Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study by Yuqi Yan, Yuanzhen Liu, Yao Wang, Tian Jiang, Jiayu Xie, Yahan Zhou, Xin Liu, Meiying Yan, Qiuqing Zheng, Haifei Xu, Jinxiao Chen, Lin Sui, Chen Chen, RongRong Ru, Kai Wang, Anli Zhao, Shiyan Li, Ying Zhu, Yang Zhang, Vicky Yang Wang, Dong Xu

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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…Functional enrichment analysis associated RCN1 with cell cycle regulation and immune-related pathways. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…The clinical feature prediction model using the GBM algorithm had an AUC of 0.819 and an accuracy of 0.739. …”
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Spatial Information of Somatosensory Stimuli in the Brain: Multivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data by In-Seon Lee, Won-mo Jung, Hi-Joon Park, Younbyoung Chae

“…We estimated the significance of the classification accuracy using a permutation test with randomly labeled training data (n=10,000). Searchlight analysis was conducted to identify brain regions associated with significantly higher accuracy compared to predictions based on chance as obtained from a random classifier. …”
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Recommendations for All-Round Newborns and Infants Hearing Screening in Russian Federation by S. S. Chibisova, G. S. Tufatulin, L. S. Namazova-Baranova, I. V. Koroleva, E. R. Tsygankova, T. G. Markova, N. N. Volodin, G. A. Tavartkiladze

“…Maintenance of all-round newborns hearing screening algorithm will allow us to avoid the diagnosis delay, to start the rehabilitation earlier and further to significantly increase the efficacy of modern high-tech methods for correcting hearing disorders in children. …”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…To advance DL‐based hydrogeological modeling, future research should focus on enhancing data availability through data fusion and public databases, improving model interpretability using physics‐informed and explainable DL techniques, and developing more efficient algorithms for training large‐scale models. Additionally, exploring new computational paradigms, such as quantum computing, could provide revolutionary solutions for handling the computational challenges associated with training complex models. …”
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Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning by Zhou An, Meichun Zeng, Xianhua Wang

“…To develop a diagnostic model for LNs, we investigated 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on the training set, followed by external validation of the test set. …”
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Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers by Nguyen Le BPharm, Sola Han PhD, Ahmed S. Kenawy MS, Yeijin Kim MS, Chanhyun Park PhD

“…MACE included acute myocardial infarction, ischemic heart disease, stroke, heart failure, revascularization, malignant arrhythmias, and cardiovascular-related death. Six ML algorithms (L2-Logistic regression, Support Vector Machine, Complement Naïve Bayes, Random Forest, XGBoost, and CatBoost) were trained on 2017-2018 data and tested on 2019 data. …”
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Efficient Joint Transmit and Receive Beam Alignment via Sequential CNN LSTM Networks by Takumi Yoshida, Koji Ishibashi, Hiroki Iimori, Paulo Valente Klaine, Szabolcs Malomsoky

“…However, to prevent misalignment due to user equipment (UE) mobility, frequent beam training is required, resulting in significant training overhead. …”
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Multidimensional geographic factors behind conflicts: a case study in Sudan by Yu Gong, Xi Li, Samir Belabbes, Luca Dell'Oro, Yuanxi Ru

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Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging by Jorge Alberto Leal, Luis Hernan Ochoa, Carmen Cecilia Contreras

“…The second SVM was also trained with nuclear logs, resistivity and fractal dimension, but in this case, with information of intervals composed of calcareous shales interbedded with limestone, recognizing automatically these rock associations during classification stage without interpretations of a geologist as input data. …”
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Advancements in artificial intelligence transforming medical education: a comprehensive overview by Aliasghar Khakpaki

“…They support clinical decision-making and procedural skills training while addressing diverse learner needs. However, ethical issues like data privacy, algorithmic biases, and equitable access, coupled with challenges like faculty resistance and technological infrastructure gaps, limit broader adoption. …”
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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN by Tawfikur Rahman, Rasel Ahommed, Nibedita Deb, Utpal Kanti Das, Md. Moniruzzaman, Md. Alamgir Bhuiyan, Farzana Sultana, Md. Kamruzzaman Kausar

“…The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency.Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. …”
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Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, E... by Daniel Niguse Mamo, Tesfahun Melese Yilma, Makda Fekadie Tewelgne, Yakub Sebastian, Tilahun Bizuayehu, Mequannent Sharew Melaku, Agmasie Damtew Walle

“…Then, seven supervised classification machine-learning algorithms for model development were trained. The performances of the predictive models were evaluated using accuracy, sensitivity, specificity, precision, f1-score, and AUC. …”
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A deep learning-orchestrated garlic routing architecture for secure telesurgery operations in healthcare 4.0 by Kavit Shah, Nilesh Kumar Jadav, Rajesh Gupta, Sucheta Gupta, Sudeep Tanwar, Joel J.P.C. Rodrigues, Fayez Alqahtani, Amr Tolba

“…A standard sensor dataset is utilized to train different AI algorithms, such as Long Short Term Memory (LSTM) and Gated Recurrent Neural Networks (GRU), for classifying malicious and non-malicious telesurgery data. …”
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Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

“…The SCZ versus VLOSLP model achieved perfect discrimination (AUC = 1.0) in both training and test sets, with substantial clinical utility. …”
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Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants by Aftab Siddique, Sudhanshu S. Panda, Sophia Khan, Seymone T. Dargan, Savana Lewis, India Carter, Jan A. Van Wyk, Ajit K. Mahapatra, Eric R. Morgan, Thomas H. Terrill

“…The study encompassed 75 adult male Spanish goats, which underwent PCV testing to ascertain their PCV ranges and their association with anemic conditions. Using artificial intelligence-powered machine learning algorithms, an advanced, easy-to-use sensor was developed for rapidly alerting farmers as to low red blood cell count of their animals in this way to enable timely medical intervention. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…We aimed to detect laboratory-induced stress using multimodal data and identify challenges researchers may encounter when conducting a similar study.MethodsWe conducted a preliminary exploration of performance of a machine-learning algorithm trained on multimodal data, namely visual, acoustic, verbal, and physiological features, in its ability to detect stress severity following a partially automated online version of the Trier Social Stress Test. …”
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Normalizing flow-assisted nested sampling on Type-II Seesaw model by Rajneil Baruah, Subhadeep Mondal, Sunando Kumar Patra, Satyajit Roy

“…All associated data, figures, and trained ML models can be found here:GitHub…”
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Development of GUI-Driven AI Deep Learning Platform for Predicting Warpage Behavior of Fan-Out Wafer-Level Packaging by Ching-Feng Yu, Jr-Wei Peng, Chih-Cheng Hsiao, Chin-Hung Wang, Wei-Chung Lo

“…Traditional electronic engineers often face difficulties in implementing AI-driven models due to the specialized programming and algorithmic expertise required. To overcome this, the platform incorporates a graphical user interface (GUI) that simplifies the design, training, and operation of deep learning models. …”
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