Intelligence-based medicine最新文献

{"title":"CTCovid19: Automatic Covid-19 model for Computed Tomography Scans Using Deep Learning","authors":"Carlos Antunes ,&nbsp;João Rodrigues ,&nbsp;António Cunha","doi":"10.1016/j.ibmed.2024.100190","DOIUrl":"10.1016/j.ibmed.2024.100190","url":null,"abstract":"<div><div>COVID-19 is an extremely contagious respiratory sickness instigated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Common symptoms encompass fever, cough, fatigue, and breathing difficulties, often leading to hospitalization and fatalities in severe cases. CTCovid19 is a novel model tailored for COVID-19 detection, specifically honing in on a distinct deep learning structure, ResNet-50 trained with ImageNet serves as the foundational framework for our model. To enhance its capability to capture pertinent features related to COVID-19 patterns in Computed Tomography scans, the network underwent fine-tuning through layer adjustments and the addition of new ones. The model achieved accuracy rates that went from 97.0 % to 99.8 % across three widely recognized and documented datasets dedicated to COVID-19 detection.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174359","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":"Skin cancer detection using deep machine learning techniques","authors":"Olusoji Akinrinade,&nbsp;Chunglin Du","doi":"10.1016/j.ibmed.2024.100191","DOIUrl":"10.1016/j.ibmed.2024.100191","url":null,"abstract":"<div><div>Technological advancements have allowed people to have unfettered access to the internet from anywhere in the world. However, there is still little access to healthcare in rural and remote areas. This study highlights the potential of deep learning techniques in improving the early detection of skin cancer, a condition affecting millions globally. By addressing the challenges of class imbalance and dataset limitations, this research presents a model that can be integrated into digital health platforms, potentially saving lives by enabling earlier diagnosis and intervention, especially in underserved regions. The study also suggest using deep learning and few-shot learning when using machine learning techniques for skin cancer diagnosis. This study utilized a novel approach the use of raw images for training and test images for test data. These input images were then pre-processed using a deep model to identify and predict subsequent outputs using the model. In addition, the effect of the Convolutional Neural Network (CNN) effect in predicting accuracy using a skin lesion's texture to differentiate between benign and malignant lesions in the body was also examined using retrieved image elements from skin photos that were significant to skin cancer identification. The study focuses on using deep learning techniques to improve the detection of skin cancer from dermoscopic images. Deep learning a top-tier method for classifying skin lesions, was applied to create an end-to-end algorithm that could identify skin cancer more accurately. A variety of deep learning backbones were utilized, addressing the challenge of class imbalance in large datasets and seeking ways to boost performance even when only small datasets are available. To overcome these obstacles, the research leveraged transfer learning, data augmentation, and Generative Adversarial Networks (GANs). It further explored different sampling techniques and loss functions that could be effective for imbalanced datasets. The study also involved a comparison between ensemble models and hybrid models to determine which was more effective for the early detection of skin cancer. The paper concluded with a discussion of the challenges faced in the early detection of skin cancer, suggesting that while progress has been made, there are still significant hurdles to overcome.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174755","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":"Comparative analysis of resource-efficient YOLO models for rapid and accurate recognition of intestinal parasitic eggs in stool microscopy","authors":"Kotteswaran Venkatesan ,&nbsp;Muthunayagam Muthulakshmi ,&nbsp;Balaji Prasanalakshmi ,&nbsp;Elangovan Karthickeien ,&nbsp;Harshini Pabbisetty ,&nbsp;Rahayu Syarifah Bahiyah","doi":"10.1016/j.ibmed.2025.100212","DOIUrl":"10.1016/j.ibmed.2025.100212","url":null,"abstract":"<div><div>Faster and reliable recognition of the specific species of intestinal parasite eggs in stool microscopic images is required for targeted and quick intervention of soil transmitted helminths (STH) disease. The main objective of the proposed work is to identify the effective light weight basic yolo models among the recent compact yolo variants such as yolov5n, yolov5s, yolov7, yolov7-tiny, yolov8n, yolov8s, yolov10n and yolov10s, that could assist in rapid and accurate recognition of 11 parasite species egg. The real time performance of the compact yolo models have been analyzed in embedded platforms: Raspberry Pi 4, Intel upSquared with the Neural Compute Stick 