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Automatic Discoid Lateral Meniscus Diagnosis from Radiographs Based on Image Processing Tools and Machine Learning.

Xibai Li¹, Yan Sun¹, Juyang Jiao²

¹The Digital ART Lab of School of Software, Shanghai JiaoTong University, Shanghai 200240, China.

Journal of Healthcare Engineering

|May 10, 2021

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This study developed AI software to accurately diagnose discoid lateral menisci from knee radiographs, achieving high success rates in image segmentation and parameter measurement.

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Orthopedics

Background:

Discoid lateral meniscus is a congenital anomaly causing knee pain and instability.
Accurate diagnosis of discoid lateral meniscus is crucial for effective treatment.
Current diagnostic methods may have limitations in speed and accessibility.

Purpose of the Study:

To develop and validate a software implementation for diagnosing discoid lateral menisci using knee radiograph images.
To automate the measurement of key knee joint parameters for improved diagnostic accuracy.
To assess the performance of the developed software against manual measurements.

Main Methods:

A software tool was created incorporating machine learning (YOLOv3) for knee joint segmentation and image enhancement techniques (BM3D).

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Edge detection algorithms and a custom algorithm were used to model the knee joint and measure specific parameters.

The software processed 160 knee radiograph images from normal individuals and patients with discoid lateral menisci.

Main Results:

The software achieved high accuracy, with 99.65% correct image segmentation and 97.5% successful parameter measurement.
No significant differences were found between manual and automated measurements in discoid (P=0.28) and control (P=0.15) groups.
The software demonstrated robustness and a satisfying success rate on raw radiographs.

Conclusions:

The developed radiograph-image-analyzing software, utilizing AI tools, can reliably diagnose discoid lateral menisci.
This technology shows potential for building a comprehensive joint database for future diagnosis of knee joint diseases.
Automated analysis of knee radiographs offers a promising approach for diagnosing meniscal abnormalities.