Intelligence-based medicine最新文献

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Fuzzy based system for coronary artery disease prediction using subtractive clustering and risk factors data 基于减法聚类和危险因素数据的冠状动脉疾病模糊预测系统
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100208
Abdeljalil El-Ibrahimi , Othmane Daanouni , Zakaria Alouani , Oussama El Gannour , Shawki Saleh , Bouchaib Cherradi , Omar Bouattane
{"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}
引用次数: 0
Breast cancer prediction using machine learning classification algorithms 使用机器学习分类算法预测乳腺癌
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2024.100193
Alan La Moglia , Khaled Mohamad Almustafa
{"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}
引用次数: 0
Hybrid deep learning and active contour approach for enhanced breast lesion segmentation and classification in mammograms 混合深度学习和主动轮廓方法增强乳房x光片中乳腺病变的分割和分类
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100224
Abdala Nour, Boubakeur Boufama
{"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}
引用次数: 0
Machine learning to predict haemorrhage after injury: So many models, so little dynamism 预测受伤后出血的机器学习:模型太多,动力太少
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100241
Greta Safoncik , Yeswanth Akula , Jared M. Wohlgemut , Allan Pang , Max Marsden
{"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}
引用次数: 0
Individual dynamic capabilities and artificial intelligence in health operations: Exploration of innovation diffusion 个人动态能力与卫生业务中的人工智能:创新扩散的探索
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100239
Antonio Pesqueira , Maria José Sousa , Rúben Pereira
{"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}
引用次数: 0
Enhancing nutritional status prediction through attention-based deep learning and explainable AI 通过基于注意力的深度学习和可解释的人工智能增强营养状况预测
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100255
Heru Agus Santoso , Nur Setiawati Dewi , Su-Cheng Haw , Arga Dwi Pambudi , Sari Ayu Wulandari
{"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}
引用次数: 0
Large language models in radiology reporting - A systematic review of performance, limitations, and clinical implications 放射学报告中的大型语言模型-性能,局限性和临床意义的系统回顾
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100287
Yaara Artsi , Eyal Klang , Jeremy D. Collins , Benjamin S. Glicksberg , Girish N. Nadkarni , Panagiotis Korfiatis , Vera Sorin
{"title":"Large language models in radiology reporting - A systematic review of performance, limitations, and clinical implications","authors":"Yaara Artsi ,&nbsp;Eyal Klang ,&nbsp;Jeremy D. Collins ,&nbsp;Benjamin S. Glicksberg ,&nbsp;Girish N. Nadkarni ,&nbsp;Panagiotis Korfiatis ,&nbsp;Vera Sorin","doi":"10.1016/j.ibmed.2025.100287","DOIUrl":"10.1016/j.ibmed.2025.100287","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>Large language models (LLMs) and vision-language models (VLMs), have emerged as potential tools for automated radiology reporting. However, concerns regarding their fidelity, reliability, and clinical applicability remain. This systematic review examines the current literature on LLM-generated radiology reports. Assessing their fidelity, clinical reliability, and effectiveness. The review aims to identify benefits, limitations, and key factors influencing AI-generated report quality.</div></div><div><h3>Materials and methods</h3><div>We conducted a systematic search of MEDLINE, Google Scholar, Scopus, and Web of Science to identify studies published between January 2015 and July 2025. Studies evaluating VLM/LLM-generated radiology reports were included (Transformer-based generative large language models). The study follows PRISMA guidelines. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.</div></div><div><h3>Results</h3><div>Fifteen studies met the inclusion criteria. Four assessed VLMs that generate full radiology reports directly from images, whereas eleven examined LLMs that summarize textual findings into radiology impressions. Six studies evaluated out-of-the-box (base) models, and nine analyzed models that had been fine-tuned. Twelve investigations paired automated natural-language metrics with radiologist review, while three relied on automated metrics. Fine-tuned models demonstrated better alignment with expert evaluations and achieved higher performance on natural language processing metrics compared to base models. All LLMs showed hallucinations, misdiagnoses, and inconsistencies.</div></div><div><h3>Conclusion</h3><div>LLMs show promise in radiology reporting. However, limitations in diagnostic accuracy and hallucinations necessitate human oversight. Future research should focus on improving evaluation frameworks, incorporating diverse datasets, and prospectively validating AI-generated reports in clinical workflows.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100287"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865345","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}
