Intelligence-based medicine最新文献

{"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":"V-NET-VGG16: Hybrid deep learning architecture for optimal segmentation and classification of multi-differentiated liver tumors","authors":"Amine Ben Slama ,&nbsp;Hanene Sahli ,&nbsp;Yessine Amri ,&nbsp;Salam Labidi","doi":"10.1016/j.ibmed.2025.100210","DOIUrl":"10.1016/j.ibmed.2025.100210","url":null,"abstract":"<div><div>Liver cancer is a leading cause of cancer-related mortality worldwide, underscoring the importance of early and accurate diagnosis. This study aims to develop an automatic system for liver tumor detection and classification using Computed Tomography (CT) images, addressing the critical challenge of accurately segmenting liver tumors and classifying them as benign, malignant, or normal tissues.</div><div>The proposed method combines two advanced deep learning models: V-Net for tumor segmentation and VGG16 for classification. A liver CT dataset augmented with various transformations, was used to enhance the model's robustness. The data was split into training (70 %) and testing (30 %) sets. The V-Net model performs the segmentation, isolating the liver and tumor regions from the CT images, while VGG16 is used for the classification of tumor types based on the segmented data.</div><div>The results demonstrate the effectiveness of this hybrid approach. The V-Net model achieved a Dice score of 97.34 % for accurate tumor segmentation, while the VGG16 model attained a classification accuracy of 96.52 % in differentiating between benign, malignant, and normal cases. These results surpass several existing state-of-the-art approaches in liver tumors analysis, demonstrating the potential of the proposed method for reliable and efficient medical image processing.</div><div>In conclusion, the hybrid V-Net and VGG16 architecture offers a powerful tool for the segmentation and classification of liver tumors, providing a significant improvement over manual segmentation methods that are prone to human error. This approach could aid clinicians in early diagnosis and treatment planning. Future work will focus on expanding the dataset and applying the method to other types of cancer to assess the model's generalizability and effectiveness in broader clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174327","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 robust deep learning algorithm for lung cancer detection from computed tomography images","authors":"A.A. Abe ,&nbsp;M. Nyathi ,&nbsp;A.A. Okunade ,&nbsp;W. Pilloy ,&nbsp;B. Kgole ,&nbsp;N. Nyakale","doi":"10.1016/j.ibmed.2025.100203","DOIUrl":"10.1016/j.ibmed.2025.100203","url":null,"abstract":"<div><div>Detecting lung cancer at its earliest stage offers the best possibility for a cure. Chest computed tomography (CT) scans are a valuable tool for early diagnosis. However, the initial stages of lung cancer may present patterns in the images that are not easily detectable by radiologist, potentially leading to misdiagnosis. Although automated approaches using deep learning (DL) algorithms have been proposed, it depends on a substantial amount of data to achieve diagnostic accuracy comparable to that of radiologists. To alleviate this challenge, this study proposes a DL algorithm that uses an ensemble of convolutional neural networks and trained on relatively small dataset (IQ_OTH/NCCD dataset) to automate lung cancer diagnosis from patient chest CT scans. The method achieved an accuracy of 98.17 %, a sensitivity of 98.21 %, and a specificity of 98.13 % when categorizing scans as either cancerous or non-cancerous. Similarly, it achieved an accuracy of 95.43 %, a sensitivity of 93.40 %, and a specificity of 97.09 % when classifying scans as normal or containing benign or malignant pulmonary nodules. These results demonstrate superior performance compared to previously proposed models, highlighting the effectiveness of DL algorithms for early lung cancer diagnosis and providing a valuable tool to assist radiologists in their assesments.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174354","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":"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":"Can artificial intelligence help physicians using diaphragmatic ultrasound?","authors":"Tianjie Zhang ,&nbsp;Changchun Li ,&nbsp;Dongwei Xu ,&nbsp;Yan Liu ,&nbsp;Qi Zhang ,&nbsp;Ye Song","doi":"10.1016/j.ibmed.2025.100202","DOIUrl":"10.1016/j.ibmed.2025.100202","url":null,"abstract":"<div><h3>Purpose</h3><div>We investigated the role of artificially intelligent architecture based on deep learning radiomics (DLR) in analyzing M-mode and B-mode ultrasound videos of the diaphragm for diaphragmatic ultrasound.</div></div><div><h3>Methods</h3><div>A total of 196 subjects underwent pulmonary function and ultrasonic examination of the diaphragm. All diaphragmatic ultrasound videos were collected by experienced sonographers as the entire dataset used in this study. The experiment was partitioned into two parts. First, the diaphragm images (including M-mode and B-mode) of 157 subjects were input into the artificial intelligence architecture by the AI team. Second, the test set comprised 39 subjects, each equipped with three mobility images and three thickness images. We applied the proposed parameter calculation method to this set. The method entails segmenting the images, extracting the diaphragmatic motion and thickness variation curves from the segmentation results, and subsequently analyzing these curves to acquire the target parameters. Concurrently, we documented the time taken for each measurement. In parallel, three medical professionals performed analogue measurements. We analysed the accuracy and consistency of the artificial intelligence measurements.