Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules by Linlin Shao, Lili Zhang, Lifang Liu, Fangfang Sun, Hongyu Li, Tongfeng Liu, Feng Hu, Lirong Zhao

“…Furthermore, the multimodal radiomics model integrating clinical data (clinical+US+CEUS radiomics model) achieved significantly improved diagnostic efficacy, with an AUC of 0.967, along with accuracy, sensitivity, specificity, and F1-score values of 0.815, 0.823, 0.792, and 0.884, respectively.ConclusionOur study developed a high-performance multimodal diagnostic model through the innovative integration of radiomic features from three critical CEUS timepoints combined with conventional ultrasound and clinical data, establishing a novel decision-support tool for accurate noninvasive classification of C-TIRADS 4 thyroid nodules. …”
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Dynamic Insights: Unraveling Public Demand Evolution in Health Emergencies Through Integrated Language Models and Spatial-Temporal Analysis by Zhang Y, Fu L, Guo X, Li M

“…Therefore, the government must promptly grasp and leverage public demands information to enhance the effectiveness and efficiency of health emergency management, that is planned to better deal with the outbreak and meet the medical demands of the public.Methods: This study employs dynamic topic mining and knowledge graph construction to analyze public demands, presenting a spatial-temporal evolution analysis method for emergencies based on EBU models. EBU models are three large language models, including ERNIE, BERTopic, and UIE.Results: The data analysis of Shanghai’s city closure and control during the COVID-19 epidemic has verified that this method can simplify the labeling and training process, and can use massive social media data to quickly, comprehensively, and accurately analyze public demands from both time and space dimensions. …”
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Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis by Faisal Abdulaziz Almisned, Natacha Usanase, Dilber Uzun Ozsahin, Ilker Ozsahin

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Enhancing stroke prediction models: A mixing of data augmentation and transfer learning for small-scale dataset in machine learning by Imam Tahyudin, Ade Nurhopipah, Ades Tikaningsih, Puji Lestari, Yaya Suryana, Edi Winarko, Eko Winarto, Nazwan Haza, Hidetaka Nambo

“…The classification models employed in this study were four algorithms: Random Forest, Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting. …”
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A nurse-led nomogram for predicting the risk of myasthenic crisis in patients with myasthenia gravis and bulbar weakness by Huimin Dong, Mengna Li, Mei Ma, Guoyan Qi

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Enhancing Skin Cancer Diagnosis Through Fine-Tuning of Pretrained Models: A Two-Phase Transfer Learning Approach by Entesar Hamed I. Eliwa

“…The VGG16 model, after fine-tuning, achieved the highest test set accuracy of 99.3%, highlighting its potential for highly accurate skin cancer classification. …”
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Dual-Attention-Enhanced MobileViT Network: A Lightweight Model for Rice Disease Identification in Field-Captured Images by Meng Zhang, Zichao Lin, Shuqi Tang, Chenjie Lin, Liping Zhang, Wei Dong, Nan Zhong

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Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers by Vijoleta Vrhovac, Marko Orošnjak, Kristina Ristić, Nemanja Sremčev, Mitar Jocanović, Jelena Spajić, Nebojša Brkljač

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Diagnosis of non-puerperal mastitis based on “whole tongue” features: non-invasive biomarker mining and diagnostic model construction by Siyuan Tu, Yulian Yin, Lina Ma, Hongfeng Chen, Meina Ye

“…Based on clinical, imaging, and microbial features, three machine learning models—logistic regression (LR), support vector machine (SVM), and gradient boosting decision tree (GBDT)—were trained to distinguish NPM.ResultsThe GBDT model achieved a superior diagnostic performance (AUROC = 0.98, accuracy = 0.95, and specificity = 0.95), outperforming the LR (AUROC = 0.98, accuracy = 0.95, and specificity = 0.90) and SVM models (AUROC = 0.87, accuracy = 0.80, and specificity = 0.75). …”
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FastColitisDetector-XAI: An efficient AI model utilizing sparse Autoencoder with explainable AI for ulcerative colitis diagnosis by Sumedh Vithalrao Dhole, Sangeeta R. Chougule

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Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms by Yuyang Chen, Jiansheng Zhong, Haixiang Li, Kunzhe Lin, Liangfeng Wei, Shousen Wang

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Application of models in predicting sputum conversion after two months of intensive phase of anti-tuberculosis medication in Taraba State by D. N Kevin, S. Bello, B. M Gabdebo, A. F Ayodele, A Walter, U. E Etim

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An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging by N. A. S. Vinoth, J. Kalaivani, R. Madonna Arieth, S. Sivasakthiselvan, Gi-Cheon Park, Gyanendra Prasad Joshi, Woong Cho

“…Furthermore, fusion models such as CapsNet, EffcientNetV2, and MobileNet-V3 Large are employed for the feature extraction. …”
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Validation of a deep learning model for cattle lameness detection: Comparison of human scorer performance and automated gait analysis by Sandra Reitmaier, Serhan Narli

“…Despite this, mild lameness remained difficult to classify for both humans and AI, particularly between LCS-2 and LCS-3. The findings support the potential of AI systems to deliver consistent, scalable, and objective lameness detection, with future applications in real time farm settings to improve welfare outcomes and decision making.…”
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Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk by Francesca Galati, Roberto Maroncelli, Chiara De Nardo, Lucia Testa, Gloria Barcaroli, Veronica Rizzo, Giuliana Moffa, Federica Pediconi

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HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot by Xiaoming Li, Yang Zhou, Yongguang Li, Shiqi Wang, Wenxue Bian, Hongmin Sun

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An Improved Optimal Cloud Entropy Extension Cloud Model for the Risk Assessment of Soft Rock Tunnels in Fault Fracture Zones by Shuangqing Ma, Yongli Xie, Junling Qiu, Jinxing Lai, Hao Sun

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Enhancing Student Management Through Hybrid Machine Learning and Rough Set Models: A Framework for Positive Learning Environments by Ateeq Ur Rehman Butt, Hamid Ali, Muhammad Asif, Hessa Alfraihi, Mohamad Khairi Ishak, Khalid Ammar

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Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble-based random forest model by Mohammad Mamun, Safiul Haque Chowdhury, Muhammad Minoar Hossain, M.R. Khatun, Sadiq Iqbal

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A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. by Tao Yu, Wei Huang, Xin Tang, Duosi Zheng

“…In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. …”
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