Kluck Dylan G, Makarov Marina R, Kanaan Yassine, Jo Chan-Hee, Birch John G
Texas Scottish Rite Hospital for Children, Dallas, Texas.
J Bone Joint Surg Am. 2023 Feb 1;105(3):202-206. doi: 10.2106/JBJS.22.00833. Epub 2022 Nov 18.
We previously demonstrated that the White-Menelaus arithmetic formula combined with skeletal age as estimated with the Greulich and Pyle (GP) atlas was the most accurate method for predicting leg lengths and residual leg-length discrepancy (LLD) at maturity in a cohort of patients treated with epiphysiodesis. We sought to determine if an online artificial intelligence (AI)-based hand-and-wrist skeletal age system provided consistent readings and to evaluate how these readings influenced the prediction of the outcome of epiphysiodesis in this cohort.
JPEG images of perioperative hand radiographs for 76 subjects were independently submitted by 2 authors to an AI skeletal age web site (http://physis.16bit.ai/). We compared the accuracy of the predicted long-leg length (after epiphysiodesis), short-leg length, and residual LLD with use of the White-Menelaus formula and either human-estimated GP or AI-estimated skeletal age.
The AI skeletal age readings had an intraclass correlation coefficient (ICC) of 0.99. AI-estimated skeletal age was generally greater than human-estimated GP skeletal age (average, 0.5 year greater in boys and 0.1 year greater in girls). Overall, the prediction accuracy was improved with AI readings; these differences reached significance for the short-leg and residual LLD prediction errors. Residual LLD was underestimated by ≥1.0 cm in 26 of 76 subjects when human-estimated GP skeletal age was used (range of underestimation, 1.0 to 3.2 cm), compared with only 10 of 76 subjects when AI skeletal age was used (range of underestimation, 1.1 cm to 2.2 cm) (p < 0.01). Residual LLD was overestimated by ≥1.0 cm in 3 of 76 subjects by both methods (range of overestimation, 1.0 to 1.3 cm for the human-estimated GP method and 1.0 to 1.6 cm for the AI method).
The AI method of determining hand-and-wrist skeletal age was highly reproducible in this cohort and improved the accuracy of prediction of leg length and residual discrepancy when compared with traditional human interpretation of the GP atlas. This improvement could be explained by more accurate estimation of skeletal age via a machine-learning AI system calibrated with a large database.
Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
我们之前证明，在一组接受骨骺阻滞术治疗的患者中，怀特 - 梅内劳斯算术公式结合使用格吕利希和派尔（GP）图谱估计的骨龄，是预测成年时腿长和残余腿长差异（LLD）最准确的方法。我们试图确定基于人工智能（AI）的在线手和腕骨龄系统是否能提供一致的读数，并评估这些读数如何影响该队列中骨骺阻滞术结果的预测。
76名受试者围手术期手部X光片的JPEG图像由2名作者独立提交至一个AI骨龄网站（http://physis.16bit.ai/）。我们使用怀特 - 梅内劳斯公式以及人工估计的GP骨龄或AI估计的骨龄，比较预测的长腿长度（骨骺阻滞后）、短腿长度和残余LLD的准确性。
AI骨龄读数的组内相关系数（ICC）为0.99。AI估计的骨龄通常大于人工估计的GP骨龄（男孩平均大0.5岁，女孩平均大0.1岁）。总体而言，AI读数提高了预测准确性；这些差异在短腿和残余LLD预测误差方面具有统计学意义。使用人工估计的GP骨龄时，76名受试者中有26名残余LLD被低估≥1.0 cm（低估范围为1.0至3.2 cm），而使用AI骨龄时，76名受试者中只有10名（低估范围为1.1 cm至2.2 cm）（p < 0.01）。两种方法在76名受试者中均有3名残余LLD被高估≥1.0 cm（人工估计的GP方法高估范围为1.0至1.3 cm，AI方法高估范围为从1.0至1.6 cm）。
在该队列中，AI确定手和腕骨龄的方法具有高度可重复性，与传统的人工解读GP图谱相比，提高了腿长和残余差异的预测准确性。这种改进可以通过使用经过大型数据库校准的机器学习AI系统更准确地估计骨龄来解释。
预后水平III。有关证据水平的完整描述，请参阅作者指南。
