Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial,Radiology

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Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial
Radiology ( IF 19.7 ) Pub Date : 2021-09-28 , DOI: 10.1148/radiol.2021204021
David K Eng ₁ , Nishith B Khandwala ₁ , Jin Long ₁ , Nancy R Fefferman ₁ , Shailee V Lala ₁ , Naomi A Strubel ₁ , Sarah S Milla ₁ , Ross W Filice ₁ , Susan E Sharp ₁ , Alexander J Towbin ₁ , Michael L Francavilla ₁ , Summer L Kaplan ₁ , Kirsten Ecklund ₁ , Sanjay P Prabhu ₁ , Brian J Dillon ₁ , Brian M Everist ₁ , Christopher G Anton ₁ , Mark E Bittman ₁ , Rebecca Dennis ₁ , David B Larson ₁ , Jayne M Seekins ₁ , Cicero T Silva ₁ , Arash R Zandieh ₁ , Curtis P Langlotz ₁ , Matthew P Lungren ₁ , Safwan S Halabi ₁

Affiliation

Background

Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice.

Purpose

To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid.

Materials and Methods

In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists’ signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center- and radiologist-level effects was used to compare the two experimental groups.

Results

Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001).

Conclusion

Use of an artificial intelligence algorithm improved skeletal age assessment accuracy and reduced interpretation times for radiologists, although differences were observed between centers.

Clinical trial registration no. NCT03530098

Online supplemental material is available for this article.

See also the editorial by Rubin in this issue.

中文翻译：

人工智能算法提高放射科医生在骨骼年龄评估中的表现：一项前瞻性多中心随机对照试验

背景

先前的研究表明，使用人工智能 (AI) 算法作为诊断辅助工具可能会提高骨骼年龄评估的质量，尽管这些研究缺乏来自临床实践的证据。

目的

比较使用和不使用 AI 算法作为诊断辅助工具的手部 X 光检查中骨骼年龄评估的准确性和解释时间。

材料和方法

在这项前瞻性随机对照试验中，使用（n = 792）和不使用（n= 739) 作为诊断辅助工具的 AI 算法。对于使用 AI 算法的检查，放射科医师将 AI 解释作为其日常临床工作的一部分进行展示，并被允许接受或修改它。来自六个中心的 93 名放射科医生对手部 X 光片进行了解读。主要疗效结果是放射科医师签署的报告中规定的骨骼年龄与不使用诊断辅助工具的四名放射科医师的平均解释之间的平均绝对差异。次要结果是解释时间。使用具有随机中心和放射科医生级别效应的线性混合效应回归模型来比较两个实验组。

结果

与不使用 AI 算法时相比，放射科医生使用 AI 算法时的总体平均绝对差异较低（5.36 个月对 5.95 个月；P = .04）。使用 AI 算法的绝对差异超过 12 个月（9.3% 对 13.0%，P = .02）和 24 个月（0.5% 对 1.8%，P = .02）的比例低于没有它的情况。使用 AI 算法的放射科医生解释时间的中位数低于没有它（102 秒对 142 秒，P = .001）。