Intelligence-based medicine最新文献

{"title":"Enhanced Polycystic Ovary Syndrome diagnosis model leveraging a K-means based genetic algorithm and ensemble approach","authors":"Najlaa Faris ,&nbsp;Aqeel Sahi ,&nbsp;Mohammed Diykh ,&nbsp;Shahab Abdulla ,&nbsp;Siuly Siuly","doi":"10.1016/j.ibmed.2025.100253","DOIUrl":"10.1016/j.ibmed.2025.100253","url":null,"abstract":"<div><div>Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women in their childbearing years. Detecting PCOS early is crucial for preserving fertility in young women and preventing long-term health complications like hypertension, heart disease, and obesity. While costly clinical tests exist to detect PCOS, there is a growing demand for more accurate and affordable diagnostic methods. The primary objective of this research is to pinpoint the most effective PCOS features that can aid experts in early diagnosis. We introduce a feature extraction model, termed KM-GN, which combines the k-means algorithm with a genetic selection algorithm to identify the most informative features for PCOS detection. These selected features are fed into our designed model, Random Subspace-based Bootstrap Aggregating Ensembles (RSBE). To assess the performance of the proposed RSBE method, we compare it against several individual and ensemble classifiers. The effectiveness of our model is assessed using a freely accessible dataset comprising 43 traits from 541 women, of whom 177 have been diagnosed with PCOS. We employ various statistical metrics to evaluate the performance, including the confusion matrix, accuracy, recall, F1 score, precision, and specificity. The experimental outcomes demonstrate the viability of implementing our proposed model as a hardware tool for efficient detection of PCOS.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100253"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882535","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":"Segmentation of low-resolution MII human oocyte images using data-efficient meta-learning","authors":"Mahshid Alizadeh Kiashi,&nbsp;Ashkan Mousazadeh Soustani,&nbsp;Seyed Abolghasem Mirroshandel","doi":"10.1016/j.ibmed.2025.100327","DOIUrl":"10.1016/j.ibmed.2025.100327","url":null,"abstract":"<div><div>Although many infertility problems in humans are treatable today, some of these methods have problems that need to be addressed. One of the ways to solve infertility problems is to use in vitro fertilization. In this method, if a suitable oocyte with a high chance of fertility is not selected, it may lead to multiple births or even infertility. Identifying the most suitable cell with the highest chance of fertility is a very difficult task even for embryologists. Hence, this research focuses on designing a deep learning framework to take on the challenging task of segmenting low-resolution oocyte microscopic images and accurately delineating and distinguishing the boundaries of the three important regions of the oocyte, i.e., zona pellucida (ZP), perivitelline space (PVS), and ooplasm. Then it is calculated with the key indicators in the health of each cell and compared with the true values to evaluate the level of abnormalities and select the most suitable cell for in vitro fertilization. Finally, after testing on 253 images of the test set, in binary segmentation, the average accuracy is 99.17 in ooplasm, 97.14 in PVS, and 94.78 in ZP, and in multi-class segmentation, the average accuracy is 99.30 in ooplasm, 97.36 in PVS, and 94.95 in ZP. These values have been obtained by training on 300 microscopic images of human oocytes, which have been reduced to less than half compared to previous studies.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100327"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683784","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":"3D brain tumor segmentation in MRI images using hierarchical adaptive pruning of non-tumor regions","authors":"Ali Mehrabi,&nbsp;Nasser Mehrshad","doi":"10.1016/j.ibmed.2025.100303","DOIUrl":"10.1016/j.ibmed.2025.100303","url":null,"abstract":"<div><h3>Background</h3><div>The detection of brain tumors in MRI images has significantly improved with the advent of deep learning methods. However, these approaches often suffer from high complexity, computational cost, and the need for extensive annotated training data, making them less practical for real-time and patient-centered diagnostic systems. To address these challenges, this study introduces a perceptually inspired, algorithmic method that mimics the diagnostic behavior of physicians, offering a lightweight and interpretable alternative for brain tumor segmentation.</div></div><div><h3>Method</h3><div>We propose a novel adaptive hierarchical pruning algorithm for 3D MRI brain images that iteratively removes low-intensity, non-tumor voxels based on the statistical distribution of intensities. The tumor region is identified through the comparison of the remaining pixel intensity values statistics. The pruning automatically stops when the mean and median of the remaining voxels converge, leaving the candidate tumor region.