Deep learning in rheumatological image interpretation.

Area of Science:

• Radiology and Rheumatology
• Medical Imaging Analysis
• Artificial Intelligence in Medicine

Background:

• Artificial intelligence (AI), particularly deep learning (DL), is increasingly impacting various fields, including initial applications in rheumatology.
• While DL may not always outperform traditional methods on low-dimensional numerical data, it demonstrates superior performance in image analysis, surpassing conventional techniques.
• The integration of DL in medical imaging necessitates an understanding of its technical background for effective clinical application.

Purpose of the Study:

• To provide an overview of deep learning techniques for automated image analysis in rheumatology.
• To review current deep learning applications in radiological imaging for rheumatic diseases.
• To critically assess the limitations, potential errors, and clinical consequences of DL for rheumatologists and radiologists.

Main Methods:

• Review of deep learning methodologies applicable to medical image analysis.
• Survey of published deep learning applications in rheumatological radiology.
• Critical evaluation of DL performance, limitations, and clinical implications.

Main Results:

• Deep learning shows significant success in image-based tasks, often outperforming established image-processing techniques.
• Current applications in rheumatology focus on detection, quantification, prediction, and monitoring of rheumatic diseases using radiological images.
• Potential pitfalls and limitations of DL in clinical practice were identified.

Conclusions:

• Deep learning offers powerful tools for analyzing radiological images in rheumatology, enhancing diagnostic and monitoring capabilities.
• Clinicians must develop a foundational understanding of DL to leverage its benefits and mitigate risks.
• Adaptation of clinical roles and collaborations is essential for integrating DL effectively into rheumatology practice.