Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics [0.03%]
用于评估跨患者的人口统计学的皮肤病变类基础AI模型预测公平性的保角不确定性量化方法
Swarnava Bhattacharyya,Umapada Pal,Tapabrata Chakraborti
Swarnava Bhattacharyya
Deep learning based diagnostic AI systems based on medical images are starting to provide similar performance as human experts. However, these data-hungry complex systems are inherently black boxes and therefore slow to be adopted for high-...
Toward intelligent clinical support for personalized sport training rehabilitation via large language models [0.03%]
基于大规模语言模型的智能临床支持个性化运动训练康复研究
Songbin Wang,Zhiqing Bai,Yihe Gai
Songbin Wang
Effective sport-training rehabilitation demands exercise prescriptions that adapt to each patient's changing symptoms and adherence patterns, yet most recommender systems rely on either numerical logs or handcrafted rules, failing to exploi...
A vision-language model-based approach for lung cancer diagnosis using lossless 3D CT images: evaluation of GPT-4.1 and GPT-4o for patient-level malignancy assessment [0.03%]
一种基于愿景语言模型的肺癌诊断方法，使用无损3DCT图像：评估GPT-4.1和GPT-4o在患者层面恶性评估中的应用
Ning Shi,Zhenpeng Liu,Zhenzhen Wan et al.
Ning Shi et al.
Purpose: Large vision-language models (VLMs), such as GPT-4.1 and GPT-4o, have shown strong potential in medical tasks. However, their application in lossless 3D medical image analysis is still underexplored. This study p...
DA3-LUNGNET: a multi-stage deep framework with adaptive attention for early detection of subcentimeter pulmonary nodules [0.03%]
一种带有自适应注意力的多阶段深度框架用于亚厘米肺结节早期检测
Bin Zhong,Runan Zhang,Shuai Yu et al.
Bin Zhong et al.
Early and reliable detection of subcentimeter pulmonary nodules remains a major bottleneck in low-dose CT-based lung cancer screening due to high miss rates, vascular-adhesion-induced false positives, and insufficient multi-scale feature fu...
An interpretable approach for schizophrenia classification using fMRI and sMRI features [0.03%]
基于功能磁共振和结构磁共振特征的可解释精神分裂症分类方法
Archita Chakraborty,Linkon Chowdhury,Selvarajah Thuseethan et al.
Archita Chakraborty et al.
Schizophrenia is a neurodivergent disorder that can be studied using neuroimaging-based machine learning models for early diagnosis and classification. Despite advances in neuroimaging, a gap remains in visualising multimodal magnetic reson...
Heartbeat audio signal analysis for cardiovascular abnormality diagnosis using neural-networks [0.03%]
基于神经网络的心音信号分析在心血管异常诊断中的应用研究
Vibha Jain,Ishwari Singh Rajput,Aditya Gupta et al.
Vibha Jain et al.
Background: Cardiovascular diseases (CVDs) represent the leading cause of mortality globally, accounting for more than 17.9 million fatalities annually. The prompt and precise forecasting of cardiovascular diseases is cru...
Design and validation of a responsible artificial intelligence-based system for the referral of diabetic retinopathy patients [0.03%]
一种用于糖尿病视网膜病变患者转诊的责任型人工智能系统的研发与验证
E Ulises Moya-Sánchez,Abraham Sánchez-Perez,Alejandro Zarate-Macías et al.
E Ulises Moya-Sánchez et al.
Diabetic Retinopathy (DR) is a leading cause of vision loss among working-age individuals. Early detection can reduce the risk of vision loss by up to 95%, yet a shortage of retinologists and logistical challenges often delay the DR detecti...
Interpretable large language models for early prediction of antimicrobial multidrug resistance [0.03%]
可解释的大语言模型在抗菌多药耐药的早期预测中的应用
Lucía Carmona-Martos,Paula Martín-Palomeque,Óscar Escudero-Arnanz et al.
Lucía Carmona-Martos et al.
Purpose: The growing burden of Antimicrobial Resistance (AMR) in Intensive Care Units (ICUs) poses a significant threat to global health, increasing patient mortality, morbidity, and healthcare costs. Early prediction of ...
Utilizing multimodal models to forecast Alzheimer's disease progression and clinical subtypes [0.03%]
利用多模态模型预测阿尔茨海默病的进展和临床亚型
Hao Ren,Fengshi Jing,Yue Zhou et al.
Hao Ren et al.
Background: Alzheimer's disease (AD) exhibits highly heterogeneous clinical courses. Early, accurate prediction and subgroup identification remain challenging due to reliance on single-modality data and coarse subtype sch...
Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography [0.03%]
基于常规和深度学习去噪技术的磁共振血管成像图像增强与分割
Daniela Herrera,Gilberto Ochoa-Ruiz,Christian Stephan-Otto et al.
Daniela Herrera et al.
The study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types o...