论文标题
基于深度学习的自动诊断系统，用于髋关节发育发育不良
Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip
论文作者
论文摘要
目的：从骨盆X光片体中，髋（DDH）的发育发育不良（DDH）的临床诊断通常涉及手动测量关键的放射角 - 中心边缘（CE），TONNIS和尖锐的角度，这是一个耗时且易于变异的过程。这项研究旨在开发一种自动化系统，以整合这些测量值，以提高DDH诊断的准确性和一致性。 方法和程序：我们开发了一个用于关键点检测的端到端深度学习模型，该模型准确地识别了骨盆X光片的八个解剖关键点，从而实现了CE，Tonnis和尖角的自动计算。为了支持诊断决策，我们引入了一个新颖的数据驱动评分系统，该系统将所有角度的信息结合到一个全面且可解释的诊断输出中。 结果：与八位中等经验的骨科医生相比，该系统在角度测量中表现出较高的一致性。 CE，TONNIS和锐角的类内相关系数为0.957（95％CI：0.952---0.962），0.942（95％CI：0.937--0.947）和0.966（分别为0.966）（95％CI：0.964---0.968）。该系统的诊断F1得分为0.863（95％CI：0.851---0.876），显着超过了骨科群体（0.777，95％CI：0.737--0.817，p = 0.005），以及使用临床诊断标准，以及使用每个角度的临床诊断标准（p <0.001）。 结论：拟议的系统提供了可靠且一致的自动测量，可对DDH的放射线角度和可解释的诊断输出，表现优于经验丰富的临床医生。 临床影响：该AI驱动的解决方案减少了手动测量的可变性和潜在错误，从而为临床医生提供了更一致，更可解释的DDH诊断工具。
Objective: The clinical diagnosis of developmental dysplasia of the hip (DDH) typically involves manually measuring key radiological angles -- Center-Edge (CE), Tonnis, and Sharp angles -- from pelvic radiographs, a process that is time-consuming and susceptible to variability. This study aims to develop an automated system that integrates these measurements to enhance the accuracy and consistency of DDH diagnosis. Methods and procedures: We developed an end-to-end deep learning model for keypoint detection that accurately identifies eight anatomical keypoints from pelvic radiographs, enabling the automated calculation of CE, Tonnis, and Sharp angles. To support the diagnostic decision, we introduced a novel data-driven scoring system that combines the information from all three angles into a comprehensive and explainable diagnostic output. Results: The system demonstrated superior consistency in angle measurements compared to a cohort of eight moderately experienced orthopedists. The intraclass correlation coefficients for the CE, Tonnis, and Sharp angles were 0.957 (95% CI: 0.952--0.962), 0.942 (95% CI: 0.937--0.947), and 0.966 (95% CI: 0.964--0.968), respectively. The system achieved a diagnostic F1 score of 0.863 (95% CI: 0.851--0.876), significantly outperforming the orthopedist group (0.777, 95% CI: 0.737--0.817, p = 0.005), as well as using clinical diagnostic criteria for each angle individually (p<0.001). Conclusion: The proposed system provides reliable and consistent automated measurements of radiological angles and an explainable diagnostic output for DDH, outperforming moderately experienced clinicians. Clinical impact: This AI-powered solution reduces the variability and potential errors of manual measurements, offering clinicians a more consistent and interpretable tool for DDH diagnosis.