2 and Jetson Nano. Finally, Gradient-weighted class activation mapping (Grad-CAM) has been used as an explainable AI (XAI) visualization method to elucidate the egg detection performance of the proposed models. Yolov7-tiny achieved the overall highest mean Average Precision (mAP) score of 98.7 %. On contrary, yolov10n yielded highest recall and F1 score of 100 % and 98.6 %. On other hand, yolov8n took least inference time with processing speed of 55 frames per second with Jetson Nano. Notably, the proposed framework demonstrates superior performance in detection of egg classes - Enterobius vermicularis, Hookworm egg, Opisthorchis viverrine, Trichuris trichiura, and Taenia spp. which is a significant outcome of the current research. Further, Grad-CAM depicts the discriminative power of unique features in parasite eggs. Thus, this study demonstrates the effectiveness, compactness and inference latency analysis of basic compact yolo variants in learning the specific patterns, texture and shape of parasitic egg species, thereby potentially enhancing the diagnostic accuracy of STH.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100212"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175219","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":"Artificial intelligence for the diagnosis of Helicobacter pylori infection in endoscopic and pathological tissues images: A systematic review and meta-analysis","authors":"Yuting Wen ,&nbsp;Yao Huang ,&nbsp;Yu Liu ,&nbsp;Shasha Zhang ,&nbsp;Zhe Liu ,&nbsp;Chan Hui ,&nbsp;Yi Wang","doi":"10.1016/j.ibmed.2025.100244","DOIUrl":"10.1016/j.ibmed.2025.100244","url":null,"abstract":"<div><h3>Background</h3><div>In recent years, artificial intelligence (AI) algorithms, including deep learning, have shown remarkable progress in image-recognition tasks. This study aimed to evaluate the diagnostic performance of AI in diagnosing Helicobacter pylori (H. pylori) infection using endoscopic and pathological images.</div></div><div><h3>Methods</h3><div>A literature search was conducted across multiple databases to identify all primary studies related to the diagnostic performance of AI algorithms for H. pylori infection published before 2024. True positive (TP), false positive (FP), false negative (FN), and true negative (TN) values were extracted or calculated for each study. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), precision-recall (PR), and diagnostic odds ratio (DOR) were calculated. A summary receiver operating characteristic curve (SROC) was used to assess overall diagnostic performance.</div></div><div><h3>Results</h3><div>Twelve studies were included in the final analysis. The pooled sensitivity was 0.87 (95 % CI 0.78–0.92), pooled specificity was 0.79 (95 % CI 0.54–0.92), pooled PLR was 4.1 (95 % CI 1.7–9.8), and pooled NLR was 0.17 (95 % CI 0.10–0.29). The DOR was 24 (95 % CI 7–78), and the SROC was 0.90 (95 % CI 0.87–0.92). Substantial heterogeneity was observed among the studies (sensitivity: I² = 90.50 %, 95 % CI 86.37–94.62; specificity: I² = 98.66 %, 95 % CI 98.34–98.97). Deek's funnel plot indicated low publication bias (P = 0.89).</div></div><div><h3>Conclusions</h3><div>AI algorithms show potential in diagnosing HP infection by improving accuracy and lesion detection. However, due to heterogeneity in study results, more comprehensive clinical validation is needed before widespread application. Future research should focus on multicenter validation, standardized datasets, integration into clinical workflows, and addressing data privacy and ethics to promote broader use of AI in HP diagnosis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100244"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820708","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":"Fuzzy based system for coronary artery disease prediction using subtractive clustering and risk factors data","authors":"Abdeljalil El-Ibrahimi ,&nbsp;Othmane Daanouni ,&nbsp;Zakaria Alouani ,&nbsp;Oussama El Gannour ,&nbsp;Shawki Saleh ,&nbsp;Bouchaib Cherradi ,&nbsp;Omar Bouattane","doi":"10.1016/j.ibmed.2025.100208","DOIUrl":"10.1016/j.ibmed.2025.100208","url":null,"abstract":"<div><div>Over the past three decades, coronary artery disease (CAD) has been considered one of the most common fatal diseases worldwide. Consequently, early diagnosis and prediction are essential, as they can significantly reduce patient mortality and treatment costs. This study aims to design an automatic expert system using fuzzy logic theory to predict CAD. Thus, aiding physicians to identify diseases at an early stage and assess their severity. This system generates fuzzy rules automatically from training dataset through a subtractive clustering method and employs the Sugeno Fuzzy Inference Engine to produce an output indicating the patient's condition. Feature selection is performed using filter methods such as variance analysis, Mutual Information, and Pearson's Correlation Coefficient to identify the most relevant factors affecting heart disease. The implementation is conducted on publicly available UCI heart disease datasets, and the system's performance is evaluated based on accuracy, specificity, and sensitivity metrics. The findings indicate a classification accuracy of 99.61 %, achieving a sensitivity rate of 100 % and a specificity rate of 99.20 %. These findings highlight the system's potential as an effective diagnostic and early prevention tool, ultimately improving clinical outcomes in CAD treatment.