Abstract：
OBJECTIVE: To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts.
METHODS: PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded.
RESULTS: A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans.
CONCLUSIONS: AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance.
CONCLUSIONS: AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.
METHODS: PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded.
RESULTS: A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans.
CONCLUSIONS: AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance.
CONCLUSIONS: AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.
摘要：
目的：回顾现有文献，以（1）确定人工智能（AI）模型用于检测舟骨和桡骨远端骨折的诊断功效，以及（2）将其功效与人类临床专家进行比较。
方法：PubMed，OVID/Medline,和Cochrane图书馆被查询调查发展的研究，验证,并分析AI对舟骨或桡骨远端骨折的检测。有关研究设计的数据，AI模型开发和架构，预测精度/接受者操作者特征曲线下面积(AUROC)，并记录成像方式.
结果：共确定了21项研究，其中12人（57.1%）使用人工智能检测桡骨远端骨折，9人(42.9%)使用人工智能检测舟骨骨折。AI模型平均表现出良好的诊断性能，舟骨骨折的AUROC值范围为0.77至0.96，桡骨远端骨折的AUROC值范围为0.90至0.99。对于舟骨和桡骨远端骨折，AI模型的准确性介于72.0％至90.3％和89.0％至98.0％之间。分别。与临床专家相比，14项研究中有13项（92.9%）报告说，人工智能模型表现出可比或更好的性能。断裂类型影响模型性能，隐匿性舟骨骨折的整体表现较差；然而，与人类相比，专门针对隐匿性骨折进行训练的模型显示出明显的性能改善。
结论：AI模型在检测舟骨和桡骨远端骨折方面表现出优异的性能，与人类专家相比，大多数人表现出可比或更好的表现。隐匿性骨折表现较差。然而,当专门针对困难的骨折模式进行训练时，AI模型展示了改进的性能。
结论：AI模型可以帮助检测常见的隐匿性骨折，同时提高桡骨远端和舟骨骨折诊断的工作流程效率。由于性能因裂缝类型而异，针对腕关节骨折检测的未来研究应明确目标是(1)识别难以检测的骨折还是(2)通过协助常规任务提高工作流程效率.
方法：PubMed，OVID/Medline,和Cochrane图书馆被查询调查发展的研究，验证,并分析AI对舟骨或桡骨远端骨折的检测。有关研究设计的数据，AI模型开发和架构，预测精度/接受者操作者特征曲线下面积(AUROC)，并记录成像方式.
结果：共确定了21项研究，其中12人（57.1%）使用人工智能检测桡骨远端骨折，9人(42.9%)使用人工智能检测舟骨骨折。AI模型平均表现出良好的诊断性能，舟骨骨折的AUROC值范围为0.77至0.96，桡骨远端骨折的AUROC值范围为0.90至0.99。对于舟骨和桡骨远端骨折，AI模型的准确性介于72.0％至90.3％和89.0％至98.0％之间。分别。与临床专家相比，14项研究中有13项（92.9%）报告说，人工智能模型表现出可比或更好的性能。断裂类型影响模型性能，隐匿性舟骨骨折的整体表现较差；然而，与人类相比，专门针对隐匿性骨折进行训练的模型显示出明显的性能改善。
结论：AI模型在检测舟骨和桡骨远端骨折方面表现出优异的性能，与人类专家相比，大多数人表现出可比或更好的表现。隐匿性骨折表现较差。然而,当专门针对困难的骨折模式进行训练时，AI模型展示了改进的性能。
结论：AI模型可以帮助检测常见的隐匿性骨折，同时提高桡骨远端和舟骨骨折诊断的工作流程效率。由于性能因裂缝类型而异，针对腕关节骨折检测的未来研究应明确目标是(1)识别难以检测的骨折还是(2)通过协助常规任务提高工作流程效率.
