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Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data

Erdal Güngör¹, Husam Vehbi², Ahmetcan Cansın³

¹Department of Orthopaedics and Traumatology, Medipol University Esenler Hospital, Istanbul, Turkey.

Knee Surgery, Sports Traumatology, Arthroscopy : Official Journal of the ESSKA

|July 17, 2024

Summary

Advanced deep learning models, YOLOv8 and EfficientNetV2, effectively detect meniscal tears on MRI scans. This AI system aids in faster diagnosis and reduces physician workload, even with limited data.

Keywords:

YOLOv8 deep learning magnetic resonance imaging meniscus tear detection object detection

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Area of Science:

Orthopaedic imaging analysis
Artificial intelligence in radiology
Deep learning for medical diagnosis

Background:

Meniscal tears are common knee injuries requiring accurate diagnosis.
Magnetic resonance imaging (MRI) is crucial for visualizing meniscal pathology.
Current diagnostic methods can be time-consuming and require specialized expertise.

Purpose of the Study:

To evaluate the effectiveness of YOLOv8 and EfficientNetV2 for detecting meniscal tears on MRI.
To assess the performance of these deep learning models with a limited dataset.
To determine the potential of AI in improving the efficiency of meniscal tear diagnosis.

Main Methods:

Utilized a dataset of 642 knee MRI scans, annotated by orthopaedic surgeons.
Employed a two-stage deep learning approach: YOLOv8 for meniscus localization and EfficientNetV2 for tear detection.
Trained and validated the models on sagittal and coronal MRI views.

Main Results:

YOLOv8 achieved high performance in meniscus localization with mAP@50 scores of 0.98 (sagittal) and 0.985 (coronal).
EfficientNetV2 demonstrated excellent meniscal tear detection with AUC scores of 0.97 (sagittal) and 0.98 (coronal).
The models showed exceptional accuracy in identifying and localizing meniscal tears.

Conclusions:

State-of-the-art deep learning models (YOLOv8, EfficientNetV2) show promise for meniscal tear detection on MRI, despite small datasets.
The AI system generates instant, structured reports, improving diagnostic speed and interpretation.
This technology can enhance clinical decision-making and alleviate physician workload in orthopaedic radiology.