A machine learning method for precise detection of spine bone mineral density

作者全名：Wang, Jiayi; Yang, Guoqing; Liu, Siyan; Qiao, Renjie; Cao, Yi; Fan, Bosha; Yang, Haoyan; Lyu, Fajin

作者地址：[Wang, Jiayi; Liu, Siyan] Chongqing Med Univ, Int Med Coll, Chongqing 401331, Peoples R China; [Yang, Guoqing] Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China; [Qiao, Renjie] Chongqing Med Univ, Dept Orthoped, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Cao, Yi; Fan, Bosha; Yang, Haoyan] Chongqing Med Univ, Clin Coll 2, Chongqing 401331, Peoples R China; [Lyu, Fajin] Chongqing Med Univ, Dept Orthoped, Affiliated Hosp 1, Chongqin 400016, Peoples R China

通信作者：Lyu, F (通讯作者)，Chongqing Med Univ, Dept Orthoped, Affiliated Hosp 1, Chongqin 400016, Peoples R China.

来源：ALEXANDRIA ENGINEERING JOURNAL

ESI学科分类：ENGINEERING

WOS号：WOS:001239641400001

JCR分区：Q1

影响因子：6.2

年份：2024

卷号：98

期号：　

开始页：290

结束页：301

文献类型：Article

关键词：HRCT; Machine learning; Changes in bone mineral density

摘要：Osteoporosis is prevalent among the elderly and requires precise diagnostic approaches for effective treatment and prevention. This work introduces a machine learning-based method for the accurate grading of osteoporosis via the analysis of high-resolution computed tomography (HRCT) images of the spine. This approach allows for the precise extraction of the trabecular component of vertebral bodies, thereby enabling accurate measurement of bone density. Evaluated in 183 individuals, our method demonstrated enhanced stability in bone density prediction and reduced bias when juxtaposed with traditional random sampling techniques. Despite the inherent risk of overestimation, the machine learning model more accurately approximates the actual bone quality compared to conventional clinical methods, which typically involve the random extraction of 3-4 trabecular bone slices from the spine. These findings offer a novel approach to the clinical assessment of osteoporosis, underscoring the significant potential of integrating machine learning into existing medical image analysis workflows.

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