Automated Measurements of Long Leg Radiographs in Pediatric Patients: A Pilot Study to Evaluate an Artificial Intelligence-Based Algorithm.

IF 2 4区医学 Q2 PEDIATRICS

Children-Basel Pub Date : 2024-09-27 DOI:10.3390/children11101182

Thies J N van der Lelij, Willem Grootjans, Kevin J Braamhaar, Pieter Bas de Witte

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引用次数: 0

Abstract

Background: Assessment of long leg radiographs (LLRs) in pediatric orthopedic patients is an important but time-consuming routine task for clinicians. The goal of this study was to evaluate the performance of artificial intelligence (AI)-based leg angle measurement assistant software (LAMA) in measuring LLRs in pediatric patients, compared to traditional manual measurements.

Methods: Eligible patients, aged 11 to 18 years old, referred for LLR between January and March 2022 were included. The study comprised 29 patients (58 legs, 377 measurements). The femur length, tibia length, full leg length (FLL), leg length discrepancy (LLD), hip-knee-ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), and mechanical medial proximal tibial angle (mMPTA) were measured automatically using LAMA and compared to manual measurements of a senior pediatric orthopedic surgeon and an advanced practitioner in radiography.

Results: Correct landmark placement with AI was achieved in 76% of the cases for LLD measurements, 88% for FLL and femur length, 91% for mLDFA, 97% for HKA, 98% for mMPTA, and 100% for tibia length. Intraclass correlation coefficients (ICCs) indicated moderate to excellent agreement between AI and manual measurements, ranging from 0.73 (95% confidence interval (CI): 0.54 to 0.84) to 1.00 (95%CI: 1.00 to 1.00).

Conclusion: In cases of correct landmark placement, AI-based algorithm measurements on LLRs of pediatric patients showed high agreement with manual measurements.

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自动测量儿科患者的长腿X光片：评估基于人工智能算法的试点研究。

背景：评估儿科骨科患者的长腿X光片（LLR）是临床医生的一项重要但耗时的常规任务。本研究的目的是评估基于人工智能（AI）的腿部角度测量辅助软件（LAMA）在测量儿科患者长腿X光片方面的性能，并与传统的人工测量方法进行比较：纳入2022年1月至3月期间转诊的符合条件的11至18岁LLR患者。该研究包括 29 名患者（58 条腿，377 次测量）。使用 LAMA 自动测量了股骨长度、胫骨长度、全腿长度 (FLL)、腿长差异 (LLD)、髋膝踝角度 (HKA)、机械股骨外侧远端角度 (mLDFA) 和机械胫骨内侧近端角度 (mMPTA)，并与一名资深儿科骨科医生和一名放射摄影高级从业人员的人工测量结果进行了比较：76%的 LLD 测量、88% 的 FLL 和股骨长度测量、91% 的 mLDFA 测量、97% 的 HKA 测量、98% 的 mMPTA 测量和 100% 的胫骨长度测量均使用 AI 实现了正确的地标定位。类内相关系数（ICCs）显示人工智能和人工测量之间的一致性为中等到极佳，范围从 0.73（95% 置信区间（CI）：0.54 到 0.84）到 1.00（95% 置信区间（CI）：1.00 到 1.00）：在地标位置正确的情况下，基于人工智能算法的儿科患者 LLR 测量结果与人工测量结果具有很高的一致性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Children-Basel PEDIATRICS-

CiteScore

2.70

自引率

16.70%

发文量

1735

审稿时长

6 weeks

期刊介绍： Children is an international, open access journal dedicated to a streamlined, yet scientifically rigorous, dissemination of peer-reviewed science related to childhood health and disease in developed and developing countries. The publication focuses on sharing clinical, epidemiological and translational science relevant to children’s health. Moreover, the primary goals of the publication are to highlight under‑represented pediatric disciplines, to emphasize interdisciplinary research and to disseminate advances in knowledge in global child health. In addition to original research, the journal publishes expert editorials and commentaries, clinical case reports, and insightful communications reflecting the latest developments in pediatric medicine. By publishing meritorious articles as soon as the editorial review process is completed, rather than at predefined intervals, Children also permits rapid open access sharing of new information, allowing us to reach the broadest audience in the most expedient fashion.