Development and reporting of artificial intelligence in osteoporosis management
Guillaume Gatineau1, Enisa Shevroja1, Colin Vendrami1
1Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland.
Summary
Artificial intelligence (AI) shows promise in osteoporosis research, aiding diagnosis and decision-making. However, inconsistent reporting and quality variations necessitate standardized practices for reliable AI integration in bone health.
Area of Science:
- Orthopedics and Bone Health
- Medical Artificial Intelligence (AI)
- Radiology and Imaging Analysis
Background:
- The exponential growth of medical data and computational power fuels AI applications in bone and osteoporosis research.
- Increasing AI studies necessitate transparent model development and reporting strategies for reliable clinical integration.
Purpose of the Study:
- To conduct a comprehensive review and systematic quality assessment of AI articles in osteoporosis research.
- To highlight recent advancements and identify trends in AI applications for bone health.
Main Methods:
- Systematic search of PubMed database (December 2020 - February 2023) for AI and osteoporosis-related articles.
- Quality assessment of 97 identified studies using 12 items from the Minimum Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist.
- Categorization of studies into bone properties assessment, osteoporosis classification, fracture detection, risk prediction, and bone segmentation.
Main Results:
- 97 articles were identified across five key areas: bone properties (11), classification (26), fracture detection (25), risk prediction (24), and segmentation (11).
- Average quality scores varied, with bone segmentation (9.0/11) and bone properties assessment (8.9/11) scoring highest, while risk prediction (7.6/11) and osteoporosis classification (7.8/11) scored lower.
- AI-driven clinical decision support emerged as a sixth area, focusing on improving clinician efficiency and patient outcomes.
Conclusions:
- Significant disparities in study quality and a lack of standardized reporting practices were observed.
- Despite limitations, AI models demonstrate potential for earlier osteoporosis diagnosis and improved clinical decision-making.
- Addressing bias in AI model assessment is crucial for building confidence and enhancing clinical workflows in bone health.
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