人工智能模型在识别胸片椎体压缩性骨折方面的潜在临床实用性。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology Pub Date : 2025-02-01 DOI:10.1016/j.jacr.2024.08.026

Ankita Ghatak MSc , James M. Hillis MBBS, DPhil , Sarah F. Mercaldo PhD , Isabella Newbury-Chaet BSc , John K. Chin MD , Subba R. Digumarthy MBBS , Karen Rodriguez MD , Victorine V. Muse MD , Katherine P. Andriole PhD , Keith J. Dreyer DO, PhD , Mannudeep K. Kalra MBBS, MD , Bernardo C. Bizzo MD, PhD

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引用次数: 0

摘要

目的：评估 Annalise Enterprise CXR Triage Trauma 人工智能模型识别胸片上椎体压缩性骨折的能力及其解决未诊断的骨质疏松症及其治疗的潜力：这项回顾性研究使用了 2015 年至 2021 年间来自四家美国医院的 596 张连续队列胸片。每张照片都包括正面（前胸或后背）和侧面投影。这些X光片由最多三名胸部放射科医生以协商一致的方式评估是否存在椎体压缩性骨折。然后，模型对病例进行推断。此外，还进行了病历审查，以确定是否存在与骨质疏松症相关的 ICD-10 诊断代码，以及研究期间和额外一年随访期间的药物使用情况：该模型成功完成了 595 个病例（99.8%）的推断，其中包括 272 个阳性病例和 323 个阴性病例。该模型的接收者操作特征曲线下面积为 0.955（95% CI：0.939 至 0.968），灵敏度为 89.3%（95% CI：85.7 至 92.7%），特异度为 89.2%（95% CI：85.4 至 92.3%）。在236例有病历信息的真阳性病例（即模型正确识别的椎体压缩性骨折）中，只有86例（36.4%）确诊为椎体压缩性骨折，140例（59.3%）确诊为骨质疏松症或骨质疏松症；只有78例（33.1%）正在接受治疗骨质疏松症的药物：该模型能准确识别椎体压缩性骨折，灵敏度为 89.3%（95% CI：85.7% 至 92.7%），特异性为 89.2%（95% CI：85.4% 至 92.3%）。该模型的自动使用有助于识别未确诊的骨质疏松症患者，以及可能从服用疾病调节药物中获益的患者。

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The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs

Purpose

To assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment.

Materials and methods

This retrospective study used a consecutive cohort of 596 chest radiographs from four US hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections. These radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then performed inference on the cases. A chart review was also performed for the presence of osteoporosis-related International Classification of Diseases, 10th revision diagnostic codes and medication use for the study period and an additional year of follow-up.

Results

The model successfully completed inference on 595 cases (99.8%); these cases included 272 positive cases and 323 negative cases. The model performed with area under the receiver operating characteristic curve of 0.955 (95% confidence interval [CI]: 0.939-0.968), sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity 89.2% (95% CI: 85.4%-92.3%). Out of the 236 true-positive cases (ie, correctly identified vertebral compression fractures by the model) with available chart information, only 86 (36.4%) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1%) were receiving a disease-modifying medication for osteoporosis.

Conclusion

The model identified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity of 89.2% (95% CI: 85.4%-92.3%). Its automated use could help identify patients who have undiagnosed osteoporosis and who may benefit from taking disease-modifying medications.

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来源期刊

Journal of the American College of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

6.30

自引率

8.90%

发文量

312

审稿时长

34 days

期刊介绍： The official journal of the American College of Radiology, JACR informs its readers of timely, pertinent, and important topics affecting the practice of diagnostic radiologists, interventional radiologists, medical physicists, and radiation oncologists. In so doing, JACR improves their practices and helps optimize their role in the health care system. By providing a forum for informative, well-written articles on health policy, clinical practice, practice management, data science, and education, JACR engages readers in a dialogue that ultimately benefits patient care.