Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review

• Published in: Osteoporosis international Vol. 35; no. 10; pp. 1681 - 1692
• Main Authors: Paderno, Alberto, Ataide Gomes, Elmer Jeto, Gilberg, Leonard, Maerkisch, Leander, Teodorescu, Bianca, Koç, Ali Murat, Meyer, Mathias
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Automation; Bone density; Bone Density - physiology; Bone Diseases, Metabolic - diagnostic imaging; Bone Diseases, Metabolic - physiopathology; Bone mineral density; Cancellous bone; Computed tomography; Deep Learning; Endocrinology; Humans; Machine Learning; Mass Screening - methods; Medical imaging; Medicine; Medicine & Public Health; Orthopedics; Osteopenia; Osteoporosis; Osteoporosis - diagnostic imaging; Osteoporosis - physiopathology; Review; Reviews; Rheumatology; Risk Assessment - methods; Spine - diagnostic imaging; Spine - physiopathology; Tomography, X-Ray Computed - methods; Vertebrae; Osteoporosis; Bone mineral density; Computer tomography; Spine; Artificial intelligence; Opportunistic screening
• ISSN: 0937-941X; 1433-2965; 1433-2965
• DOI: 10.1007/s00198-024-07179-1

Purpose This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans. Methods PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings. Results Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection. Conclusions The review highlights AI’s promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost.