对全腿站立式x光片自动测量的深度学习软件的评估。

Q2 Medicine
Louis Lassalle, Nor-Eddine Regnard, Marion Durteste, Jeanne Ventre, Vincent Marty, Lauryane Clovis, Zekun Zhang, Nicolas Nitche, Alexis Ducarouge, Jean-Denis Laredo, Ali Guermazi
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引用次数: 0

摘要

背景:精确的下肢测量对于评估肌肉骨骼健康至关重要;全自动解决方案有可能提高这些测量的标准化和可重复性。这项研究比较了BoneMetrics (Gleamer, Paris, France)(一种基于商业人工智能(AI)的软件)进行的测量与专家在前后位全腿站立x线片上进行的手动测量。方法:对来自四家影像机构的连续正位全腿站立x线片数据集进行回顾性分析。确定髋关节-膝关节-踝关节角度、骨盆倾斜度、腿长、股长和胫骨长度的关键解剖标志由两位肌肉骨骼放射专家独立注释,并作为基本事实。使用平均绝对误差、Bland-Altman分析和类内相关系数将人工智能的性能与这些参考测量进行比较。结果:167例患者的175张正位全腿站立x线片被纳入最终数据集(平均年龄= 49.9±23.6岁;103名女性和64名男性)。平均绝对误差值是0.30°(95%可信区间[CI] [0.28, 0.32]) hip-knee-ankle角,0.75毫米(95% CI[0.60, 0.88])骨盆倾斜,1.03毫米(95% CI[0.91, 1.14])腿的长度从顶部的股骨头,1.45毫米(95%置信区间[1.33,1.60])从股骨头中心的腿长,0.95毫米(95% CI[0.85, 1.04])股骨长度的股骨头,1.23毫米(95%置信区间[1.12,1.32])股骨长度从股骨头的中心,胫骨长度为1.38 mm (95% CI[1.21, 1.52])。Bland-Altman分析显示,所有测量结果都没有系统性偏差。此外,该软件与金标准测量结果具有良好的一致性,所有参数的类内相关系数(ICC)值均大于0.97。结论:在前后位全腿站立x线片上的自动测量为人工评估提供了可靠的替代方案。在肌肉骨骼放射学中使用人工智能有可能在不影响患者护理标准的情况下支持医生的日常实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of a deep learning software for automated measurements on full-leg standing radiographs.

Background: Precise lower limb measurements are crucial for assessing musculoskeletal health; fully automated solutions have the potential to enhance standardization and reproducibility of these measurements. This study compared the measurements performed by BoneMetrics (Gleamer, Paris, France), a commercial artificial intelligence (AI)-based software, to expert manual measurements on anteroposterior full-leg standing radiographs.

Methods: A retrospective analysis was conducted on a dataset comprising consecutive anteroposterior full-leg standing radiographs obtained from four imaging institutions. Key anatomical landmarks to define the hip-knee-ankle angle, pelvic obliquity, leg length, femoral length, and tibial length were annotated independently by two expert musculoskeletal radiologists and served as the ground truth. The performance of the AI was compared against these reference measurements using the mean absolute error, Bland-Altman analyses, and intraclass correlation coefficients.

Results: A total of 175 anteroposterior full-leg standing radiographs from 167 patients were included in the final dataset (mean age = 49.9 ± 23.6 years old; 103 women and 64 men). Mean absolute error values were 0.30° (95% confidence interval [CI] [0.28, 0.32]) for the hip-knee-ankle angle, 0.75 mm (95% CI [0.60, 0.88]) for pelvic obliquity, 1.03 mm (95% CI [0.91,1.14]) for leg length from the top of the femoral head, 1.45 mm (95% CI [1.33, 1.60]) for leg length from the center of the femoral head, 0.95 mm (95% CI [0.85, 1.04]) for femoral length from the top of the femoral head, 1.23 mm (95% CI [1.12, 1.32]) for femoral length from the center of the femoral head, and 1.38 mm (95% CI [1.21, 1.52]) for tibial length. The Bland-Altman analyses revealed no systematic bias across all measurements. Additionally, the software exhibited excellent agreement with the gold-standard measurements with intraclass correlation coefficient (ICC) values above 0.97 for all parameters.

Conclusions: Automated measurements on anteroposterior full-leg standing radiographs offer a reliable alternative to manual assessments. The use of AI in musculoskeletal radiology has the potential to support physicians in their daily practice without compromising patient care standards.

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CiteScore
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