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174328","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":"Breast cancer prediction using machine learning classification algorithms","authors":"Alan La Moglia ,&nbsp;Khaled Mohamad Almustafa","doi":"10.1016/j.ibmed.2024.100193","DOIUrl":"10.1016/j.ibmed.2024.100193","url":null,"abstract":"<div><div>In bioinformatics, the integration of machine learning has revolutionized disease diagnosis. Machine learning algorithms remove human limitations, offering more accuracy in diagnosing diseases like cancer. Breast cancer, the second most diagnosed cancer in women, often relies on mammography, which is only 70 % accurate, leading to potential misdiagnosis. Biopsies, though more reliable, are subject to human error and conflicting specialist opinions, often requiring multiple biopsies. The shortage of pathologists further complicates accurate and timely diagnoses. Machine learning can reduce these errors, providing faster and more precise results. In this study, a breast cancer dataset with 11 features is analyzed using eight machine learning classifiers. Results showed that Logistic Regression achieved the highest testing accuracy of 91.67 % without feature selection. After applying feature selection, classifiers like LGBM improved, with a notable 90.74 % accuracy. This study highlights the importance of integrating machine learning into healthcare, not only for breast cancer but for other diseases like heart disease and diabetes. Continued exploration and application of machine learning in bioinformatics will enhance its accessibility and effectiveness for medical professionals worldwide, leading to improved patient outcomes.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174753","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":"Hybrid deep learning and active contour approach for enhanced breast lesion segmentation and classification in mammograms","authors":"Abdala Nour,&nbsp;Boubakeur Boufama","doi":"10.1016/j.ibmed.2025.100224","DOIUrl":"10.1016/j.ibmed.2025.100224","url":null,"abstract":"<div><div>Accurate segmentation and classification of breast lesions in mammography images are crucial steps in effective breast cancer screening and diagnosis. This study presents a hybrid deep learning and active contour approach to automated mammogram analysis. The proposed methodology leverages the powerful feature extraction capabilities of deep convolutional neural networks and the precise boundary delineation of active contour models. A U-Net is trained on a large dataset of mammogram images to learn discriminative features and generate initial segmentation masks for breast lesions. Subsequently, an active contour refinement stage is employed to fine-tune the segmentation boundaries and enhance lesion delineation accuracy. This integration of active contour models (ACM) with deep learning techniques overcomes traditional image segmentation limitations. Morphological operations and energy minimization techniques are applied to the initial segmentation mask, resulting in highly accurate and refined lesion segmentation. This study investigates the synergistic integration of deep learning with Adaptive Contour Modeling for breast lesion segmentation. Our proposed U-Net_ACM model leverages the strengths of both approaches, demonstrating state-of-the-art performance and outperforming methods relying solely on deep learning or traditional image processing techniques. Evaluation on a test set reveals a 97.34 % accuracy, a Dice coefficient of 0.813, and an Intersection over Union of 0.891 for the U-Net_ACM model. These results surpass the performance of established pre-trained deep learning models such as VGG16, VGG19, and DeepLabV3, highlighting the benefits of the combined approach. This hybrid methodology offers a robust, automated solution for mammogram analysis, potentially improving breast cancer screening outcomes. The superior segmentation quality and overall performance demonstrated by the U-Net_ACM model suggest its potential for enhancing breast cancer screening and diagnosis in clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454805","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":"Machine learning to predict haemorrhage after injury: So many models, so little dynamism","authors":"Greta Safoncik ,&nbsp;Yeswanth Akula ,&nbsp;Jared M. Wohlgemut ,&nbsp;Allan Pang ,&nbsp;Max Marsden","doi":"10.1016/j.ibmed.2025.100241","DOIUrl":"10.1016/j.ibmed.2025.100241","url":null,"abstract":"<div><div>Accurately predicting the need for