Department of Administration, Mayo Clinic, Phoenix, Arizona, USA.
Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.
Med Phys. 2023 Jul;50(7):4296-4307. doi: 10.1002/mp.16230. Epub 2023 Feb 14.
While low bone density is a major burden on US health system, current osteoporosis screening guidelines by the US Preventive Services Task Force are limited to women aged ≥65 and all postmenopausal women with certain risk factors. Even within recommended screening groups, actual screening rates are low (<26%) and vary across socioeconomic groups. The proposed model can opportunistically screen patients using abdominal CT studies for low bone density who may otherwise go undiagnosed.
To develop an artificial intelligence (AI) model for opportunistic screening of low bone density using both contrast and non-contrast abdominopelvic computed tomography (CT) exams, for the purpose of referral to traditional bone health management, which typically begins with dual energy X-ray absorptiometry (DXA).
We collected 6083 contrast-enhanced CT imaging exams paired with DXA exams within ±6 months documented between May 2015 and August 2021 in a single institution with four major healthcare practice regions. Our fusion AI pipeline receives the coronal and axial plane images of a contrast enhanced abdominopelvic CT exam and basic patient demographics (age, gender, body cross section lengths) to predict risk of low bone mass. The models were trained on lumbar spine T-scores from DXA exams and tested on multi-site imaging exams. The model was again tested in a prospective group (N = 344) contrast-enhanced and non-contrast-enhanced studies.
The models were evaluated on the same test set (1208 exams)-(1) Baseline model using demographic factors from electronic medical records (EMR) - 0.7 area under the curve of receiver operator characteristic (AUROC); Imaging based models: (2) axial view - 0.83 AUROC; (3) coronal view- 0.83 AUROC; (4) Fusion model-Imaging + demographic factors - 0.86 AUROC. The prospective test yielded one missed positive DXA case with a hip prosthesis among 23 positive contrast-enhanced CT exams and 0% false positive rate for non-contrast studies. Both positive cases among non-contrast enhanced CT exams were successfully detected. While only about 8% patients from prospective study received a DXA exam within 2 years, about 30% were detected with low bone mass by the fusion model, highlighting the need for opportunistic screening.
The fusion model, which combines two planes of CT images and EMRs data, outperformed individual models and provided a high, robust diagnostic performance for opportunistic screening of low bone density using contrast and non-contrast CT exams. This model could potentially improve bone health risk assessment with no additional cost. The model's handling of metal implants is an ongoing effort.
尽管低骨密度是美国医疗系统的主要负担，但美国预防服务工作组的现行骨质疏松症筛查指南仅限于年龄≥65 岁的女性和所有有特定风险因素的绝经后女性。即使在推荐的筛查组中，实际的筛查率也很低（<26%），并且在社会经济群体之间存在差异。所提出的模型可以利用腹部 CT 研究对低骨密度患者进行机会性筛查，这些患者否则可能无法被诊断出来。
开发一种人工智能（AI）模型，利用对比增强和非对比增强的腹部 CT 检查对低骨密度进行机会性筛查，目的是转介至传统的骨骼健康管理，传统的骨骼健康管理通常从双能 X 线吸收法（DXA）开始。
我们在一个拥有四个主要医疗实践区域的机构中收集了 6083 例在 2015 年 5 月至 2021 年 8 月期间进行的伴或不伴双能 X 线吸收法检查的对比增强 CT 成像检查，这些检查在±6 个月内记录在案。我们的融合人工智能管道接收对比增强的腹部 CT 检查的冠状面和轴面图像以及基本的患者人口统计学数据（年龄、性别、身体横断面长度），以预测低骨量的风险。模型在 DXA 检查的腰椎 T 评分上进行训练，并在多站点成像检查上进行测试。该模型在一个前瞻性的 344 例对比增强和非对比增强研究中再次进行了测试。
模型在相同的测试集（1208 例）上进行了评估：（1）使用电子病历（EMR）中的人口统计学因素的基线模型-0.7 接收器操作特征（ROC）曲线下面积（AUROC）；基于影像学的模型：（2）轴面视图-0.83 AUROC；（3）冠状视图-0.83 AUROC；（4）融合模型-影像学+人口统计学因素-0.86 AUROC。前瞻性测试在 23 例阳性增强 CT 检查中漏诊了 1 例阳性 DXA 检查的病例，而在非增强 CT 检查中无假阳性率。非增强 CT 检查中的阳性病例均被成功检出。虽然前瞻性研究中只有约 8%的患者在 2 年内接受了 DXA 检查，但融合模型发现约 30%的患者存在低骨量，这突出了机会性筛查的必要性。
融合模型结合了两个 CT 图像平面和 EMR 数据，其性能优于单独的模型，并且在使用对比增强和非对比增强 CT 检查对低骨密度进行机会性筛查方面提供了高而稳健的诊断性能。该模型有可能在不增加成本的情况下改善骨骼健康风险评估。模型对金属植入物的处理仍在进行中。
