计算机视觉在骨质疏松性椎体骨折风险预测中的应用:系统综述。

IF 3.8 2区 医学 Q1 CLINICAL NEUROLOGY Neurospine Pub Date : 2023-12-01 Epub Date: 2023-12-31 DOI:10.14245/ns.2347022.511
Anthony K Allam, Adrish Anand, Alex R Flores, Alexander E Ropper
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引用次数: 0

摘要

骨质疏松性脊椎骨折(OVFs)是一个重大的健康问题,与发病率、死亡率和生活质量下降有关。传统的椎体骨质疏松性骨折风险评估工具,如骨矿物质密度(BMD),只能捕捉到风险概况的一小部分。人工智能,特别是计算机视觉,通过对视频、组织病理学切片和放射扫描的分析,已经在其他医学领域掀起了一场革命。在这篇综述中,我们概述了计算机视觉算法和目前用于预测 OVF 风险的计算机视觉模型。我们重点介绍了计算机视觉在 OVF 风险预测中的临床应用、未来发展方向和局限性。
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Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review.

Osteoporotic vertebral fractures (OVFs) are a significant health concern linked to increased morbidity, mortality, and diminished quality of life. Traditional OVF risk assessment tools like bone mineral density (BMD) only capture a fraction of the risk profile. Artificial intelligence, specifically computer vision, has revolutionized other fields of medicine through analysis of videos, histopathology slides and radiological scans. In this review, we provide an overview of computer vision algorithms and current computer vision models used in predicting OVF risk. We highlight the clinical applications, future directions and limitations of computer vision in OVF risk prediction.

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来源期刊
Neurospine
Neurospine Multiple-
CiteScore
5.80
自引率
18.80%
发文量
93
审稿时长
10 weeks
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