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.

IF 2.7 Q2 ORTHOPEDICS Asian Spine Journal Pub Date : 2025-06-01 Epub Date: 2025-03-05 DOI:10.31616/asj.2024.0314
Alexey Vladimirovich Petraikin, Perry Joseph Pickhardt, Mikhail Gennadevich Belyaev, Zhanna Evgenievna Belaya, Maxim Evgenievich Pisov, Alim Niyazovich Bukharaev, Aleksei Andreevich Zakharov, Nikita Dmitrievich Kudryavtsev, Tatiana Mikhailovna Bobrovskaya, Dmitry Sergeevich Semenov, Ekaterina Sergeevna Akhmad, Zlata Romanovna Artyukova, Liya Ruslanovna Abuladze, Ludmila Arsenevna Nizovtsova, Ivan Andreevich Blokhin, Anton Vyacheslavovich Vladzymyrskyy, Yuriy Aleksandrovich Vasilev
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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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利用基于人工智能的胸部计算机断层扫描图像形态计量学分析对骨质疏松症进行机会性筛查:利用COVID-19大流行在俄罗斯进行的一项回顾性多中心研究
研究设计:回顾性队列研究。目的:评估利用人工智能(AI)算法检测常规胸部计算机断层扫描(CT)椎体压缩畸形(VCD >25%)和骨密度降低(BMD)的机会性骨质疏松症筛查的有效性。文献综述:骨质疏松症是一种隐匿的代谢性疾病,通常长期无症状,通常因并发症的发生而被诊断。目前正在积极研究一种基于常规CT检查的骨质疏松症诊断方法,包括使用人工智能服务。方法:回顾性入选于2021年6月9日至2021年6月30日在4家住院和3家门诊采用标准方案行胸部CT检查的患者,年龄为bb0 ~ 50岁。形态测量AI算法检测Hounsfield单位(HU)的椎体压缩指数和椎体密度。人工智能算法使用模拟模型进行了BMD测量校准。骨质疏松性骨密度根据美国放射学会标准定义(结果:纳入1888例患者(平均年龄66.3岁)的CT研究)。AI算法检测出VCD患者336例(17.8%),VF患者105例(5.5%)。501例患者检测到低BMD值(26.5%;女性占31.0%,男性占18.6%)。结论:我们观察到使用人工智能算法检测VF和低BMD的机会性骨质疏松症筛查的诊断准确性很高。基于人工智能的胸部CT扫描骨质疏松症和VF的机会性筛查有助于识别高危人群。
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来源期刊
Asian Spine Journal
Asian Spine Journal ORTHOPEDICS-
CiteScore
5.10
自引率
4.30%
发文量
108
审稿时长
24 weeks
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