深度学习在腿长不一致的 X 光片腿长测量中的诊断性能：系统综述。

IF 2 Q2 ORTHOPEDICS

Journal of Experimental Orthopaedics Pub Date : 2024-11-10 DOI:10.1002/jeo2.70080

Bradley A. Lezak, James A. Pruneski, Jacob F. Oeding, Kyle N. Kunze, Riley J. Williams III, Michael J. Alaia, Andrew D. Pearle, Joshua S. Dines, Kristian Samuelsson, Ayoosh Pareek

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引用次数: 0

摘要

目的：系统综述有关机器学习在腿长不一致（LLD）方面的应用的文献，并深入分析与该主题最相关的手稿，以强调机器学习在诊断和治疗腿长不一致方面的重要性和未来的临床意义：根据《系统综述和元分析首选报告项目》指南，使用 PubMed、OVID/Medline 和 Cochrane 图书馆进行了系统的电子检索。两名观察员独立筛选了潜在文章的摘要和标题：结果：检索共发现六项研究。所有测量值均采用标准化前后长腿X光片进行计算。其中五项研究（83.3%）使用股骨长度、胫骨长度和腿长的测量值来评估LLD，一项研究（16.6%）使用髂嵴高度差来量化LLD。深度学习模型在预测所有长度测量值方面表现出极佳的可靠性，类内相关系数在 0.98 到 1.0 之间，平均绝对误差 (MAE) 值在 0.11 到 0.45 厘米之间。三项研究报告了 LLD 的测量结果，其中卷积神经网络模型预测 LLD 的 MAE 最低，为 0.13 厘米：机器学习模型在确定 LLD 方面有效且高效。临床意义：本综述强调了深度学习（DL）算法在准确可靠地测量长腿X光片上的下肢长度和腿长差异（LLD）方面的潜力。报告的平均绝对误差和类内相关系数值表明，DL模型的性能与放射科医生的性能相当，这表明基于DL的评估有可能在临床实践中用于自动测量下肢长度和LLD：证据等级：IV 级。

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Diagnostic performance of deep learning for leg length measurements on radiographs in leg length discrepancy: A systematic review

Purpose

To systematically review the literature regarding machine learning in leg length discrepancy (LLD) and to provide insight into the most relevant manuscripts on this topic in order to highlight the importance and future clinical implications of machine learning in the diagnosis and treatment of LLD.

Methods

A systematic electronic search was conducted using PubMed, OVID/Medline and Cochrane libraries in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Two observers independently screened the abstracts and titles of potential articles.

Results

A total of six studies were identified in the search. All measurements were calculated using standardized anterior-posterior long-leg radiographs. Five (83.3%) of the studies used measurements of the femoral length, tibial length and leg length to assess LLD, whereas one (16.6%) study used the iliac crest height difference to quantify LLD. The deep learning models showed excellent reliability in predicting all length measurements with intraclass correlation coefficients ranging from 0.98 to 1.0 and mean absolute error (MAE) values ranging from 0.11 to 0.45 cm. Three studies reported measurements of LLD, and the convolutional neural network model showed the lowest MAE of 0.13 cm in predicting LLD.

Conclusions

Machine learning models are effective and efficient in determining LLD. Implementation of these models may reduce cost, improve efficiency and lead to better overall patient outcomes.

Clinical Relevance

This review highlights the potential of deep learning (DL) algorithms for accurate and reliable measurement of lower limb length and leg length discrepancy (LLD) on long-leg radiographs. The reported mean absolute error and intraclass correlation coefficient values indicate that the performance of the DL models was comparable to that of radiologists, suggesting that DL-based assessments could potentially be used to automate the measurement of lower limb length and LLD in clinical practice.