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Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial
Wongthawat Liawrungrueang1, Pyeoungkee Kim2, Vit Kotheeranurak3,4
1Department of Orthopaedics, School of Medicine, University of Phayao, Phayao 56000, Thailand.
Diagnostics (Basel, Switzerland)
|February 25, 2023
Summary
Deep learning accurately grades intervertebral disc degeneration (IDD) using MRI scans. This artificial intelligence approach offers a reliable and efficient method for diagnosing low back pain causes.
Area of Science:
- Radiology
- Artificial Intelligence
- Medical Imaging
Background:
- Intervertebral disc degeneration (IDD) is a primary cause of low back pain.
- Magnetic resonance imaging (MRI) is the standard diagnostic tool for IDD.
- Automated detection of IDD using AI shows promise for efficient diagnosis.
Purpose of the Study:
- To evaluate deep convolutional neural networks (CNNs) for detecting, classifying, and grading IDD.
- To assess the reliability of AI in grading IDD based on the Pfirrmann system.
Main Methods:
- A dataset of 1000 lumbar T2-weighted MRI images was used.
- 800 images formed the training set, and 200 comprised the test set.
- A radiologist annotated the training data using the Pfirrmann grading system.
Main Results:
- The deep CNN model achieved over 95% accuracy in detecting and classifying lumbar IDD.
- The model successfully graded intervertebral discs based on the Pfirrmann system.
Conclusions:
- Deep CNNs reliably automate the grading of lumbar IDD from routine MRIs.
- This AI-driven method provides a rapid and efficient approach for IDD classification.
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