A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-rays and CT Scans Using Deep Learning and...

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Orthopedic Diagnostics

Background:

• Knee bone disease diagnosis relies on imaging modalities like X-ray, CT, and MRI.
• Advancements in artificial intelligence offer new avenues for image analysis and disease detection.
• A systematic review is needed to compare AI methodologies for knee bone imaging.

Purpose of the Study:

• To systematically review and compare deep learning (DL) and machine learning (ML) techniques for diagnosing knee bone diseases.
• To evaluate the performance of DL and ML in analyzing X-ray, CT, and MRI scans of knee bones.
• To provide insights for future research in AI-driven orthopedic diagnostics.

Main Methods:

• A comparative systematic literature review was conducted.
• 32 relevant research papers were analyzed.
• Studies utilizing DL and ML for knee bone imaging analysis were included.

Main Results:

• Deep learning techniques demonstrated superior performance in diagnosing knee bone diseases across X-ray, CT, and MRI modalities.
• The review compared the accuracy of different DL and ML approaches for knee bone imaging.
• Specific findings highlight the efficacy of DL for analyzing knee bone scans.

Conclusions:

• Deep learning is the most effective AI approach for diagnosing knee bone diseases using medical imaging.
• This review provides a foundation for developing advanced AI tools in orthopedic imaging.
• Future research should integrate information retrieval and big data techniques with AI for enhanced knee bone analysis.