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An AI-Based Image Quality Control Framework for Knee Radiographs.

Hongbiao Sun¹, Wenwen Wang¹, Fujin He²

¹Department of Radiology, Shanghai Changzheng Hospital, Naval Medical University, No.415 Fengyang Road, Huangpu District, Shanghai, 200003, China.

Journal of Digital Imaging

|June 2, 2023

Summary

An artificial intelligence (AI) model automates knee radiograph quality control (QC), matching clinician accuracy while reducing measurement time. This AI tool offers a faster, more objective approach to QC in clinical practice.

Keywords:

Artificial intelligence Deep learning Image quality control Knee plain radiograph

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Published on: August 2, 2024

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Manual quality control (QC) of knee radiographs is subjective and time-consuming.
Accurate QC is essential for diagnosing knee diseases from radiographs.

Purpose of the Study:

To develop an automated AI model for knee radiograph QC.
To assess the performance of the AI model compared to clinicians.

Main Methods:

A high-resolution net (HR-Net) AI model was developed to identify key points on knee radiographs.
Geometric calculations were used to derive QC criteria: AP/LAT overlap ratios and LAT flexion angle.
The model was trained and validated on a large dataset of knee radiographs, including external validation.

Main Results:

The AI model demonstrated high intraclass consistency coefficients (ICCs) comparable to clinicians for AP/LAT fibular head overlap and LAT knee flexion angle in both internal and external validation cohorts.
No significant differences were found between the AI model and clinicians across the three QC criteria.
The AI model significantly reduced measurement time compared to clinicians.

Conclusions:

The AI-based model performs comparably to clinicians in knee radiograph QC.
The AI model offers a faster and potentially more objective alternative for clinical QC.
This AI tool shows significant potential for improving efficiency in clinical practice.