引用次数: 0
Osteo-fusion: A multimodal decision-chaining approach for automated knee osteoarthritis detection & severity classification 骨融合:一种多模式决策链方法用于膝关节骨关节炎的自动检测和严重程度分类
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100268
Neha Sharma , Riya Sapra , Sarita Gulia , Parneeta Dhaliwal
{"title":"Osteo-fusion: A multimodal decision-chaining approach for automated knee osteoarthritis detection & severity classification","authors":"Neha Sharma ,&nbsp;Riya Sapra ,&nbsp;Sarita Gulia ,&nbsp;Parneeta Dhaliwal","doi":"10.1016/j.ibmed.2025.100268","DOIUrl":"10.1016/j.ibmed.2025.100268","url":null,"abstract":"<div><h3>Purpose</h3><div>Knee Osteoarthritis (KOA) is a degenerative joint condition that affects the knee, caused by gradual deterioration of cartilage. Applying Machine Learning (ML) principles to the Medical Imaging (MI) data, related to KOA, has the ability to significantly improve automated disease identification and severity analysis.</div></div><div><h3>Materials and methods</h3><div>This study proposes a novel predictive classifier model, named as Osteo-Fusion, based on a <strong>Decision chaining approach</strong>, which combines the strengths of different modalities, such as X-ray and Gait to enable efficient automated diagnosis and severity classification of KOA. The proposed technique integrates the advantages of Transfer learning and Fusion learning to enhance the efficiency of the automated diagnostic process. The proposed technique employs a <strong>two-stage decision-chaining approach</strong> based on <strong>decision-level fusion</strong>.</div></div><div><h3>Results</h3><div>The proposed model achieved higher accuracy and precision values across both the X-ray and Gait classification tasks. The optimized VGG-16 model achieved <strong>98.5</strong><strong>%</strong> training accuracy and <strong>96</strong><strong>%</strong> validation accuracy on the X-ray dataset. The optimized VGG-16 model demonstrated strong performance on the gait dataset as well for severity classification, by obtaining 99% Training accuracy and 97% Validation accuracy,achieving an overall accuracy of <strong>98</strong><strong>%</strong>, precision of <strong>0.99</strong>, recall of <strong>0.97</strong>, and an F1-score of <strong>0.98</strong> across various performance metrics for severity classification. The proposed decision-chaining approach, which integrates structural and functional assessments for KOA classification, achieved an overall accuracy of <strong>85</strong><strong>%</strong> and a weighted F1-score of <strong>0.8325</strong> on the testing dataset. Grad-CAM visualizations are used to enhance interpretability by highlighting the regions influencing the model’s decisions.</div></div><div><h3>Conclusion</h3><div>The proposed model leverages the complementary strengths of multiple modalities, X-ray for structural assessment and gait analysis for functional evaluation, resulting in improved overall performance in automated disease diagnosis and severity classification. The accuracy achieved by optimized VGG-16 on X-ray and Gait is significantly higher as compared to the existing systems. The simulated decision-chaining system shows strong performance in identifying Moderate and Severe cases.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100268"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297553","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}
引用次数: 0
SmartOralDx: A deep learning-powered system for precise classification of oral diseases from clinical imagery SmartOralDx:一个深度学习驱动的系统,用于从临床图像中精确分类口腔疾病
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100278
Jashvant Kumar , Khaled Mohamad Almustafa , Akhilesh Kumar Sharma , Muhammed Sutcu , Juliano Katrib
