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人工智能辅助计算机检测在胸部 X 光日常实践中对呼吸科门诊胸部 CT 转诊率的影响。

Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic.

机构信息

Department of Radiology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea.

Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.

出版信息

Korean J Radiol. 2023 Sep;24(9):890-902. doi: 10.3348/kjr.2023.0255.

Abstract

OBJECTIVE

The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT).

MATERIALS AND METHODS

AI-CAD was implemented in clinical practice at the Seoul National University Hospital. CRs obtained from patients who visited the pulmonology outpatient clinics before (January-December 2019) and after (January-December 2020) implementation were included in this study. After implementation, the referring pulmonologist requested CRs with or without AI-CAD analysis. We conducted multivariable logistic regression analyses to evaluate the associations between using AI-CAD and the following study outcomes: the rate of chest CT referral, defined as request and actual acquisition of chest CT within 30 days after CR acquisition, and the CT referral rates separately for subsequent positive and negative CT results. Multivariable analyses included various covariates such as patient age and sex, time of CR acquisition (before versus after AI-CAD implementation), referring pulmonologist, nature of the CR examination (baseline versus follow-up examination), and radiology reports presence at the time of the pulmonology visit.

RESULTS

A total of 28546 CRs from 14565 patients (mean age: 67 years; 7130 males) and 25888 CRs from 12929 patients (mean age: 67 years; 6435 males) before and after AI-CAD implementation were included. The use of AI-CAD was independently associated with increased chest CT referrals (odds ratio [OR], 1.33; = 0.008) and referrals with subsequent negative chest CT results (OR, 1.46; = 0.005). Meanwhile, referrals with positive chest CT results were not significantly associated with AI-CAD use (OR, 1.08; = 0.647).

CONCLUSION

The use of AI-CAD for CR interpretation in pulmonology outpatients was independently associated with an increased frequency of overall referrals for chest CT scans and referrals with subsequent negative results.

摘要

目的

基于人工智能的计算机辅助检测(AI-CAD)在诊断准确性之外的临床影响仍不确定。我们旨在研究在日常实践中临床实施 AI-CAD 对胸部 X 线摄影(CR)解读的影响,以评估其对胸部计算机断层扫描(CT)转诊率的影响。

材料与方法

AI-CAD 在首尔国立大学医院临床实施。纳入本研究的 CR 来自于 2019 年 1 月至 12 月和 2020 年 1 月至 12 月期间就诊于呼吸科门诊的患者。实施后,转诊的呼吸科医生请求进行有或无 AI-CAD 分析的 CR。我们进行了多变量逻辑回归分析,以评估使用 AI-CAD 与以下研究结果之间的关联:定义为在获取 CR 后 30 天内请求和实际进行胸部 CT 的胸部 CT 转诊率,以及分别针对后续阳性和阴性 CT 结果的 CT 转诊率。多变量分析包括各种协变量,如患者年龄和性别、CR 获取时间(在 AI-CAD 实施之前与之后)、转诊的呼吸科医生、CR 检查的性质(基线与随访检查),以及在呼吸科就诊时放射学报告的存在情况。

结果

共纳入了 28546 例 CR 来自于 14565 名患者(平均年龄:67 岁;7130 名男性)和 25888 例 CR 来自于 12929 名患者(平均年龄:67 岁;6435 名男性),分别来自 AI-CAD 实施前后。使用 AI-CAD 与增加的胸部 CT 转诊(优势比[OR],1.33; = 0.008)和随后的阴性胸部 CT 结果转诊(OR,1.46; = 0.005)独立相关。同时,与阳性胸部 CT 结果相关的转诊与 AI-CAD 的使用没有显著相关性(OR,1.08; = 0.647)。

结论

在呼吸科门诊中使用 AI-CAD 进行 CR 解读与整体胸部 CT 扫描转诊频率的增加以及随后的阴性结果转诊独立相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e97/10462895/735364a49ccb/kjr-24-890-g001.jpg

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