A Scoping Review of Interpretable AI Model Approaches in Echocardiography: Frameworks, Model Types, Explanation Strategies, and Evaluation Practices [0.03%]
一种可解释的人工智能模型在超声心动图中的方法综述：框架、模型类型、解释策略和评估实践
Ryoma Ishii,Roger A Edwards,Patrick E Gelbach et al.
Ryoma Ishii et al.
Echocardiography is a central modality for cardiac diagnosis; rising clinical demand and advances in machine learning have accelerated AI development for automated analysis. As these systems may influence high-stakes decisions, model interp...
Deep Learning-Based Classification of Facial Profile Convexity from Profile Photographs in Orthodontics: An Artificial Intelligence Study [0.03%]
基于深度学习的正畸侧面照面部凸度分类：一项人工智能研究
Hoori Mirmohammadsadeghi,Yasamin Vazirizadeh,Hirad Rokni et al.
Hoori Mirmohammadsadeghi et al.
The soft-tissue facial profile is a cornerstone of orthodontic diagnosis and treatment planning, strongly influencing facial esthetics and patient satisfaction. This study aimed to develop and evaluate a deep learning-based framework for au...
Radiomics Integrated with Deep Learning Based on Contrast-Enhanced MRI for Predicting Short-Term Efficacy of Drug-Eluting Beads Transarterial Chemoembolization in Hepatocellular Carcinoma from Multicenter Retrospective Study [0.03%]
基于多中心回顾性研究的对比增强MRI集成的放射组学结合深度学习预测药物洗脱珠载化疗栓塞治疗肝细胞癌短期疗效
Zihan Xi,Yuchi Tian,Lulu Liu et al.
Zihan Xi et al.
We aimed to develop and validate a predictive model combining radiomics, deep learning, and clinical features for the preoperative prediction of the short-term efficacy of initial drug-eluting bead transarterial chemoembolization (DEB-TACE)...
Planning CT-Guided Dual-Branch Attention GAN for Longitudinal CBCT Outpainting in Adaptive Radiotherapy [0.03%]
基于计划CT的双重分支注意力机制生成对抗网络的纵向CBCT外推 painting在自适应放疗中的应用
Soyoung Chung,Jin Ho Kim,Min Gyo Chung et al.
Soyoung Chung et al.
Limited longitudinal field of view in cone-beam computed tomography (CBCT) remains a significant challenge for image-guided adaptive radiotherapy. We developed a deep learning-based CBCT outpainting framework that synthesizes anatomically c...
Benchmarking Deep Segmentation Architectures for Histopathological Nuclei Segmentation: A Controlled Study of Transfer Learning, Robustness, and Efficiency Across CNN, Transformer, and Hybrid Models [0.03%]
基于CNN、Transformer和混合模型的深度分割架构在组织学细胞核分割任务上的基准测试：迁移学习、鲁棒性和效率的控制实验研究
Abdulkadir Gulsen
Abdulkadir Gulsen
Accurate nuclei segmentation is essential for quantitative histopathological analysis and downstream clinical applications such as cancer grading, biomarker assessment, and prognostic modeling. Although deep learning has substantially advan...
A Three-Slice Deep Learning-Radiomics Nomogram for Challenging Renal Mass Cases: A Double-Center Study [0.03%]
基于深度学习影像组学列线图的疑难肾占位性病变诊断模型研究：一项双中心研究
Yu Shu,Hangzhe Sun,Jiayue Zhang et al.
Yu Shu et al.
To determine the optimal spatial-depth architecture for deep learning-radiomics (DLR) differentiation of fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC), and to validate the resulting integrated nomogram under ...
TempoSafe-CVS: A Temporal Multi-scale Deep Learning Framework for Automated Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy [0.03%]
一种自动评估腹腔镜胆囊切除术安全关键视图的时空多尺度深度学习框架
Hafsa Gulzar,Deboch Eyob Abera,Muhammad Zubair Nawaz et al.
Hafsa Gulzar et al.
Accurate assessment of the critical view of safety (CVS) is essential for preventing bile duct injuries during laparoscopic cholecystectomy. Existing artificial intelligence approaches primarily rely on static frame-level analysis and often...
Fully Automated Segmentation of Anatomical Subregions of the Thoracolumbar Spine on Computed Tomography [0.03%]
全自动 CT 扫描腰椎解剖亚区域分割
Raffaele Da Mutten,Sven Theiler,Massimo Bottini et al.
Raffaele Da Mutten et al.
Segmentations of the vertebral column that include anatomical subregions can be used for patient education, pedicle screw planning, or radiomic feature extraction for spinal surgery. Deep learning has proven successful in tackling medical i...
CONRep: Uncertainty-Aware Vision-Language Report Drafting Using Conformal Prediction [0.03%]
基于协形预测的不确定性感知视觉语言报告起草模型 CONRep
Danial Elyassirad,Benyamin Gheiji,Mahsa Vatanparast et al.
Danial Elyassirad et al.
The objective of this study is to quantify uncertainty in vision-language model (VLM)-based automated radiology report drafting (ARRD) to support trustworthy clinical deployment. In this study, we developed CONRep in two settings: label-bas...
Sex-Related Performance Disparities in Convolutional Neural Networks for Imaging-Based Assessment of Coronary Atherosclerosis and Ischemic Heart Disease: A Systematic Review [0.03%]
基于影像的冠状动脉动脉粥样硬化和缺血性心脏病评估中卷积神经网络性别差异的系统评价
Aya Mudrik,Ayala Dodge,Alon Moore Galindo et al.
Aya Mudrik et al.
Sex-related disparities persist in the diagnosis and management of ischemic heart disease (IHD), raising concern that convolutional neural networks (CNNs) used in coronary imaging may perpetuate these inequities. This systematic review eval...