</div></div><div><h3>Results</h3><div>The study included a total of 196 subjects. The optimal segmentation model achieved dice scores of 73.51 % and 80.76 % on the test sets of mobility images and thickness images, respectively. Our method yielded results similar to those obtained by senior sonographers and demonstrated a high level of consistency with all three medical professionals, particularly the senior sonographer, in the measurements of diaphragm excursion (DE), diaphragm contraction duration (DCD), and diaphragmatic thickness at the end of inspiration (DTei). Meanwhile, our proposed method exhibited the highest level of time efficiency. The average duration for measuring the mobility images was 1.49s and for thickness images was 0.68s, compared to critical care physicians (8.23s, 15.89s), junior sonographers (6.14s, 9.69s), and senior sonographers (4.48s,6 0.77s).</div></div><div><h3>Conclusions</h3><div>Our study suggests that artificial intelligence can assist physicians in obtaining accurate diaphragmatic ultrasound data and reducing interobserver variability. Additionally, it could also improve time efficiency in this process.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419553","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 breast cancer detection in ultrasound images using a novel hybrid ensemble deep learning model","authors":"Radwan Qasrawi ,&nbsp;Omar Daraghmeh ,&nbsp;Suliman Thwib ,&nbsp;Ibrahem Qdaih ,&nbsp;Ghada Issa ,&nbsp;Stephanny Vicuna Polo ,&nbsp;Haneen Owienah ,&nbsp;Diala Abu Al-Halawa ,&nbsp;Siham Atari","doi":"10.1016/j.ibmed.2025.100222","DOIUrl":"10.1016/j.ibmed.2025.100222","url":null,"abstract":"<div><div>Breast cancer remains a leading cause of mortality among women globally, emphasizing the critical need for prompt and accurate detection to improve patient outcomes. This study introduces an innovative hybrid model combining ultrasound image enhancement techniques with advanced machine learning for rapid and more accurate breast cancer prognosis. The proposed model integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) for image quality improvement with an Ensemble Deep Random Vector Functional Link Neural Network (edRVFL) for classification. Utilizing a dataset of 4103 high-resolution ultrasound images from the Dunya Women's Cancer Center in Palestine, categorized into normal, benign, and malignant groups, the model was trained and evaluated using a 25-fold cross-validation approach. Results demonstrate higher performance of the hybrid model compared to traditional machine learning algorithms, achieving accuracies of 96 % for benign and 98 % for malignant cases after CLAHE enhancement. To further improve lesion detection and segmentation, a new method combining YOLOv5 object detection with the MedSAM foundation model was developed, achieving a Dice Similarity Coefficient of 0.988 after CLAHE enhancement. Validation in a clinical setting on 850 cases showed promising results, with 91.4 % ± 0.021 accuracy for benign and 84 % ± 0.024 for malignant predictions compared to histopathology. The model's high accuracy and interpretability, supported by Grad-CAM analysis, demonstrate its potential for integration into clinical practice. This study advances the application of machine learning in breast cancer detection from ultrasound images, presenting a valuable tool for enabling early detection and improving prognosis for breast cancer patients.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403018","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":"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":"New cognitive computational strategy for optimizing brain tumour classification using magnetic resonance imaging Data","authors":"R. Kishore Kanna ,&nbsp;Ayodeji Olalekan Salau","doi":"10.1016/j.ibmed.2025.100215","DOIUrl":"10.1016/j.ibmed.2025.100215","url":null,"abstract":"<div><div>The brain is one of the most important organs in the human body. It governs all actions whether one is aware of the action or not. Brain tumors occur when the system of cell division in the brain is disrupted. Brain tumors are frequently associated with severe malignancies worldwide. The uncontrolled accumulation and growth of these cells can lead to the formation of seizures or tumors with impaired brain function.</div><div>Magnetic resonance imaging (MRI) is a common technology used to detect brain lesions; however, manual analysis of MRI images by physicians is challenging due to uncertainty and time constraints. The aim of this paper is to introduce machine learning (ML) algorithms designed to increase the speed and cognitive statistical methods for brain tumor classification.</div><div>In this study, we proposed a novel penguin search-optimized quantum-enhanced support vector machine (PSO-QESVM) to categorize brain tumor using MRI data. We used a publicly accessible brain MR image dataset for brain tumor classification tasks which we obtained from an online source. A median filter (MF) was used as part of the pre-processing step to eliminate noise from the data. Using ResNet and VGG16, features were extracted from the pre-processed data.</div><div>The proposed method was implemented using Python 3.7+ software. A comparison was made between the suggested approach and other conventional algorithms. The results show the proposed method achieved a superior efficiency with regards to recall (98.9 %), accuracy (98.90 %), f1-score (98.5 %), and precision (98.7 %).</div><div>The study demonstrated the applicability of the suggested strategy for brain tumor classification. The suggested cognitive computational strategy achieved a promising performance. To reduce the size of the model and implement it on a real-time medical diagnosis framework, we intend to employ knowledge distillation techniques.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175220","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":"DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification","authors":"Mohammad Amin ,&nbsp;Khalid M.O. Nahar ,&nbsp;Hasan Gharaibeh ,&nbsp;Ahmad Nasayreh ,&nbsp;Neda'a Alsalmanc ,&nbsp;Alaa Alomar ,&nbsp;Majd Malkawi ,&nbsp;Noor Alqasem ,&nbsp;Aseel Smerat ,&nbsp;Raed Abu Zitar ,&nbsp;Shawd Nusier ,&nbsp;Absalom E. Ezugwu ,&nbsp;Laith Abualigah","doi":"10.1016/j.ibmed.2024.100192","DOIUrl":"10.1016/j.ibmed.2024.100192","url":null,"abstract":"<div><div>Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174360","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}