</div></div><div><h3>Results</h3><div>The proposed algorithm was evaluated on all patients of the BraTS2019 and BraTS2023 datasets, achieving segmentation accuracies of 99.1 % and 99.13 %, respectively. It demonstrated high sensitivity and specificity compared to several deep learning methods, showing robust performance across diverse patient scans.</div></div><div><h3>Conclusions</h3><div>This study demonstrates that a simple, perceptually driven segmentation algorithm can match or outperform complex deep learning models, particularly in clinical settings where lightweight, transparent, and efficient tools are essential.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100303"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319572","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":"An integrated machine learning based adaptive error minimization framework for Alzheimer's stage identification","authors":"Fahima Hossain,&nbsp;Rajib Kumar Halder,&nbsp;Mohammed Nasir Uddin","doi":"10.1016/j.ibmed.2025.100243","DOIUrl":"10.1016/j.ibmed.2025.100243","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a degenerative neurological condition that impairs cognitive functioning. Early detection is critical for slowing disease progression and limiting brain damage. Although machine learning and deep learning models help identify Alzheimer's disease, their accuracy and efficiency are widely questioned. This study provides an integrated system for classifying four AD phases from 6400 MRI scans using pre-trained neural networks and machine learning classifiers. Preprocessing steps include noise removal, image enhancement (AGCWD, Bilateral Filter), and segmentation. Intensity normalization and data augmentation methods are applied to improve model generalization. Two models are developed: the first employs pre-trained neural net-works (VGG16, VGG19, DenseNet201, ResNet50, EfficientNetV7, InceptionV3, InceptionResNetV2, and MobileNet) for both feature extraction and classification. In contrast, the second integrates features from these networks with machine learning classifiers (XGBoost, Random Forest, SVM, KNN, Gradient Boosting, AdaBoost, Decision Tree, Linear Discriminant Analysis, Logistic Regression, and Multilayer Perceptron). The second model incorporates an adaptive error minimization sys-tem for enhanced accuracy. VGG16 achieved the highest accuracy (99.61 % training and 97.94 % testing), whereas VGG19+MLP with adaptive error minimization achieved 97.08 %, exhibiting superior AD classification ability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807121","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":"ESC-UNET: A hybrid CNN and Swin Transformers for skin lesion segmentation","authors":"Anwar Jimi ,&nbsp;Nabila Zrira ,&nbsp;Oumaima Guendoul ,&nbsp;Ibtissam Benmiloud ,&nbsp;Haris Ahmad Khan ,&nbsp;Shah Nawaz","doi":"10.1016/j.ibmed.2025.100257","DOIUrl":"10.1016/j.ibmed.2025.100257","url":null,"abstract":"<div><div>One of the most important tasks in computer-aided diagnostics is the automatic segmentation of skin lesions, which plays an essential role in the early diagnosis and treatment of skin cancer. In recent years, the Convolutional Neural Network (CNN) has largely replaced other traditional methods for segmenting skin lesions. However, due to insufficient information and unclear lesion region segmentation, skin lesion image segmentation still has challenges. In this paper, we propose a novel deep medical image segmentation approach named “ESC-UNET” which combines the advantages of CNN and Transformer to effectively leverage local information and long-range dependencies to enhance medical image segmentation. In terms of the local information, we use a CNN-based encoder and decoder framework. The CNN branch mines local information from medical images using the locality of convolution processes and the pre-trained EfficientNetB5 network. As for the long-range dependencies, we build a Transformer branch that emphasizes the global context. In addition, we employ Atrous Spatial Pyramid Pooling (ASPP) to gather network-wide relevant information. The Convolution Block Attention Module (CBAM) is added to the model to promote effective features and suppress ineffective features in segmentation. We have evaluated our network using the ISIC 2016, ISIC 2017, and ISIC 2018 datasets. The results demonstrate the efficiency of the proposed model in segmenting skin lesions.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168078","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":"Development and validation of a moderate aortic stenosis disease progression model","authors":"Miguel R. Sotelo ,&nbsp;Paul Nona ,&nbsp;Loren Wagner ,&nbsp;Chris Rogers ,&nbsp;Julian Booker ,&nbsp;Efstathia Andrikopoulou","doi":"10.1016/j.ibmed.2025.100201","DOIUrl":"10.1016/j.ibmed.2025.100201","url":null,"abstract":"<div><h3>Background</h3><div>Understanding the multifactorial determinants of rapid progression in patients with aortic stenosis (AS) remains limited. We aimed to develop and validate a machine learning model (ML) for predicting rapid progression from moderate to severe AS within one year.