Purpose

The aim of this study is to determine the diagnostic performance of artificial intelligence with the use of convolutional neural networks (CNN) for detecting scaphoid fractures on anteroposterior wrist radiographs. The performance of the deep learning algorithm was also compared with that of the emergency department (ED) physician and two orthopaedic specialists (less experienced and experienced in the hand surgery).

Methods

A total 390 patients with AP wrist radiographs were included in the study. The presence/absence of the fracture on radiographs was confirmed via CT. The diagnostic performance of the CNN, ED physician and two orthopaedic specialists (less experienced and experienced) as measured by AUC, sensitivity, specificity, F-Score and Youden index, to detect scaphoid fractures was evaluated and compared between the groups.

Results

The CNN had 76% sensitivity and 92% specificity, 0.840 AUC, 0.680 Youden index and 0.826 F score values in identifying scaphoid fractures. The experienced orthopaedic specialist had the best diagnostic performance according to AUC. While CNN's performance was similar to a less experienced orthopaedic specialist, it was better than the ED physician.

Conclusion

The deep learning algorithm has the potential to be used for diagnosing scaphoid fractures on radiographs. Artificial intelligence can be useful for scaphoid fracture diagnosis particularly in the absence of an experienced orthopedist or hand surgeon.

中文翻译：

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直接放射成像诊断舟状骨骨折的人工智能系统的评估。

目的

本研究的目的是确定人工智能的诊断性能，使用卷积神经网络 (CNN) 检测手腕前后位 X 光片上的舟状骨骨折。深度学习算法的性能也与急诊科 (ED) 医生和两名骨科专家（在手外科经验较少和经验不足）的性能进行了比较。

方法

共有 390 名有 AP 腕部 X 光片的患者被纳入研究。通过 CT 确认 X 线片上是否存在骨折。通过 AUC、敏感性、特异性、F-Score 和约登指数测量 CNN、ED 医生和两名骨科专家（经验较少和经验不足）的诊断性能，以检测舟状骨骨折，并在各组之间进行了评估和比较。

结果

CNN在识别舟状骨骨折方面具有 76% 的敏感性和 92% 的特异性、0.840 AUC、0.680 Youden 指数和 0.826 F评分值。根据 AUC，经验丰富的骨科专家的诊断性能最好。虽然 CNN 的表现与经验不足的骨科专家相似，但它比 ED 医生要好。

结论

深度学习算法有可能用于诊断 X 光片上的舟状骨骨折。人工智能可用于舟状骨骨折诊断，特别是在没有经验丰富的骨科医生或手外科医生的情况下。