blood transfusion in bleeding patients remains a critical challenge in emergency care. Machine learning (ML) models show promise for improving decision support in these scenarios, but a gap remains between research and practical application. Existing models frequently overlook the dynamic nature of clinical data, hindering their ability to provide accurate predictions for blood transfusion needs in emergency settings. We conducted a scoping review to examine ML models that integrate time-varying variables to predict blood transfusion needs in trauma patients. We discuss challenges in data collection, particularly the limitations of electronic health records (EHRs) in capturing high-quality time-series data and emphasise the need for explainable artificial intelligence (AI). We suggest future directions for research that include advancing computational approaches, improving data collection, and enhancing the interpretability of ML models to ensure their clinical relevance and utility.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783419","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":"Individual dynamic capabilities and artificial intelligence in health operations: Exploration of innovation diffusion","authors":"Antonio Pesqueira ,&nbsp;Maria José Sousa ,&nbsp;Rúben Pereira","doi":"10.1016/j.ibmed.2025.100239","DOIUrl":"10.1016/j.ibmed.2025.100239","url":null,"abstract":"<div><div>This research investigates the integration of individual dynamic capabilities (IDC), artificial intelligence (AI), and the Technology Acceptance Model (TAM) within health operations to evaluate their role in fostering innovation diffusion in healthcare. A convergent, multifaceted research approach encompassing quantitative and qualitative methodologies was employed, commencing with a systematic review of the extant literature. This was then complemented by the execution of focus group sessions involving 21 participants. The main objective of this sequential exploratory design was to synthesize existing research present an empirical validation of real-world case studies, and assess AI deployment challenges that influence operational efficiency and service quality in healthcare organizations. The findings underscore the importance of IDC in advancing healthcare practices by driving cross-functional adaptation, facilitating AI implementation, and ensuring smooth operational transformation in line with healthcare standards and best practices. The findings offer valuable insights for operational and executive-level decision-makers aiming to optimize health operations by integrating IDC and AI technologies, enhancing patient care, service quality, and innovative health solutions.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100239"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725918","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 nutritional status prediction through attention-based deep learning and explainable AI","authors":"Heru Agus Santoso ,&nbsp;Nur Setiawati Dewi ,&nbsp;Su-Cheng Haw ,&nbsp;Arga Dwi Pambudi ,&nbsp;Sari Ayu Wulandari","doi":"10.1016/j.ibmed.2025.100255","DOIUrl":"10.1016/j.ibmed.2025.100255","url":null,"abstract":"<div><div>Accurate and interpretable prediction of child malnutrition remains a critical challenge, as existing AI models often lack the transparency needed for clinical adoption. This study introduces a deep learning framework enhanced with Multi-Head Attention (MHA) for nutritional status prediction, offering a novel contribution through the first direct head-to-head comparison of CNN-MHA and LSTM-MHA to evaluate the effectiveness of spatial feature learning versus sequential dependency modeling in structured anthropometric tabular data. Our framework integrates advanced preprocessing techniques, feature selection, and Explainable AI (SHAP), enabling clinically aligned and transparent predictions. Experimental results on a 9605-sample dataset reveal that CNN-MHA achieves superior performance (99.08 % accuracy) compared to LSTM-MHA (98.91 %), confirming that spatial modeling is better suited for this dataset type. SHAP-based feature attribution further validates WHO-standard z-scores as the most influential predictors, enhancing model credibility for clinical application. Additionally, the study introduces an IoT-enabled anthropometric data acquisition system, enhancing real-time monitoring and scalability. This research represents a significant methodological advancement in nutritional status prediction, addressing key gaps in feature prioritization, accuracy, and interpretability. By bridging the gap between high-accuracy AI and clinical transparency, this study advances AI-driven nutritional monitoring and offers a scalable, explainable framework for public health interventions. Future research should explore multi-modal data integration to further enhance generalizability and real-world applicability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934810","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}