</div></div><div><h3>Methods</h3><div>8746 patients were identified with moderate AS across seven healthcare organizations. Three ML models were trained using demographic, and echocardiographic variables, namely Random Forest, XGBoost and causal discovery-logistic regression. An ensemble model was developed integrating the aforementioned three. A total of 3355 patients formed the training and internal validation cohort. External validation was performed on 171 patients from one institution.</div></div><div><h3>Results</h3><div>An ensemble model was selected due to its superior F1 score and precision in internal validation (0.382 and 0.301, respectively). Its performance on the external validation cohort was modest (F1 score = 0.626, precision = 0.532).</div></div><div><h3>Conclusion</h3><div>An ensemble model comprising only demographic and echocardiographic variables was shown to have modest performance in predicting one-year progression from moderate to severe AS. Further validation in larger populations, along with integration of comprehensive clinical data, is crucial for broader applicability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174355","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":"Improving CNN interpretability and evaluation via alternating training and regularization in chest CT scans","authors":"Rodrigo Ramos-Díaz ,&nbsp;Jesús García-Ramírez ,&nbsp;Jimena Olveres ,&nbsp;Boris Escalante-Ramírez","doi":"10.1016/j.ibmed.2025.100211","DOIUrl":"10.1016/j.ibmed.2025.100211","url":null,"abstract":"<div><div>Interpretable machine learning is an emerging trend that holds significant importance, considering the growing impact of machine learning systems on society and human lives. Many interpretability methods are applied in CNN after training to provide deeper insights into the outcomes, but only a few have tried to promote interpretability during training. The aim of this experimental study is to investigate the interpretability of CNN. This research was applied to chest computed tomography scans, as understanding CNN predictions has particular importance in the automatic classification of medical images. We attempted to implement a CNN technique aimed at improving interpretability by relating filters in the last convolutional to specific output classes. Variations of such a technique were explored and assessed using chest CT images for classification based on the presence of lungs and lesions. A search was conducted to optimize the specific hyper-parameters necessary for the evaluated strategies. A novel strategy is proposed employing transfer learning and regularization. Models obtained with this strategy and the optimized hyperparameters were statistically compared to standard models, demonstrating greater interpretability without a significant loss in predictive accuracy. We achieved CNN models with improved interpretability, which is crucial for the development of more explainable and reliable AI systems.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388250","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":"Predicting the effect of Bevacizumab therapy in ovarian cancer from H&E whole slide images using transformer model","authors":"Md Shakhawat Hossain ,&nbsp;Munim Ahmed ,&nbsp;Md Sahilur Rahman ,&nbsp;MM Mahbubul Syeed ,&nbsp;Mohammad Faisal Uddin","doi":"10.1016/j.ibmed.2025.100231","DOIUrl":"10.1016/j.ibmed.2025.100231","url":null,"abstract":"<div><div>Ovarian cancer (OC) ranks fifth in all cancer-related fatalities in women. Epithelial ovarian cancer (EOC) is a subclass of OC, accounting for 95 % of all patients. Conventional treatment for EOC is debulking surgery with adjuvant Chemotherapy; however, in 70 % of cases, this leads to progressive resistance and tumor recurrence. The United States Food and Drug Administration (FDA) recently approved Bevacizumab therapy for EOC patients. Bevacizumab improved survival and decreased recurrence in 30 % of cases, while the rest reported side effects, which include severe hypertension (27 %), thrombocytopenia (26 %), bleeding issues (39 %), heart problems (11 %), kidney problems (7 %), intestinal perforation and delayed wound healing. Moreover, it is costly; single-cycle Bevacizumab therapy costs approximately $3266. Therefore, selecting patients for this therapy is critical due to the high cost, probable adverse effects and small beneficiaries. Several methods were proposed previously; however, they failed to attain adequate accuracy. We present an AI-driven method to predict the effect from H&amp;E whole slide image (WSI) produced from a patient's biopsy. We trained multiple CNN and transformer models using 10 × and 20 × images to predict the effect. Finally, the Data Efficient Image Transformer (DeiT) model was selected considering its high accuracy, interoperability and time efficiency. The proposed method achieved 96.60 % test accuracy and 93 % accuracy in 5-fold cross-validation and can predict the effect in less than 30 s. This method outperformed the state-of-the-art test accuracy (85.10 %) by 11 % and cross-validation accuracy (88.2 %) by 5 %. High accuracy and low prediction time ensured the efficacy of the proposed method.