An AI-Based Image Quality Control Framework for Knee Radiographs

Author:

Sun Hongbiao,Wang Wenwen,He Fujin,Wang Duanrui,Liu Xiaoqing,Xu Shaochun,Zhao Baolian,Li Qingchu,Wang Xiang,Jiang Qinling,Zhang Rong,Liu Shiyuan,Xiao Yi

Abstract

AbstractImage quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically performed by clinicians. We proposed an AI-based fully automatic QC model for knee radiographs using high-resolution net (HR-Net) to identify predefined key points in images. We then performed geometric calculations to transform the identified key points into three QC criteria, namely, anteroposterior (AP)/lateral (LAT) overlap ratios and LAT flexion angle. The proposed model was trained and validated using 2212 knee plain radiographs from 1208 patients and an additional 1572 knee radiographs from 753 patients collected from six external centers for further external validation. For the internal validation cohort, the proposed AI model and clinicians showed high intraclass consistency coefficients (ICCs) for AP/LAT fibular head overlap and LAT knee flexion angle of 0.952, 0.895, and 0.993, respectively. For the external validation cohort, the ICCs were also high, with values of 0.934, 0.856, and 0.991, respectively. There were no significant differences between the AI model and clinicians in any of the three QC criteria, and the AI model required significantly less measurement time than clinicians. The experimental results demonstrated that the AI model performed comparably to clinicians and required less time. Therefore, the proposed AI-based model has great potential as a convenient tool for clinical practice by automating the QC procedure for knee radiographs.

Funder

the National Natural Science Foundation of China

the Special Military Medical Project of Shanghai Changzheng Hospital

Contract grant sponsor: Pyramid Talent Project of Shanghai Changzheng Hospital

Shenkang capacity enhancement project

Military Commission surface project

the National Health Commission Radiological Imaging Database Construction Project

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

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