An information bottleneck-based optimal transport network for automated diagnosis of spinal diseases [0.03%]
一种基于信息瓶颈的最优传输网络用于脊柱疾病自动诊断
Minghao Shao,Haocheng Xu,Linli Li et al.
Minghao Shao et al.
Spinal diseases are common and widely impactful health issues in modern society. With the advancement of computer vision and medical image analysis, image-based automatic recognition and classification of spinal diseases have become researc...
BrainSeg: a generalized framework for comprehensive multimodal brain tissue segmentation, parcellation, and lesion labeling [0.03%]
脑部影像综合多模态脑组织分割、精细划分和病变标注通用框架
Shijie Huang,Zifeng Lian,Dengqiang Jia et al.
Shijie Huang et al.
Precise brain segmentation is fundamental for quantitative neuroimaging analysis. However, most existing methods lack generalization across the human lifespan and diverse imaging modalities, limiting their utility for Comprehensive Brain Se...
Real-time anatomy recognition in laparoscopic liver resection using video segmentation AI model [0.03%]
基于视频分割AI模型的腹腔镜肝切除术实时解剖识别
Haisu Tao,Kangwei Guo,Yijun Yang et al.
Haisu Tao et al.
Laparoscopic liver resection (LLR) is challenging due to the complex and variable intrahepatic vascular anatomy, limited surgical field of view, and lack of tactile feedback, which collectively increase the risk of intraoperative injury. Ac...
A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images [0.03%]
基于超声图像的儿童回肠末段肠套叠严重程度评估的视觉变换器深度学习模型
Jie Liu,Yue Wang,Danping Zeng et al.
Jie Liu et al.
Timely identification of children with ileocolic intussusception likely to fail air-enema reduction is critical to avoid delays and bowel perforation. However, even expert sonographers show inter-observer variability. We developed and prosp...
Psychometric characterization of human and artificial intelligence performance on cardiology residency in-service examination items [0.03%]
心血管专科住院医师考核题目的量表特征及人工智能的答题表现分析
Aykan Çelik,Tuncay Kırış,Uğur Kocabaş et al.
Aykan Çelik et al.
Large language models (LLMs) are increasingly evaluated using medical examination datasets, yet most studies emphasize overall accuracy rather than the psychometric structure of test items. We evaluated five LLMs on 199 text-only cardiology...
Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance [0.03%]
传统机器学习和深度学习算法在肥胖预测或分类中的应用：系统综述及性能比较
Mehrdad Jamali,Meysam Zarezadeh,Mohammad Vesal Bideshki et al.
Mehrdad Jamali et al.
This systematic review evaluated traditional machine learning (TML) and deep learning (DL) approaches for obesity prediction in longitudinal studies and obesity classification in cross-sectional studies. PubMed, Scopus, and Web of Science w...
Simulation of covariate and concept drift in machine learning hospital admission prediction from emergency triage [0.03%]
模拟机器学习中的协变量和概念漂移：以急诊分诊的住院预测为例
Ethan Williams,Toshi Sinha,Matthew Summerscales et al.
Ethan Williams et al.
Machine learning models that predict hospital admission at triage may support patient flow forecasting, yet the effects of covariate drift, concept drift, and retraining on long-term performance are poorly understood. We developed an Extrem...
Sleep EEG foundation models reveal within-stage microstructure that improves health screening beyond traditional stages [0.03%]
睡眠EEG基础模型揭示了传统阶段内的微观结构，可改善健康筛查效果
William Coon,Mattson Ogg
William Coon
Sleep physiology provides rich longitudinal biosignals reflecting integrated brain and systemic physiology, yet polysomnography is commonly compressed into coarse, human-defined stages. We asked whether self-supervised foundation models lea...
Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented prompting: a multicenter validation study [0.03%]
基于大型语言模型和专家知识增强提示的自动化风险评分在静脉血栓栓塞中的多中心验证研究
Jing Ma,Dingyi Wang,Yaqian Zhang et al.
Jing Ma et al.
Venous thromboembolism (VTE) is a common but preventable complication in hospitalized patients, yet standardized risk scoring using unstructured electronic health records (EHRs) remains challenging. We retrospectively analyzed anonymized EH...
AI-driven diagnostic algorithm enhances early detection of paroxysmal nocturnal hemoglobinuria in real-world settings [0.03%]
基于人工智能的诊断算法可提高阵发性夜间血红蛋白尿在现实世界中的早期检出率
Robert Dewor,Michal J Dabrowski,Łukasz Więcek et al.
Robert Dewor et al.
Paroxysmal nocturnal haemoglobinuria (PNH) is a rare, life-threatening hematologic disease with diagnostic delays exceeding 5 years in 24% of cases. We developed and deployed an artificial intelligence algorithm analyzing structured and uns...