Intelligence-based medicine最新文献

{"title":"Generative AI and scientific manuscript peer review","authors":"Robert Hoyt MD (Associate Clinical Professor), Alfonso Limon PhD (Senior Data Scientist), Anthony Chang MD (Chief Intelligence and Innovation Officer)","doi":"10.1016/j.ibmed.2025.100246","DOIUrl":"10.1016/j.ibmed.2025.100246","url":null,"abstract":"","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195200","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":"Attention-driven graph-based machine learning for non-invasive diagnosis of NAFLD","authors":"Ekta Srivastava ,&nbsp;Sarath Mohan ,&nbsp;Tapan Kumar Gandhi ,&nbsp;Ashok Kumar Choudhury ,&nbsp;Sandeep Kumar","doi":"10.1016/j.ibmed.2025.100288","DOIUrl":"10.1016/j.ibmed.2025.100288","url":null,"abstract":"<div><div>An estimated 25%–30% of the global population is affected by non-alcoholic fatty liver disease (NAFLD), a silent yet progressive condition that can advance from simple steatosis to severe stages like non-alcoholic steatohepatitis (NASH), fibrosis, and cirrhosis, significantly heightening the risk of liver cancer. Currently, the gold-standard method for staging NAFLD is liver biopsy, an invasive procedure with risks such as bleeding, infection, and sampling error. Due to its high cost and impracticality for routine monitoring, there is a critical need for reliable, non-invasive diagnostic tools capable of effectively identifying NAFLD stages. We developed a graph-based framework in which each patient is represented as a node in a similarity network. Edges are formed via k-nearest neighbors (KNN) on standardized clinical and biochemical features, with missing values imputed by KNN to preserve biologically plausible variability. A two-layer Graph Attention Network (GAT) then learns edge-specific attention weights to focus on the most informative inter-patient relationships. Tested on a proprietary ILBS cohort (n = 622), our model achieved 75.2% accuracy (AUC = 0.768; F1 = 0.752), an 11% absolute improvement over Support Vector Machines and Random Forests, and demonstrated robustness in 10-fold cross-validation and adversarial noise tests. On a separate public dataset (n = 80) spanning lipidomic, glycomic, fatty acid, and hormone panels, it exceeded 99% accuracy (AUC <span><math><mo>&gt;</mo></math></span> 0.99). Attention-based explanations further highlighted key patient similarities driving each prediction. These findings suggest that attention-driven graph learning can clearly improve non-invasive NAFLD staging, enabling early detection and supporting personalized disease monitoring in diverse clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912336","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":"Privacy-aware and interpretable deep learning framework for dental caries classification","authors":"Jashvant Kumar ,&nbsp;Khaled Mohamad Almustafa ,&nbsp;Rand Madanat ,&nbsp;Akhilesh Kumar Sharma ,&nbsp;Muhammed Sutcu ,&nbsp;Juliano Katrib","doi":"10.1016/j.ibmed.2025.100294","DOIUrl":"10.1016/j.ibmed.2025.100294","url":null,"abstract":"<div><div>Dental caries remains one of the most prevalent and persistent chronic diseases globally, affecting individuals across all age groups and posing a significant burden on public health systems. Early detection is critical to prevent the progression of tooth decay, reduce treatment complexity, and improve long-term oral health outcomes. In response to these clinical demands, this study presents a comprehensive, privacy-aware, and interpretable deep learning framework for the automated classification of dental caries from X-ray images. The approach addresses the issues of class imbalance, low Resolution image and privacy preserved patient's medical images.The framework is structured into three progressive phases that incorporate supervised learning through Convolutional Neural Networks (CNN), ResNet-18, and DenseNet; unsupervised clustering using Principal Component Analysis (PCA); and a decentralized federated learning strategy to ensure secure model training across distributed datasets. The experimental dataset consists of 957 labelled dental radiographs, including 174 healthy and 783 carious cases, emphasizing the issue of class imbalance. Initial baseline models achieved an accuracy of 84 %, which improved to 96 % following strategic data augmentation and class balancing interventions. PCA-based clustering visualizations revealed well-separated clusters (Silhouette Score: 0.6660), confirming the discriminative power of the selected features. Meanwhile, the federated learning implementation preserved data confidentiality without sacrificing performance, reinforcing the model's suitability for real-world clinical deployment. Collectively, these findings validate the framework's robustness, interpretability, and adaptability, offering a scalable and ethically aligned solution for AI-driven dental diagnostics in modern healthcare systems.