ArticleJMIR formative research2025
Implementation of Fully Automated AI-Integrated System for Body Composition Assessment on Computed Tomography for Opportunistic Sarcopenia Screening: Multicenter Prospective Study.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Imaging-Based Markers of Sarcopenia in Lower Limb Ischemia: A Systematic Review.Journal of clinical medicine · 2026Review
- Automated L3 Skeletal Muscle Segmentation for the Evaluation of Sarcopenia: Development and Independent Validation of an Ensemble-Based 2D nnU-Net Pipeline in a Complex Liver Disease Cohort.Muscles (Basel, Switzerland) · 2026Article
- Clinical significance and future applications of standardized imaging parameters for assessing paraspinal muscle fatty infiltration in lumbar degenerative disease: a narrative review.Journal of orthopaedic surgery and research · 2026Review
- Standardizing attenuation across tube voltages and vertebral levels for opportunistic osteoporosis screening on low-dose chest CT.Radiology advances · 2026Article
- Artificial intelligence vs human opportunistic detection of vertebral fractures in routine CT scans: results of a pilot study.Hormones (Athens, Greece) · 2026Article
- CT Body Composition Changes Predict Survival in Immunotherapy-Treated Cancer Patients: A Retrospective Cohort Study.Cancers · 2026Article
- Artificial intelligence standardizes CT-based body composition analysis in breast cacer to address methodological heterogeneity.Frontiers in oncology · 2026Review
- Article
- Sarcopenia in Interventional Radiology: An Opportunistic Imaging Biomarker for Patient Outcomes and Procedural Planning.Muscles (Basel, Switzerland) · 2025Review
- Body Composition and Survival in Locally Advanced Rectal Cancer Patients Treated with Neoadjuvant Radiochemotherapy.Nutrients · 2025Article
- AI-Enhanced Lower Extremity X-Ray Segmentation: A Promising Tool for Sarcopenia Diagnosis.Healthcare (Basel, Switzerland) · 2025Article
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10 authors.
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Abstract
Background: Opportunistic computed tomography (CT) screening for the evaluation of sarcopenia and myosteatosis has been gaining emphasis. A fully automated artificial intelligence (AI)-integrated system for body composition assessment on CT scans is a prerequisite for effective opportunistic screening. However, no study has evaluated the implementation of fully automated AI systems for opportunistic screening in real-world clinical practice for routine health check-ups. Objective: The aim of this study is to evaluate the performance and clinical utility of a fully automated AI-integrated system for body composition assessment on opportunistic CT during routine health check-ups. Methods: This prospective multicenter study included 537 patients who underwent routine health check-ups across 3 institutions. Our AI algorithm models are composed of selecting L3 slice and segmenting muscle and fat area in an end-to-end manner. The AI models were integrated into the Picture Archiving and Communication System (PACS) at each institution. Technical success rate, processing time, and segmentation accuracy in Dice similarity coefficient were assessed. Body composition metrics were analyzed across age and sex groups. Results: The fully automated AI-integrated system successfully retrieved anonymized CT images from the PACS, performed L3 selection and segmentation, and provided body composition metrics, including muscle quality maps and muscle age. The technical success rate was 100% without any failed cases requiring manual adjustment. The mean processing time from CT acquisition to report generation was 4.12 seconds. Segmentation accuracy comparing AI results and human expert results was 97.4%. Significant age-related declines in skeletal muscle area and normal-attenuation muscle area were observed, alongside increases in low-attenuation muscle area and intramuscular adipose tissue. Conclusions: Implementation of the fully automated AI-integrated system significantly enhanced opportunistic sarcopenia screening, achieving excellent technical success and high segmentation accuracy without manual intervention. This system has the potential to transform routine health check-ups by providing rapid and accurate assessments of body composition.
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