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Background <p>Osteoarthritis (OA) is one of the most common joint diseases, affecting more than 500 million people worldwide. In recent decades, there has only been limited progress in terms of diagnosis and treatment. For a&#xa0;long time, OA was considered to be primarily a&#xa0;mechanically induced degenerative disease. However, more recent work has shown that OA is a&#xa0;heterogeneous condition that manifests in different phenotypes. Although artificial intelligence (AI) is becoming increasingly important in medical research, its specific application in the field of OA remains limited in clinical use.</p> Objectives <p>The aim of this review is to summarize the current approaches to phenotyping OA and to highlight the role of AI in the identification and classification of OA phenotypes.</p> Materials and methods <p>Selective literature review</p> Results <p>There are several promising applications of AI in OA diagnosis and assessment, such as automated assessment of cartilage damage or prediction of the need for arthroplasty. Close cooperation between orthopaedics, radiology, and AI experts is necessary to integrate AI models into clinical practice.</p> Conclusions <p>The use of AI to detect and assess OA-typical changes offers major potential to improve diagnostic imaging, clinical interpretation, and disease prognosis. Through more precise diagnoses and individualized prognoses, AI-based methods could significantly contribute to making treatment decisions more effective and, thus, optimizing patient care overall.</p>

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Differenzierung MR-tomographischer Arthrosephänotypen mithilfe von Techniken der Künstlichen Intelligenz

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Kirsten Schleid,
  • Assil-Ramin Alimy,
  • Tim Hoenig,
  • Simon Westfechtel,
  • Sven Nebelung,
  • Frank Timo Beil,
  • Tim Rolvien

摘要

Background

Osteoarthritis (OA) is one of the most common joint diseases, affecting more than 500 million people worldwide. In recent decades, there has only been limited progress in terms of diagnosis and treatment. For a long time, OA was considered to be primarily a mechanically induced degenerative disease. However, more recent work has shown that OA is a heterogeneous condition that manifests in different phenotypes. Although artificial intelligence (AI) is becoming increasingly important in medical research, its specific application in the field of OA remains limited in clinical use.

Objectives

The aim of this review is to summarize the current approaches to phenotyping OA and to highlight the role of AI in the identification and classification of OA phenotypes.

Materials and methods

Selective literature review

Results

There are several promising applications of AI in OA diagnosis and assessment, such as automated assessment of cartilage damage or prediction of the need for arthroplasty. Close cooperation between orthopaedics, radiology, and AI experts is necessary to integrate AI models into clinical practice.

Conclusions

The use of AI to detect and assess OA-typical changes offers major potential to improve diagnostic imaging, clinical interpretation, and disease prognosis. Through more precise diagnoses and individualized prognoses, AI-based methods could significantly contribute to making treatment decisions more effective and, thus, optimizing patient care overall.