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Article Abstract
Study Design: Retrospective cohort study.
Purpose: To evaluate the effectiveness of opportunistic osteoporosis screening using an artificial intelligence (AI) algorithm for detecting vertebral compression deformity (VCD >25%) and reduced bone mineral density (BMD) from routine chest computed tomography (CT) scans.
Overview Of Literature: Osteoporosis is an insidious metabolic disease that often remains asymptomatic for a long time, and is typically diagnosed due to the occurrence of complications. An approach for diagnosing osteoporosis based on routine CT examinations, including the use of AI services, is being actively studied.
Methods: Patients aged >50 years who underwent chest CT using the standard protocol between 09.06.2021 and 30.06.2021 at four inpatient and three outpatient clinics were retrospectively enrolled. The morphometric AI algorithm detected vertebral compression index and vertebrae density in Hounsfield units (HU). The AI algorithm was calibrated for BMD measurements using a phantom. Osteoporotic BMD was defined according to the American College of Radiology criteria (<80 mg/mL). The presence of vertebral fracture (VF) was verified using semiquantitative and algorithm-based qualitative methods by three radiologists, followed by two experts with 15 and 35 years of experience in the field.
Results: CT studies of 1,888 patients (mean age, 66.3 years) were included. The AI algorithm detected VCD in 336 patients (17.8%), with 105 (5.5%) having VF. Low BMD values were detected in 501 patients (26.5%; 31.0% of females, 18.6% of males).
Conclusions: We observed high diagnostic accuracy of opportunistic osteoporosis screening using AI algorithms for detecting VF and low BMD. AI-based opportunistic screening of osteoporosis and VF in chest CT scans can facilitate the identification of high-risk cohorts.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12242260 | PMC |
| http://dx.doi.org/10.31616/asj.2024.0314 | DOI Listing |
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