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601341","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":"Swarm intelligence in biomedical engineering","authors":"Seyyed Ali Zendehbad ,&nbsp;Elias Mazrooei Rad ,&nbsp;Shahryar Salmani Bajestani","doi":"10.1016/j.ibmed.2025.100308","DOIUrl":"10.1016/j.ibmed.2025.100308","url":null,"abstract":"<div><div>Swarm Intelligence (SI), a specialized branch of Artificial Intelligence (AI), is founded on the collective behaviors observed in biological systems, such as those of ants, bees, and bird flocks. This bio-inspired approach enables SI to develop computational algorithms that tackle complex problems, which traditional methods often struggle to address. Over the past few decades, SI has gained substantial traction in biomedical engineering due to its capacity to address multifaceted issues with higher adaptability and efficiency. This review presents key advancements in SI applications across three primary areas: neurorehabilitation, Alzheimer's Disease Diagnosis (ADD), and medical image processing. In neurorehabilitation, SI has played a pivotal role in improving the precision and adaptability of devices such as exoskeletons and neuroprostheses, enhancing motor function recovery for patients. Similarly, in ADD, SI algorithms have shown significant promise in analyzing neuroimaging and neurophysiological data, increasing diagnostic accuracy and enabling earlier intervention. Furthermore, in medical image processing, SI techniques have been effectively applied to tasks such as image segmentation, tumor detection, feature extraction, and artifact reduction, particularly in modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound imaging (such as Sonography). Based on the literature analyzed, SI methods have demonstrated consistent strengths in global optimization, adaptability to noisy data, and robustness in feature selection tasks when compared to traditional machine learning techniques. However, SI still faces challenges, especially regarding computational complexity, model interpretability, and limited clinical translation. Overcoming these hurdles is crucial for the full-scale adoption of this technology in clinical settings. This review not only highlights the progress in these areas but also synthesizes current limitations and future directions to guide the effective integration of SI into real-world biomedical applications.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465892","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":"Early prediction of sepsis using an XGBoost model with single time-point non-invasive vital signs and its correlation with C-reactive protein and procalcitonin: A multi-center study","authors":"Albert C. Yang ,&nbsp;Wei-Ming Ma ,&nbsp;Dung-Hung Chiang ,&nbsp;Yi-Ze Liao ,&nbsp;Hsien-Yung Lai ,&nbsp;Shu-Chuan Lin ,&nbsp;Mei-Chin Liu ,&nbsp;Kai-Ting Wen ,&nbsp;Tzong-Huei Lin ,&nbsp;Wen-Xiang Tsai ,&nbsp;Jun-Ding Zhu ,&nbsp;Ting-Yu Chen ,&nbsp;Hung-Fu Lee ,&nbsp;Pei-Hung Liao ,&nbsp;Huey-Wen Yien ,&nbsp;Chien-Ying Wang","doi":"10.1016/j.ibmed.2025.100242","DOIUrl":"10.1016/j.ibmed.2025.100242","url":null,"abstract":"<div><div>We aimed to develop an early warning system to predict sepsis based solely on single time-point and non-invasive vital signs, and to evaluate its correlation with related biomarkers, namely C-reactive protein (CRP) and Procalcitonin (PCT). We utilized retrospective data from Physionet and four medical centers in Taiwan, encompassing a total of 46,184 Intensive Care Unit (ICU) patients, to develop and validate a machine learning algorithm based on XGBoost for predicting sepsis. The model was specifically designed to use non-invasive vital signs captured at a single time point, The correlation between sepsis AI prediction model and levels of CRP and PCT was evaluated. The developed model demonstrated balanced performance across various datasets, with an average recall of 0.908 and precision of 0.577. The model's performance was further validated by the independent dataset from Cheng-Hsin General Hospital (recall: 0.986, precision: 0.585). Temperature, systolic blood pressure, and respiration rate were the top contributing predictors in the model. A significant correlation was observed between the model's sepsis predictions and elevated CRP levels, while PCT showed a less consistent pattern. Our approach, combining AI algorithms with vital sign data and its clinical relevance to CRP level, offers a more precise and timely sepsis detection, with the potential to improve care in emergency and critical care settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747482","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}