Asian spine journal · 2025
Opportunistic screening for osteoporosis using artificial intelligence-based morphometric analysis of chest computed tomography images: a retrospective multi-center study in Russia leveraging the COVID-19 pandemic.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Petraikin Alexey Vladimirovich, Pickhardt Perry Joseph, Belyaev Mikhail Gennadevich, Belaya Zhanna Evgenievna, Pisov Maxim Evgenievich, Bukharaev Alim Niyazovich, Zakharov Aleksei Andreevich, Kudryavtsev Nikita Dmitrievich, Bobrovskaya Tatiana Mikhailovna, Semenov Dmitry Sergeevich, Akhmad Ekaterina Sergeevna, Artyukova Zlata Romanovna, Abuladze Liya Ruslanovna, Nizovtsova Ludmila Arsenevna, Blokhin Ivan Andreevich, Vladzymyrskyy Anton Vyacheslavovich, Vasilev Yuriy Aleksandrovich
Аннотация
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.
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.