Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explain... by Sami Alrabie, Ahmed Barnawi

“…First, we observed that automatic heart-sound segmentation algorithms—commonly used for data augmentation—produce varying outcomes, raising concerns about the accuracy of both the segmentation process and the resulting classification performance. …”
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OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification by Irshad Khalil, Asif Mehmood, Hyunchul Kim, Jungsuk Kim

“…Traditional approaches rely on machine learning for feature extraction, while deep learning methods utilize data-driven classification model training. In recent years, Deep Learning (DL) and Machine Learning (ML) algorithms have become essential tools, particularly in medical image classification, and are widely used to classify and identify various diseases. …”
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Bladder drainage methods for acute urinary retention due to benign prostatic hyperplasia: patient-preference analysis by A. I. Volnukhin, D. Yu. Pushkar, V. A. Malkhasyan

“…After completing the questionnaire, all the patients were shown a video on self-catheterization and asked to re-take the questionnaire after watching it. This allowed evaluating the impact of the training video on patients' preferences and their confidence in performing self-catheterization.Results. …”
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Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics. by Shuang Liu, Xuanpeng Zhao, Qingyan Meng, Baoshan Li

“…Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). …”
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Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer by Tianle Xue, Yunpeng Chen, Xiaomeng Li, Zhixiang Zhou, Qiyang Chen

“…The diagnostic performance of the candidate genes was evaluated using receiver operating characteristic (ROC) analyses in both training and validation cohorts. …”
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Pure data correction enhancing remote sensing image classification with a lightweight ensemble model by Huaxiang Song, Hanglu Xie, Yingying Duan, Xinyi Xie, Fang Gan, Wei Wang, Jinling Liu

“…Furthermore, we propose a straightforward algorithm to generate an ensemble network composed of two components, serving as the proposed lightweight classifier. …”
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A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy by Yuling Pan, Qingkun Fan, Yu Liang, Yunfan Liu, Haihang You, Chunzi Liang

“…We divided the dataset into training and testing cohorts, applying ML algorithms such as logistic regression, random forest, and XGBoost for automated learning and prediction. …”
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Open-Set Recognition of Environmental Sound Based on KDE-GAN and Attractor–Reciprocal Point Learning by Jiakuan Wu, Nan Wang, Huajie Hong, Wei Wang, Kunsheng Xing, Yujie Jiang

“…While open-set recognition algorithms have been extensively explored in computer vision, their application to environmental sound analysis remains understudied. …”
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Forecasting Tunnel-Induced Ground Settlement: A Hybrid Deep Learning Approach and Traditional Statistical Techniques With Sensor Data by Syed Mujtaba Hussaine, Linlong Mu, Yimin Lu, Syed Sajid Hussain

“…Additionally, the statistical Autoregressive Integrated Moving Average (ARIMA)/Seasonal ARIMA (SARIMA) models were enhanced through seasonality removal, automated model selection using the auto_arima algorithm, and parameter fine-tuning via grid search to improve their predictive accuracy. …”
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Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol by Fatemeh Shabani, Ata Jodeiri, Sakineh Mohammad‑Alizadeh‑Charandabi, Fatemeh Abbasalizadeh, Jafar Tanha, Mojgan Mirghafourvand

“…In Phase 2, an artificial intelligence model will be developed using machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and neural networks, followed by model training, validation, and integration into a user-friendly application. …”
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Multiparametric radiomics signature for predicting molecular genotypes in adult-type diffuse gliomas utilizing 18F-FET PET/MRI by Jie Bai, Bixiao Cui, Fengqi Li, Xin Han, Hongwei Yang, Jie Lu

“…A total of 994 radiomic features were extracted from these specified modalities. The Naive Bayesian algorithm with five-fold validation was trained to develop prediction models for the IDH, TERT, and MGMT genotypes and to calculate the radiomics score (Rad-Score). …”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…Six machine learning models, including Support Vector Machine, Logistic Regression, Random Forest, XGBoost, Artificial Neural Network, and AdaBoost, were trained using extensive hyperparameter tuning. Comprehensive evaluations were conducted based on multiple metrics, including Area Under the Curve (AUC), accuracy, F1 score, sensitivity, specificity, Positive Predictive Value, and Negative Predictive Value. …”
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Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

“…Data were extracted from electronic medical records, including demographics, clinical variables, ultrasound findings (bowel wall thickness, edema, gas location, peristalsis, seroperitoneum), and serological markers (WBC, neutrophil count, CRP, ALP, albumin, procalcitonin, platelet count, INR, hemoglobin). Twelve ML algorithms were evaluated using 10-fold cross-validation on a training set (70%). …”
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EXPRESSION OF MATRIX METALLOPROTEINASES IN LACRIMAL FLUID AND GENE POLYMORPHISM OF COMPLEMENT FACTOR H (CFH) IN PATIENTS WITH PRIMARY OPEN-ANGLE GLAUCOMA by V. A. Sokolov, V. G. Likhvantseva, O. N. Levanova, A. A. Nikiforov, V. A. Vygodin

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Development and validation of risk prediction models for acute kidney disease in gout patients: a retrospective study using machine learning by Siqi Jiang, Lingyu Xu, Chenyu Li, Xinyuan Wang, Chen Guan, Yanfei Wang, Lin Che, Xuefei Shen, Yan Xu

“…The dataset was split into 80% for model training and 20% for testing model performance. Nine machine learning algorithms were evaluated, with performance assessed using metrics, such as AUROC, precision, recall, and F1 score. …”
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Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based cal... by Yan Zhang, Nan Wang, Yuxin Qiu, Yingxiao Jiang, Peiyan Qin, Xiaoxiao Wang, Yang Li, Xiangdi Meng, Furong Hao

“…Thirteen ML algorithms were trained and compared to determine the optimal model. …”
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A Hybrid Method Combining Variational Mode Decomposition and Deep Neural Networks for Predicting PM2.5 Concentration in China by Senlin Li, Bo Tang, Xiaowu Deng

“…To address this issue, this study introduces a hybrid parallel method (VDPS) that combines variational mode decomposition (VMD) with single deep neural networks for PM2.5 concentration prediction. The VMD algorithm, which can decompose signals adaptively and possesses a certain level of robustness to noise and interference, decomposes complex time series into intrinsic mode functions (IMFs), which are then used as inputs for training a deep neural network prediction model. …”
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M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy by Nalini Schaduangrat, Hathaichanok Chuntakaruk, Thanyada Rungrotmongkol, Pakpoom Mookdarsanit, Watshara Shoombuatong

“…Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. …”
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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…Abstract Key parameters for evaluating shale reservoirs include total organic carbon (TOC), thermal maturity, and hardness, the latter influencing fracture development and being crucial for managing ultralow permeability reservoirs. …”
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Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study by Aurélie Jourdan, Aurélie Jourdan, Caroline Dania, Caroline Dania, Maxime Cambournac, Maxime Cambournac

“…Cine loops were analyzed using the Butterfly Auto B-line Counter and reviewed independently by two POCUS-trained clinicians, each blinded to the AI results and to the other's evaluation.ResultsThe AI algorithm failed to provide a B-line count in 14.2% of cineloops overall, with failures occurring in 11.8% of the suspected CPE group and 2.4% of the non-CPE group. …”
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