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Automated rotator cuff tear classification using 3D convolutional neural network.

Eungjune Shim¹, Joon Yub Kim², Jong Pil Yoon³

¹Center for Bionics, Korea Institute of Science and Technology, Seoul, 02792, Korea.

Scientific Reports

|September 25, 2020

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This summary is machine-generated.

A deep learning 3D CNN model accurately diagnoses rotator cuff tears (RCT) from MRI scans, outperforming orthopedic experts. This AI tool aids in classifying tear size and visualizing location for better clinical diagnosis.

Area of Science:

Orthopedics
Radiology
Artificial Intelligence
Medical Imaging Analysis

Background:

Rotator cuff tear (RCT) is a prevalent shoulder injury.
Current diagnosis relies on manual interpretation of MRI scans by orthopedists.
Need for automated and accurate diagnostic tools for RCT.

Purpose of the Study:

To develop and evaluate a 3D convolutional neural network (CNN) for automated RCT diagnosis.
To classify RCT tear size and visualize tear location using deep learning.
To compare the AI model's performance against orthopedic experts.

Main Methods:

Utilized a Voxception-ResNet (VRN) based 3D CNN architecture.
Trained and tested the model on 2,124 patient MRI datasets.

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Classified RCT into five categories: None, Partial, Small, Medium, Large-to-Massive.

Employed 3D Class Activation Mapping (CAM) for tear visualization.

Main Results:

The 3D CNN achieved superior diagnostic accuracy compared to shoulder specialists and general orthopedists.
Achieved 92.5% binary accuracy vs. 76.4% (specialists) and 68.2% (general).
Demonstrated high sensitivity (0.94) and specificity (0.90).
3D CAM effectively visualized tear location and size.

Conclusions:

The proposed VRN-based 3D CNN shows significant potential for assisting in clinical RCT diagnosis.
AI models can achieve higher accuracy than human experts in specific diagnostic tasks.
This technology offers a feasible approach for improving the accuracy and efficiency of RCT diagnosis.