Breaking the silence: AI’s contribution to detecting vertebral fractures in opportunistic CT scans in the elderly—a validation study

IF 3.1 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
Anna Spångeus, Tomas Bjerner, Maria Lindblom, Christoph Götz, Allan Hummer, Christoph Salzlechner, Mischa Woisetschläger
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引用次数: 0

Abstract

Summary

Vertebral fractures frequently go undetected in clinical practice. AI-assisted detection on CT scans demonstrates considerable promise, with a sensitivity of 86% and a specificity of 99%. The performance varied based on sex, and CT kernel, showing superior results in females and in scans using non-bone kernel protocols.

Purpose

Vertebral fractures (VFs) are highly underdiagnosed, necessitating the development of new identification methods for opportunistic screening in computed tomography (CT) scans. This study validated an AI algorithm (ImageBiopsy Lab [IBL], FLAMINGO) for detecting VFs in a geriatric cohort, with various subgroup analyses including different CT protocols.

Methods

The performance of the AI in detecting VFs was compared to assessments by two experienced radiologists. A total of 246 thoracic or abdominal CT scans, primarily conducted for purposes other than skeletal examination, were included in the study.

Results

The patients had a mean age of 84 years (range 62 to 103), with 42% being female. The AI demonstrated high accuracy (0.93), sensitivity (0.86), and specificity (0.99) in detecting moderate to severe VFs. Subgroup analysis revealed accuracy ranging from 0.88 to 0.96, with higher accuracy in females compared to males (0.96 vs. 0.89, p = 0.03) and in scans performed with non-bone kernel versus bone kernel protocols (0.96 vs. 0.88, p = 0.02). No significant differences were found for age, contrast phase, or spinal region.

Conclusion

The results indicate that the AI algorithm exhibits high performance in a geriatric setting. If effectively integrated with a fracture liaison service, this could enhance VF detection considerable in the future.

打破沉默:人工智能在老年人CT扫描中检测椎体骨折的贡献——一项验证研究
椎体骨折在临床实践中经常未被发现。CT扫描上的人工智能辅助检测显示出相当大的前景,灵敏度为86%,特异性为99%。表现因性别和CT核而异,在女性和使用非骨核协议的扫描中显示出更好的结果。目的:椎骨骨折(VFs)是高度漏诊的,需要开发新的识别方法,以便在计算机断层扫描(CT)中进行机会性筛查。本研究验证了一种人工智能算法(ImageBiopsy Lab [IBL], FLAMINGO)用于检测老年队列中的VFs,并进行了不同亚组分析,包括不同的CT方案。方法将人工智能检测VFs的性能与两位经验丰富的放射科医生的评估进行比较。该研究共包括246次胸部或腹部CT扫描,主要用于骨骼检查以外的目的。结果患者平均年龄84岁(62 ~ 103岁),女性占42%。人工智能在检测中重度VFs方面表现出较高的准确性(0.93)、灵敏度(0.86)和特异性(0.99)。亚组分析显示准确率在0.88到0.96之间,女性比男性准确率更高(0.96比0.89,p = 0.03),非骨核扫描比骨核扫描准确率更高(0.96比0.88,p = 0.02)。在年龄、对比期或脊柱区域方面没有发现显著差异。结论人工智能算法在老年环境下表现出良好的性能。如果与裂缝连接服务有效集成,这将大大提高未来的VF检测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Archives of Osteoporosis
Archives of Osteoporosis ENDOCRINOLOGY & METABOLISMORTHOPEDICS -ORTHOPEDICS
CiteScore
5.50
自引率
10.00%
发文量
133
期刊介绍: Archives of Osteoporosis is an international multidisciplinary journal which is a joint initiative of the International Osteoporosis Foundation and the National Osteoporosis Foundation of the USA. The journal will highlight the specificities of different regions around the world concerning epidemiology, reference values for bone density and bone metabolism, as well as clinical aspects of osteoporosis and other bone diseases.
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