Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；City of Oulu（奥卢市）；Ailean Technologies Oy（艾利安技术公司）
Abu Mohammed Raisuddin, Elias Vaattovaara, Mika Nevalainen, Marko Nikki, Elina Järvenpää, Kaisa Makkonen, Pekka Pinola, Tuula Palsio, Arttu Niemensivu, Osmo Ter… 展开作者
Abu Mohammed Raisuddin, Elias Vaattovaara, Mika Nevalainen, Marko Nikki, Elina Järvenpää, Kaisa Makkonen, Pekka Pinola, Tuula Palsio, Arttu Niemensivu, Osmo Tervonen, Aleksei Tiulpin
Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection -- DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set -- average precision of 0.99 (0.99-0.99) vs 0.64 (0.46-0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98-0.99) vs 0.84 (0.72-0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.
YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images
Department of Electrical and Computer Engineering, Tamkang University（潭府大学电气与计算机工程系）；Graduate Institute of Networking and Multimedia, National Taiwan University（台湾大学网络与多媒体研究所）；School of Nursing, National Taipei University of Nursing and Health Sciences（台北护理大学护理学院）
YOLOv8-ResCBAM: YOLOv8 Based on An Effective Attention Module for Pediatric Wrist Fracture Detection
Graduate Institute of Networking and Multimedia, National Taiwan University（国立台湾大学网路与多媒体研究所）；Department of Electrical and Computer Engineering, Tamkang University（淡江大学电机与电脑工程学系）
Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images
Graduate Institute of Networking and Multimedia（网络与多媒体研究生院）；National Taiwan University（台湾大学）；Department of Electrical and Computer Engineering（电子工程系）；Tamkang University（Tamkang大学）；College of Energy and Mechanical Engineering（能源与机械工程学院）；Shanghai University of Electric Power（上海电力大学）
YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection
Department of Electrical and Computer Engineering, Tamkang University, New Taipei City, 251301, Taiwan（电子工程系，潭南大学，新北市）；Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei City 106335, Taiwan（网络与多媒体研究所，台湾大学，台北市）；School of Nursing, National Taipei University of Nursing and Health Sciences, Taipei City, 112303, Taiwan（护理学院，台北护理及健康科学大学，台北市）；College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai, 201306, China（能源与机械工程学院，上海电力大学，上海）
Automatic Analysis System of Calcaneus Radiograph: Rotation-Invariant Landmark Detection for Calcaneal Angle Measurement, Fracture Identification and Fracture Region Segmentation
Beijing Institute of Technology（北京理工大学）；The First Affiliated Hospital of Jinzhou Medical University（锦州医科大学附属第一医院）；Zhejiang University of Science & Technology（浙江科技学院）；Peking University People’s Hospital（北京大学人民医院）