{"title":"SmartOralDx: A deep learning-powered system for precise classification of oral diseases from clinical imagery","authors":"Jashvant Kumar ,&nbsp;Khaled Mohamad Almustafa ,&nbsp;Akhilesh Kumar Sharma ,&nbsp;Muhammed Sutcu ,&nbsp;Juliano Katrib","doi":"10.1016/j.ibmed.2025.100278","DOIUrl":"10.1016/j.ibmed.2025.100278","url":null,"abstract":"<div><div>The early and accurate diagnosis of oral diseases is essential for effective treatment and improved patient outcomes. This study introduces SmartOralDx, a deep learning-based diagnostic system designed to classify multiple oral disease categories from clinical imagery. The system was evaluated using various convolutional neural network (CNN) architectures, including baseline CNN, MobileNetV2, CNN + LSTM, and CNN + BiLSTM with Attention, across datasets comprising clinical and X-ray images. Initial results indicated that the inclusion of low-contrast X-ray images negatively impacted model performance. By refining the dataset to include only high-resolution clinical images and applying contrast-enhancement techniques using CLAHE, significant improvements were achieved in classification accuracy. The contrast-augmented CNN model achieved the highest testing accuracy of 94.26 %, while hybrid models incorporating temporal and attention mechanisms further enhanced interpretability and generalization, with the CNN + LSTM model reaching 90.75 % test accuracy. The study highlights the importance of data quality, augmentation, and model architecture in medical image classification and suggests that SmartOralDx has strong potential for integration into clinical workflows and mobile-based diagnostic tools. Future work will focus on expanding the dataset with diverse demographic inputs, deploying the system in real-time environments, and integrating it into smartphone-based platforms for broader accessibility.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100278"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633988","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}
引用次数: 0
Colonoscopy image analysis for polyp detection: A systematic review of existing approaches and opportunities 结肠镜图像分析用于息肉检测:现有方法和机会的系统回顾
Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100260
Carlos Albuquerque , Paulo Alexandre Neves , António Godinho , Eftim Zdravevski , Petre Lameski , Ivan Miguel Pires , Paulo Jorge Coelho
{"title":"Colonoscopy image analysis for polyp detection: A systematic review of existing approaches and opportunities","authors":"Carlos Albuquerque ,&nbsp;Paulo Alexandre Neves ,&nbsp;António Godinho ,&nbsp;Eftim Zdravevski ,&nbsp;Petre Lameski ,&nbsp;Ivan Miguel Pires ,&nbsp;Paulo Jorge Coelho","doi":"10.1016/j.ibmed.2025.100260","DOIUrl":"10.1016/j.ibmed.2025.100260","url":null,"abstract":"<div><h3>Objective:</h3><div>Colonoscopy is a diagnostic procedure using a flexible tube called a colonoscope with a camera to identify abnormalities in the large intestine and rectum, such as inflamed or swollen tissues, polyps, and cancer signs. It is crucial for early detection of colorectal cancer. However, analyzing colonoscopy images requires trained professionals, making it time-consuming and susceptible to errors. Advancements in machine learning have shown promising results in detecting polyps in colonoscopy images, improving efficiency. This paper provides a comprehensive overview of recent research in this field.</div></div><div><h3>Methods and procedures:</h3><div>This review uses the PRISMA (Preferred Items for Reporting Systematic Reviews and Meta-analyses) methodology, where an NLP (Natural Language Processing) toolkit, was used to search in several scientific databases, including IEEE Xplore, Springer, PubMed, Elsevier, and MDPI, published between 2010 and 2021, and related to colonoscopy detection based on image processing techniques.</div></div><div><h3>Results:</h3><div>This paper thoroughly analyzes the latest methods and prospects for polyp identification in colonoscopy pictures. Sixteen papers met the inclusion criteria, highlighting the need for automated system development and further research.</div></div><div><h3>Clinical Impact:</h3><div>The significance of the results lies in their ability to facilitate the creation of novel polyp identification techniques that medical professionals, trainees, and students may apply in near real-time.</div></div><div><h3>Conclusion:</h3><div>While every study that was given offers valuable insights into individual outcomes and methodology, no reports of clinical validation were made. A qualified individual’s validation is required for a method to be accepted. Even though the results are encouraging, the impact and applicability in actual situations are reduced in the absence of this phase.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100260"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471580","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}
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