Validity of machine learning algorithms for automatically extract growing rod length on radiographs in children with early-onset scoliosis.

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Medical & Biological Engineering & Computing Pub Date : 2025-01-01 Epub Date: 2024-08-16 DOI:10.1007/s11517-024-03181-1
Mohammad Humayun Kabir, Marek Reformat, Sarah Southon Hryniuk, Kyle Stampe, Edmond Lou
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Abstract

The magnetically controlled growing rod technique is an effective surgical treatment for children who have early-onset scoliosis. The length of the instrumented growing rods is adjusted regularly to compensate for the normal growth of these patients. Manual measurement of rod length on posteroanterior spine radiographs is subjective and time-consuming. A machine learning (ML) system using a deep learning approach was developed to automatically measure the adjusted rod length. Three ML models-rod model, 58 mm model, and head-piece model-were developed to extract the rod length from radiographs. Three-hundred and eighty-seven radiographs were used for model development, and 60 radiographs with 118 rods were separated for final testing. The average precision (AP), the mean absolute difference (MAD) ± standard deviation (SD), and the inter-method correlation coefficient (ICC[2,1]) between the manual and artificial intelligence (AI) adjustment measurements were used to evaluate the developed method. The AP of the 3 models were 67.6%, 94.8%, and 86.3%, respectively. The MAD ± SD of the rod length change was 0.98 ± 0.88 mm, and the ICC[2,1] was 0.90. The average time to output a single rod measurement was 6.1 s. The developed AI provided an accurate and reliable method to detect the rod length automatically.

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机器学习算法自动提取早发脊柱侧凸儿童X光片上生长杆长度的有效性。
磁控生长棒技术是一种针对早期脊柱侧弯儿童的有效手术治疗方法。植入器械的生长棒的长度会定期调整,以补偿这些患者的正常生长。在脊柱后正位X光片上手动测量生长棒长度既主观又耗时。我们开发了一种采用深度学习方法的机器学习(ML)系统,用于自动测量调整后的杆件长度。开发了三种 ML 模型--杆模型、58 毫米模型和头部件模型,用于从射线照片中提取杆长度。模型开发使用了三百八十七张射线照片,最终测试分离了 60 张射线照片和 118 根杆件。使用平均精度(AP)、平均绝对差值(MAD)± 标准差(SD)以及人工和人工智能调整测量之间的方法间相关系数(ICC[2,1])来评估所开发的方法。3 个模型的 AP 分别为 67.6%、94.8% 和 86.3%。杆长度变化的 MAD ± SD 为 0.98 ± 0.88 mm,ICC[2,1]为 0.90。所开发的人工智能提供了一种准确可靠的竿长自动检测方法。
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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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