A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT by Cong Zhang, Xin Gao, Xuebin Zheng, Jun Xie, Gang Feng, Yunchao Bao, Pengchen Gu, Chuan He, Ruimin Wang, Jiahe Tian

“…Automated identification and localization algorithms were applied to select predefined pairs of PET and CT slices from whole-body images. …”
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Using Video Cameras to Assess Physical Activity and Other Well-Being Behaviors in Urban Environments: Feasibility, Reliability, and Participant Reactivity Studies by Jack S Benton, James Evans, Jamie Anderson, David P French

“…It also provides a rigorous foundation for developing more scalable automated computer vision algorithms for assessing human behaviors.…”
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Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease by Heather Mary-Kathleen Kosick, Chris McIntosh, Chinmay Bera, Mina Fakhriyehasl, Mohamed Shengir, Oyedele Adeyi, Leila Amiri, Giada Sebastiani, Kartik Jhaveri, Keyur Patel

“…We aimed to evaluate machine learning (ML) algorithms based on simple NIT and US for prediction of adverse liver-related outcomes in MASLD. …”
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Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition by Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee

“…Specifically, we perform few-shot action recognition tasks under FL settings, enhancing privacy protection while maintaining efficient communication and storage. …”
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Research on the spatial layout of the elderly care industry based on explainable machine learning: A case study of Hangzhou(基于可解释性机器学习的养老产业空间布局研究)... by 曾笑奇(ZENG Xiaoqi), 赵秋皓(ZHAO Qiuhao), 冯友建(FENG Youjian)

“…The results indicate that∶ (1) SHAP effectively interprets the results of the machine learning algorithms; (2) Most factors, such as population size and government institutions, positively impact the layout of elderly care facilities, whereas other factors, such as financial service facilities, negatively impact their layout; (3) Future construction of elderly care facilities in Hangzhou should prioritize the central districts of Shangcheng and Gongshu as well as their surrounding areas, while development in Fuyang and Lin'an districts could be moderated. …”
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Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy by Wu Tenghui, Liu Xinyi, Si Ziyi, Zhang Yanting, Ma Ziqian, Zhu Yiwen, Gan Ling

“…Multiple machine learning algorithms were employed to model and validate the diagnostic performance of different types of features. …”
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Energy-Efficient Recessed-Source/Drain SOI Feedback FET-Based Oscillators and Coupled Networks for Neuromorphic Computing by Sasi Kiran Suddarsi, Dhanaraj Kakkanattu Jagalchandran, Gopi Krishna Saramekala

“…Due to the controlled oscillations of a coupled network, our proposed Re-S/D SOI FBFET oscillator presents a promising avenue for efficiently implementing neuromorphic computing algorithms.…”
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Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India by Satiprasad Sahoo, Chiranjit Singha, Ajit Govind

“…The current study focuses on applying machine learning approaches to forecast future Kharif rice yield gaps in eastern India while accounting for climate change implications. …”
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Prediction of Cover–Subsidence Sinkhole Volume Using Fibre Bragg Grating Strain Sensor Data by Wesley B. Richardson, Suné von Solms, Johan Meyer, Charis Harley

“…It was found that statistical techniques and machine-learning algorithms that assume normality are inappropriate when performing phase classification and volume regression tasks on the cover–subsidence sinkhole when given fibre Bragg grating sensor’s strain data. …”
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The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis by Wenjing Fan, Zhiping Wu, Wangyang Zhao, Luzhu Jia, Shuze Li, Wei Wei, Xin Chen

“…The comprehensive SE and SP of ML are 78% (95% CI: 69–85%) and 85% (78–90%), respectively, while the AUC is 0.89 (0.86–0.91). The SE and SP of DL was 87% (95% CI: 80–92%) and 75% (67–81%), respectively, with an AUC of 0.88 (0.85–0.91). …”
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Replace government healthcare with patient-controlled health care by Deane Waldman

“…This is caused by low reimbursement rates, overly bureaucratic verification procedures for obtaining payment, overregulation of requirements for doctor-patient relations and procedures for reviewing medical errors, the need to comply with population-based clinical algorithms, etc. It leads to a decrease in the quality of medical care, an increase in patient deaths while waiting for medical care, the risk of disease complications due to delays in diagnosis and timely treatment, ignoring the needs of unique, individual patients, and an increase in the likelihood of medical errors. …”
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Deepfake Audio Detection for Urdu Language Using Deep Neural Networks by Omair Ahmad, Muhammad Sohail Khan, Salman Jan, Inayat Khan

“…Therefore, developing effective algorithms to distinguish fake audio from real audio is critical to preventing such frauds. …”
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Intraoperative Nerve Mapping and Visualization System: Optimal Neurological Function Preservation in Schwannoma Surgery by Wenjianlong Zhou, Xiaoyi Shi, Qin Xu, Xiangxiang Liu, Junshi Li, Hui Qiao, Lirui Yang, Baowang Li, Liangpeng Chen, Yuan Zhang, Xiudong Guan, Shunchang Ma, Zhongyan Wang, Linhao Yuan, Jiang Li, Tieqiang Zhang, Deling Li, Dong Huang, Zhihong Li, Wang Jia

“…For surgical guidance, we developed algorithms enabling real‐time intraoperative neuro‐mapping. …”
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Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma by Xiang Li, Donghao Yin, Jiahao Geng, Yanyu Xu, Zijing Xu, Xuemeng Yang, Quanwei Li, Zimeng Shang, Zhiyun Yang, Zhong Xu, Jiabo Wang, Enxiang Zhang, Xinhua Song

“…Based on prognosis-related DEGs, patients and cells were stratified into high- and low-expression groups using corresponding computational algorithms. The intersecting DEGs from both datasets were analyzed using univariate Cox regression, and a prognostic risk score model was constructed through machine learning algorithms. …”
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Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective stu... by Zixiang Pang, Jiawei Liang, Jiayi Chen, Yangqin Ou, Qinmian Wu, Shengsheng Huang, Shengbin Huang, Yuanming Chen

“…Consensus feature selection across the seven top-performing ML algorithms identified AISI, SII, CALLY and SLSI as independent predictors of SSI. …”
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts by Salwa Hassanein, Salwa Hassanein, Rabie Adel El Arab, Rabie Adel El Arab, Amany Abdrbo, Mohammad S. Abu-Mahfouz, Mastoura Khames Farag Gaballah, Mohamed Mahmoud Seweid, Mohamed Mahmoud Seweid, Mohammed Almari, Husam Alzghoul

“…Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…AI‑driven multi‑omic integration accelerated epitope mapping, shrinking discovery timelines by months, while predictive analytics optimized manufacturing workflows and supply‑chain operations (including temperature‑controlled, “cold‑chain” logistics). …”
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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Feature selection was performed using univariate Cox regression (p ≤ 0.05), and four ML algorithms—Random Survival Forest (RSF), Gradient Boosting Machine (GBM), CoxBoost, and XGBoost—were applied to develop recurrence prediction models. …”
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Image quality improvement in head and neck angiography based on dual-energy CT and deep learning by He Zhang, Lulu Zhang, Juan Long, He Zhang, Xiaonan Sun, Shuai Zhang, Aiyun Sun, Shenman Qiu, Yankai Meng, Tao Ding, Chunfeng Hu, Kai Xu

“…Abstract Objective Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA). …”
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Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review by Tianyi Liu, Andrew J. Krentz, Zhiqiang Huo, Vasa Ćurčin

“…Of these, 10 were systematic reviews (four incorporating meta-analyses), while 12 constituted narrative reviews, with the majority published post-2020. …”
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