"10.1016/j.imu.2024.101563","DOIUrl":"10.1016/j.imu.2024.101563","url":null,"abstract":"<div><h3>Background:</h3><p>Human user authentication can be implemented by token-, keyword-, or identity-based mechanisms for digital environment session entry (i.e., smartphones, platforms with log-in). Physiological signals, such as ECG, have shown discriminative properties for user identity recognition. Due to ECG hidden nature, it is resilient to public trait exposition, light/noise saturation, or eavesdropping in contrast to fingerprint, facial, voice, or password approaches. ECG might fill those gaps toward a cooperative authentication environment.</p></div><div><h3>Methods:</h3><p>This paper proposes a Deep Learning identification scenario in which the inclusion of sex recognition directs the input sample toward a sex-specialized identity classification model, simplifying the discrimination space for each model. The architecture proposed could be suitable for large populations. Our scheme worked with an ECG three-axis pseudo-orthogonal configuration in which each axis is transformed into a time-frequency space. Additionally, we combine each lead matrix in an RGB image, joining the contribution of each wavelet waveform.</p></div><div><h3>Results:</h3><p>Our results suggest that it is possible to identify people by using RGB wavelet representations, achieving a classification average of 99.97%. In addition, the inclusion of the sex category for our identification purpose does not significantly affect the classification performance, making it a feasible solution for systems with a larger population.</p></div><div><h3>Conclusions:</h3><p>With the features of our database, we have evidence that it is possible to recognize a person’s identity using an ECG sex recognition module through our proposed architecture.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101563"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001199/pdfft?md5=7033d19b6ac3ea3a62bec9d541c40587&pid=1-s2.0-S2352914824001199-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962531","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":"Denoising diffusion probabilistic models for addressing data limitations in chest X-ray classification","authors":"Evi M.C. Huijben,&nbsp;Josien P.W. Pluim,&nbsp;Maureen A.J.M. van Eijnatten","doi":"10.1016/j.imu.2024.101575","DOIUrl":"10.1016/j.imu.2024.101575","url":null,"abstract":"<div><p>Deep learning plays a crucial role in medical imaging analysis, particularly in tasks such as image classification and segmentation. However, learning from medical imaging datasets presents challenges, including scarcity of labeled examples, class imbalances, and inadequate representation of diverse patient populations. To address these challenges, there has been a growing interest in the use of deep generative models to create synthetic training data, with denoising diffusion probabilistic models (DDPMs) recently gaining attention for their ability to produce realistic and high-quality images. This study explores the potential of a DDPM to generate synthetic chest X-rays for multi-label classifier training. The results indicate that the use of a conditional DDPM has the potential to produce a realistic training set of synthetic chest X-rays. In addition, the study analyzes the impact on classification performance of addressing class imbalance. Balancing the synthetic training set increased the overall classification sensitivity from 0.02 to 0.59, but decreased the overall specificity from 0.99 to 0.71. Furthermore, we investigated the potential of unconditional pre-training to learn general representations, followed by conditional fine-tuning of the DDPM. The results indicate that this approach allows the amount of labeled training data to be reduced to 25% of the original set. Finally, we demonstrate that fidelity and classification metrics do not consistently exhibit the same trends. Integrating a DDPM into the classification pipeline underscores the benefits of having optimal control over the data and efficient use of available unlabeled data. Our research provides insights for making informed decisions about integrating generative models into medical image analysis.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101575"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235291482400131X/pdfft?md5=629db3cc19c06c57d9e66726c73db9a2&pid=1-s2.0-S235291482400131X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058076","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 convolutional neural networks for filtering out normal frames in reviewing wireless capsule endoscopy videos","authors":"Ehsan Roodgar Amoli ,&nbsp;Pezhman Pasyar ,&nbsp;Hossein Arabalibeik ,&nbsp;Tahereh Mahmoudi","doi":"10.1016/j.imu.2024.101572","DOIUrl":"10.1016/j.imu.2024.101572","url":null,"abstract":"<div><p>Wireless capsule endoscopy (WCE) has emerged as a valuable non-invasive technique for visualizing the entire gastrointestinal (GI) tract. However, manual evaluation of WCE videos is a time-consuming and costly process. In this study, we present a novel diagnostic assistant system that employs deep convolutional neural networks (DCNNs) to accelerate the evaluation process. Our primary objective is to achieve a high negative predictive value (NPV), which is essential for the efficient identification of normal frames. Six distinct DCNN models were developed and implemented with this objective in mind. The models were trained on a limited dataset encompassing common GI pathologies that reflect real clinical scenarios. Each DCNN architecture comprises a convolutional part derived from renowned pre-trained networks and a custom-designed classifier block optimized for high NPV and classification accuracy. Following a comprehensive assessment utilizing the 5-fold cross-validation approach, the VG_BFCG model was identified as the most effective, exhibiting an average test accuracy of 0.946 and an NPV of 0.983. Moreover, in the event of encountering novel pathologies not present in the training data, our models exhibited robustness in NPV, which is of great importance for practical applications. For example, the DN_BFCG model demonstrated consistent performance, with an NPV exceeding 0.99 across a range of new pathologies. This validates the reliability of our models in clinical settings. Our findings suggest that our developed DCNN architectures have the potential to enhance the efficiency and accuracy of WCE video analysis, which could transform the landscape of gastroenterological diagnostics.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101572"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235291482400128X/pdfft?md5=55cf3c0dc8b0e8f24953f77449be27da&pid=1-s2.0-S235291482400128X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058277","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}