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人工智能驱动的髋关节形态评估测量方法的验证

Validation of AI-driven measurements for hip morphology assessment.

作者信息

Lassalle Louis, Regnard Nor-Eddine, Durteste Marion, Ventre Jeanne, Marty Vincent, Clovis Lauryane, Zhang Zekun, Nitche Nicolas, Ducarouge Alexis, Tran Alexia, Laredo Jean-Denis, Guermazi Ali

机构信息

Réseau Imagerie Sud Francilien, Lieusaint, France; Ramsay Santé, Clinique du Mousseau, Evry, France; Gleamer, Paris, France.

Réseau Imagerie Sud Francilien, Lieusaint, France; Ramsay Santé, Clinique du Mousseau, Evry, France; Gleamer, Paris, France.

出版信息

Eur J Radiol. 2025 Feb;183:111911. doi: 10.1016/j.ejrad.2024.111911. Epub 2024 Dec 31.

Abstract

RATIONALE AND OBJECTIVES

Accurate assessment of hip morphology is crucial for the diagnosis and management of hip pathologies. Traditional manual measurements are prone to mistakes and inter- and intra-reader variability. Artificial intelligence (AI) could mitigate such issues by providing accurate and reproducible measurements. The aim of this study was to compare the performance of BoneMetrics (Gleamer, Paris, France) in measuring pelvic and hip parameters on anteroposterior (AP) and false profile radiographs to expert manual measurements.

MATERIALS AND METHODS

This retrospective study included AP and false profile pelvic radiographs collected from private practices in France. Pelvic and hip measurements included the femoral neck shaft angle, lateral center edge angle, acetabular roof angle, pelvic obliquity, and vertical center anterior angle. AI measurements were compared to a ground truth established by two expert radiologists. Performance metrics included mean absolute error (MAE), Bland-Altman analysis, and intraclass correlation coefficients (ICC).

RESULTS

AI measurements were performed on AP views from 88 patients and on false profile views from 60 patients. They demonstrated high accuracy, with MAE values inferior to 0.5 mm for pelvic obliquity and inferior to 4.2° for all pelvic angles on both views. ICC values indicated good to excellent agreement between AI measurements and the ground truth (0.78-0.99). Notably, no significant differences were found in AI measurement accuracy between patients with normal and abnormal acetabular coverage.

CONCLUSION

The application of AI in measuring pelvic and hip parameters on AP and false profile radiographs demonstrates promising outcomes. The results reveal that these AI-powered measurements provide accurate estimations and show strong agreement with expert manual measurements. Ultimately, the use of AI has the potential to enhance the reproducibility of measurements as part of comprehensive hip assessments, thereby improving diagnostic accuracy.

摘要

原理与目的

准确评估髋关节形态对于髋关节疾病的诊断和治疗至关重要。传统的手工测量容易出错,且在不同阅片者之间以及同一阅片者不同时间的测量结果存在差异。人工智能(AI)可以通过提供准确且可重复的测量来缓解此类问题。本研究的目的是比较BoneMetrics(法国巴黎Gleamer公司)在前后位(AP)和假斜位X线片上测量骨盆和髋关节参数的性能与专家手工测量的结果。

材料与方法

这项回顾性研究纳入了从法国私人诊所收集的AP位和假斜位骨盆X线片。骨盆和髋关节测量包括股骨颈干角、外侧中心边缘角、髋臼顶角、骨盆倾斜度和垂直中心前角。将AI测量结果与两位专家放射科医生确定的真实值进行比较。性能指标包括平均绝对误差(MAE)、Bland-Altman分析和组内相关系数(ICC)。

结果

对88例患者的AP位视图和60例患者的假斜位视图进行了AI测量。结果显示其具有很高的准确性,在两种视图上,骨盆倾斜度的MAE值均小于0.5毫米,所有骨盆角度的MAE值均小于4.2°。ICC值表明AI测量结果与真实值之间具有良好到极好的一致性(0.78 - 0.99)。值得注意的是,髋臼覆盖正常和异常的患者在AI测量准确性方面未发现显著差异。

结论

AI在AP位和假斜位X线片上测量骨盆和髋关节参数的应用显示出了良好的结果。结果表明,这些由AI驱动的测量提供了准确的估计,并且与专家手工测量结果高度一致。最终,作为全面髋关节评估的一部分,使用AI有可能提高测量的可重复性,从而提高诊断准确性。

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