From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis

Area of Science:

• Biomedical Imaging
• Artificial Intelligence
• Body Composition Analysis

Background:

• Body composition (BC) assessment, including fat, muscle, and bone, is increasingly recognized for its impact on oncologic and non-oncologic health outcomes.
• Non-invasive methods like CT and MR imaging offer quantitative BC data for opportunistic screening and health management.
• Current clinical use of BC analysis is limited by manual segmentation challenges and complex data interpretation.

Purpose of the Study:

• To review recent advancements in body composition analysis and artificial intelligence.
• To highlight the synergy between AI and BC assessment for improved health management.
• To discuss current limitations and future challenges in the clinical application of AI-driven BC analysis.

Main Methods:

• Review of recent literature on body composition assessment and artificial intelligence applications in medicine.
• Focus on AI-driven automation of segmentation and analysis of cross-sectional imaging (CT and MR).
• Exploration of AI's role in opportunistic BC screening and personalized medicine.

Main Results:

• Artificial intelligence can automate and simplify BC analysis, overcoming limitations of manual segmentation.
• AI facilitates opportunistic BC evaluation during routine CT scans, aiding in early detection and prevention.
• AI enhances BC's potential in personalized medicine for diagnosis and treatment strategies.

Conclusions:

• The integration of AI with body composition analysis shows significant promise for advancing healthcare.
• AI-driven BC assessment can improve patient management from diagnosis to personalized treatments.
• Addressing current limitations and future challenges is crucial for the routine clinical adoption of AI in BC analysis.