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100294"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912406","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":"Exploring the intersection of cochlear implants and artificial intelligence: A mixed-method systematic and scoping review","authors":"Aurenzo Gonçalves Mocelin ,&nbsp;Pedro Angelo Basei de Paula ,&nbsp;Daniel Tiepolo Kochinski ,&nbsp;Thayná Cristina Wiezbicki ,&nbsp;Rogério de Azevedo Hamerschmidt ,&nbsp;Mayara Risnei Watanabe ,&nbsp;Rogério Hamerschmidt","doi":"10.1016/j.ibmed.2025.100296","DOIUrl":"10.1016/j.ibmed.2025.100296","url":null,"abstract":"<div><h3>Objective</h3><div>This study systematically evaluates the role of artificial intelligence (AI) in cochlear implant (CI) technology, focusing on speech enhancement, automated fitting, AI-assisted surgery, predictive modeling, and rehabilitation. The review identifies key advancements, existing limitations, and areas for future development.</div></div><div><h3>Methods</h3><div>Following PRISMA guidelines, we conducted a systematic search across PubMed, IEEE Xplore, Scopus, ScienceDirect, and Embase. We included peer-reviewed primary data studies on AI applications in CIs. The selected studies were categorized into thematic subdomains, such as noise suppression, adaptive programming, AI-driven surgical planning, and telemedicine applications.</div></div><div><h3>Results</h3><div>From an initial pool of 743 records, 129 studies met the eligibility criteria and were included in the final analysis. These studies were categorized into eleven thematic subdomains. The review identified the main application areas and emerging research fronts at the intersection of artificial intelligence and cochlear implant technologies, including speech enhancement, automated fitting, predictive modeling, rehabilitation support, and AI-assisted surgery.</div></div><div><h3>Discussion and conclusion</h3><div>AI is transforming CI technology by improving speech perception, personalization, and surgical precision. However, challenges persist, including computational constraints, data heterogeneity, and the need for large-scale clinical validation. Future research should prioritize energy-efficient AI architectures, regulatory approval pathways, and ethical considerations in automated decision-making. Advancing AI-driven telemedicine solutions can expand CI accessibility, reducing the need for in-person programming. Addressing these challenges will accelerate the development of more adaptive and user-centered CI solutions, ultimately enhancing auditory rehabilitation and quality of life for CI users.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100296"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264862","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":"Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects","authors":"Zhinya Kawa Othman ,&nbsp;Mohamed Mustaf Ahmed ,&nbsp;Olalekan John Okesanya ,&nbsp;Adamu Muhammad Ibrahim ,&nbsp;Shuaibu Saidu Musa ,&nbsp;Bryar A. Hassan ,&nbsp;Lanja Ibrahim Saeed ,&nbsp;Don Eliseo Lucero-Prisno III","doi":"10.1016/j.ibmed.2025.100233","DOIUrl":"10.1016/j.ibmed.2025.100233","url":null,"abstract":"<div><div>Advanced AI algorithms, notably generative pre-trained transformer (GPT) models, are revolutionizing healthcare and drug discovery and development by efficiently processing and interpreting large volumes of medical data. Specialized models, such as ProtGPT2 and BioGPT, extend their capabilities to protein engineering and biomedical text mining. Our study will contribute to ongoing discussions to revolutionize drug development, leading to a faster and more reliable validation of new therapeutic agents that are crucial for healthcare advancement and patient outcomes. GPT models, such as MTMol-GPT, are robust, generalizable, and provide important information for developing treatments for complicated disorders. SynerGPT utilizes a genetic algorithm to optimize prompts and select drug combinations for testing based on individual patient characteristics. Ligand generation for specific target proteins with potential drug activity is a significant stage in the drug design process, which enhances the quality of the synthesized compounds and augments the precision of capturing chemical structures and their activity correlations, highlighting the model's creativity and capability for innovative ligand design. Despite these advancements, there are still problems with the data volume, scalability, interpretability, and validation. Ethical considerations, robust methods, and omics data must be successfully integrated to develop AI for drug discovery and ensure successful deployment. In summary, these models significantly influence drug research and development, specifically in the earlier stages from initial target selection to post-marketing surveillance for medication safety monitoring.