基于深度学习的髋关节发育不良自动诊断系统
Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip
AI总结：
针对髋关节发育不良（DDH）手动测量放射学角度耗时且易变的问题，本研究开发端到端深度学习关键点检测模型自动计算CE、Tonnis、Sharp角，结合数据驱动评分系统输出可解释诊断结果，性能优于中等经验骨科医生，为临床提供更一致的诊断工具。
摘要：
目的：髋关节发育不良（DDH）的临床诊断通常需要从骨盆X光片中手动测量关键放射学角度——中心边缘角（CE）、Tonnis角和Sharp角，该过程耗时且易受变异性影响。本研究旨在开发一种整合这些测量值的自动化系统，以提高DDH诊断的准确性和一致性。方法与流程：我们开发了一种端到端的深度学习关键点检测模型，可从骨盆X光片中准确识别8个解剖学关键点，从而实现CE、Tonnis和Sharp角的自动计算。为支持诊断决策，我们引入了一种新颖的数据驱动评分系统，将三个角度的信息整合为全面且可解释的诊断输出。结果：与8名中等经验的骨科医生队列相比，该系统在角度测量方面表现出更优的一致性。CE角、Tonnis角和Sharp角的组内相关系数分别为0.957（95%CI：0.952--0.962）、0.942（95%CI：0.937--0.947）和0.966（95%CI：0.964--0.968）。系统的诊断F1得分为0.863（95%CI：0.851--0.876），显著优于骨科医生组（0.777，95%CI：0.737--0.817，p=0.005），也优于单独使用每个角度的临床诊断标准（p<0.001）。结论：所提出的系统为DDH提供了可靠且一致的放射学角度自动测量以及可解释的诊断输出，性能优于中等经验的临床医生。临床影响：这种AI驱动的解决方案减少了手动测量的变异性和潜在误差，为临床医生提供了更一致且可解释的DDH诊断工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
