Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Orthopedics

Background:

• Knee osteoarthritis (OA) is a prevalent condition causing pain and functional impairment.
• Current manual grading of knee OA from X-rays is subjective, time-consuming, and inefficient for high-throughput settings.
• Radiologists use the Kellgren-Lawrence grading scheme for severity assessment.

Purpose of the Study:

• To introduce an end-to-end deep learning model for the automatic diagnosis of knee OA.
• To develop a computationally efficient method for knee OA detection and severity grading.
• To overcome the limitations of manual radiological assessment.

Main Methods:

• Utilized Faster R-CNN (Region Proposal Network and Fast R-CNN) for simultaneous image localization and classification.
• Employed Convolutional Neural Networks (CNNs) for feature extraction and filtering irrelevant image information.
• Implemented a novel loss function to address class imbalance and used larger anchors for improved object matching.

Main Results:

• The proposed model achieved a mean average precision of approximately 0.82.
• Demonstrated high performance with sensitivity above 78% and specificity above 94%.
• Achieved a processing time of 0.33 seconds per image, indicating a favorable accuracy-speed trade-off.

Conclusions:

• The developed end-to-end deep learning model offers an efficient and automatic solution for knee OA diagnosis.
• This model shows significant potential as a computer-aided diagnosis tool in clinical practice.
• The approach enhances diagnostic accuracy and efficiency in managing knee osteoarthritis.