Eduardo Moreno Júdice de Mattos Farina,Gilberto Szarf
Eduardo Moreno Júdice de Mattos Farina
Spatiotemporal Asymmetries of Longitudinal Screening Mammograms for Breast Cancer Risk Prediction [0.03%]
用于乳腺癌风险预测的纵向筛查乳房X光照片的时空不对称性
Zhengbo Zhou,Dooman Arefan,Margarita L Zuley et al.
Zhengbo Zhou et al.
Purpose To develop and evaluate a new deep learning-based risk model that explicitly captures bilateral and longitudinal asymmetries on sequential mammograms for predicting breast cancer risk. Materials and Methods In this institutional rev...
Anatomy-specific Performance of CT Angiography-based AI for Anterior Circulation Occlusion: Systematic Review and Meta-Analysis [0.03%]
基于CTA的人工智能检测前循环大血管闭塞的解剖特异性性能：系统综述和荟萃分析
Yike Liu,Chaofan Li,Zepeng Ren et al.
Yike Liu et al.
Purpose To evaluate anatomy-specific diagnostic performance of CT angiography-based artificial intelligence (AI) for anterior circulation occlusion detection and negative-result implications for distal occlusions. Materials and Methods In t...
Predetermined Change Control Plan Adoption and Documentation Transparency in FDA-cleared Radiology Artificial Intelligence/Machine Learning Devices [0.03%]
美国食品和药物管理局批准的放射学人工智能/机器学习设备中的预定变更控制计划采纳及文件透明度问题
Ketan Dayma,Palak Patel,Kenneth Hildreth et al.
Ketan Dayma et al.
Purpose To characterize Predetermined Change Control Plan (PCCP) adoption and documentation transparency among U.S. Food and Drug Administration (FDA)-cleared radiology artificial intelligence/machine learning (AI/ML)-devices (2015-2025). M...
More than Meets the Eye: Exposure Parameters Cause Shortcut Learning in Radiography Deep Learning Models [0.03%]
超乎所见：摄影中的曝光参数导致放射学深度学习模型的捷径学习现象
Bastien Le Guellec,Guillaume Chassagnon
Bastien Le Guellec
Simulation of AI-driven CT Imaging Queue Prioritization in the Emergency Department [0.03%]
基于人工智能的急诊CT影像检查优先级排序仿真研究
Erica Silva,Kevin Tang,Sam Chiacchia et al.
Erica Silva et al.
Purpose To evaluate the impact of an AI-driven CT queue prioritization system on emergency department (ED) CT wait times using a discrete-event simulation (DES). Materials and Methods Multimodal data from 313,966 ED visits (August 2020-Augu...
Externally Tested AI for Lung Nodule Classification: A Realistic Benchmark for an Emerging Screening Era [0.03%]
面向肺癌筛查的外部验证人工智能模型评估体系
Keno K Bressem,Su Hwan Kim
Keno K Bressem
Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment [0.03%]
胸部放射摄影中曝光参数对深度学习模型的影响及其应用意义
Han-Jay Shu,Shun-Ting Chang,Rodrigo Rosa Gameiro et al.
Han-Jay Shu et al.
Purpose To investigate whether deep learning models trained on chest radiographs (CXRs) rely on radiographic exposure parameters as shortcut features and to quantify the resulting biases under controlled confounding and natural exposure reg...
Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation [0.03%]
基于医学图像分割的高效深度学习对成本和专家时间的影响
Astha Jaiswal,Miriam Rinneburger,Franziska Meyer et al.
Astha Jaiswal et al.
Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference standard medical image segmentation, including assessment of two sample-selection strategies an...
Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge [0.03%]
用于评估肺癌筛查CT中可疑结节恶性风险估计的AI和放射科医生的基准测试：LUNA25挑战赛
Dré Peeters,Bogdan Obreja,Noa Antonissen et al.
Dré Peeters et al.
Purpose To compare the performance of an artificial intelligence (AI) system with that of radiologists for estimating malignancy risk of indeterminate-size nodules (5-15 mm) on low-dose CT (LDCT) within a standardized and transparent evalua...