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548273","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":"Testing the real-world utility of Bayes theorem in artificial intelligence-enabled electrocardiogram algorithm for the detection of left ventricular systolic dysfunction","authors":"Betsy J. Medina-Inojosa ,&nbsp;David M. Harmon ,&nbsp;Jose R. Medina-Inojosa ,&nbsp;Rickey E. Carter ,&nbsp;Itzhak Zachi Attia ,&nbsp;Paul A. Friedman ,&nbsp;Francisco Lopez-Jimenez","doi":"10.1016/j.ibmed.2025.100238","DOIUrl":"10.1016/j.ibmed.2025.100238","url":null,"abstract":"<div><h3>Objective</h3><div>To assess how the theoretical principles of Bayes' theorem hold true in a clinically impactful way when testing the diagnostic performance of an artificial intelligence (AI) tool, using the case of the AI-enabled electrocardiogram (AI-ECG) screening tool that detects left ventricular systolic dysfunction (LVSD) in a “real-world” setting.</div></div><div><h3>Patient and methods</h3><div>We analyzed data from 42,883 consecutive patients who underwent a clinically indicated ECG and an echocardiogram within two weeks at our center between January 1st and December 31st^, 2019. We then evaluated area under the curve (AUC) of the receiver operating characteristics, sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the AI-ECG to detect LVSD (left ventricle ejection fraction of ≤40 %) across (i) cumulative risk factor prevalence (pre-test probabilities) (ii) different diagnostic thresholds, using paired ECG-echocardiogram data.</div></div><div><h3>Results</h3><div>Prevalence of LVSD was 1.9 %, 4.0 %, 7.0 % and 13.9 % for patients with 0, 1–2, 3–4 and ≥5 risk-factors for LVSD. The AUC of the AI-ECG for each group was 0.955, 0.933, 0.901 and 0.886, respectively (p for trend&lt;0.001). Pre-test probabilities hardly influenced sensitivity but did impact specificity. PPV was affected more than NPV, which was modestly altered. Thresholds impacted diagnostic performance parameters, although their effect on NPV at low pre-test probability was negligible.</div></div><div><h3>Conclusion</h3><div>In real world, pre-test probabilities/cumulative risk-factors of disease do affect specificity. Using different diagnostic thresholds yields the highest impact on algorithm performance. A Bayesian approach may enhance individualized diagnostic performance when implementing AI algorithms.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825511","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":"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":"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":"Artificial intelligence and patient care: Perspectives of audiologists and speech-language pathologists","authors":"Komal Aggarwal ,&nbsp;Rohit Ravi ,&nbsp;Krishna Yerraguntla","doi":"10.1016/j.ibmed.2025.100214","DOIUrl":"10.1016/j.ibmed.2025.100214","url":null,"abstract":"<div><h3>Background</h3><div>Artificial Intelligence has been implemented across various fields, including healthcare, where it has significantly advanced patient care in recent years. The present study aimed to explore the perspectives of audiologists and speech-language pathologists (ASLPs) toward AI in patient care.</div></div><div><h3>Methods</h3><div>The study employed a cross-sectional design with a convenience sampling method. The questionnaire included 27 questions consisting of demographic details and perspectives towards AI in audiology and speech language pathology services. Descriptive statistics were performed to analyze the data.</div></div><div><h3>Results</h3><div>Ninety-five ASLPs participated in the study, working across different work settings and with a mean age of 28.34 years, ranging between 18 and 47 years. Almost 50 % of participants reported AI tools can be helpful in diagnosis and planning the treatment. About One-fourth (25 %) believed that AI could help in rehabilitation. Few of participants (14.8 %) reported that AI may replace audiology and speech-language pathology services. ChatGPT was the most used platform by ASLPs in their practice. The ASLP clinicians believed AI would revolutionise ASLP practice without alarming effects on their employability.</div></div><div><h3>Conclusion</h3><div>The findings suggest that while AI has potential in ASLP practice, there is still a need for greater understanding and adoption of the technology.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173632","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}