Applicability of artificial intelligence-based computer-aided detection (AI-CAD) for pulmonary tuberculosis to community-based active case finding.

IF 3.6 Q1 TROPICAL MEDICINE
Kosuke Okada, Norio Yamada, Kiyoko Takayanagi, Yuta Hiasa, Yoshiro Kitamura, Yutaka Hoshino, Susumu Hirao, Takashi Yoshiyama, Ikushi Onozaki, Seiya Kato
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引用次数: 0

Abstract

Background: Artificial intelligence-based computer-aided detection (AI-CAD) for tuberculosis (TB) has become commercially available and several studies have been conducted to evaluate the performance of AI-CAD for pulmonary tuberculosis (TB) in clinical settings. However, little is known about its applicability to community-based active case-finding (ACF) for TB.

Methods: We analysed an anonymized data set obtained from a community-based ACF in Cambodia, targeting persons aged 55 years or over, persons with any TB symptoms, such as chronic cough, and persons at risk of TB, including household contacts. All of the participants in the ACF were screened by chest radiography (CXR) by Cambodian doctors, followed by Xpert test when they were eligible for sputum examination. Interpretation by an experienced chest physician and abnormality scoring by a newly developed AI-CAD were retrospectively conducted for the CXR images. With a reference of Xpert-positive TB or human interpretations, receiver operating characteristic (ROC) curves were drawn to evaluate the AI-CAD performance by area under the ROC curve (AUROC). In addition, its applicability to community-based ACFs in Cambodia was examined.

Results: TB scores of the AI-CAD were significantly associated with the CXR classifications as indicated by the severity of TB disease, and its AUROC as the bacteriological reference was 0.86 (95% confidence interval 0.83-0.89). Using a threshold for triage purposes, the human reading and bacteriological examination needed fell to 21% and 15%, respectively, detecting 95% of Xpert-positive TB in ACF. For screening purposes, we could detect 98% of Xpert-positive TB cases.

Conclusions: AI-CAD is applicable to community-based ACF in high TB burden settings, where experienced human readers for CXR images are scarce. The use of AI-CAD in developing countries has the potential to expand CXR screening in community-based ACFs, with a substantial decrease in the workload on human readers and laboratory labour. Further studies are needed to generalize the results to other countries by increasing the sample size and comparing the AI-CAD performance with that of more human readers.

基于人工智能的肺结核计算机辅助检测(AI-CAD)在社区主动病例发现中的适用性。
背景:基于人工智能的肺结核计算机辅助检测(AI-CAD)已经投入商业使用,并且已经开展了几项研究来评估 AI-CAD 在临床环境中治疗肺结核的效果。然而,人们对其在基于社区的结核病主动病例查找(ACF)中的适用性知之甚少:我们分析了从柬埔寨一个社区 ACF 中获得的匿名数据集,该 ACF 的目标人群是 55 岁或以上的老人、有任何结核病症状(如慢性咳嗽)的人以及结核病高危人群,包括家庭接触者。所有参加 ACF 的人都由柬埔寨医生进行了胸部 X 光检查(CXR),并在符合痰液检查条件时进行了 Xpert 检测。由经验丰富的胸科医生对 CXR 图像进行解读,并使用新开发的 AI-CAD 对异常情况进行评分。以 Xpert 阳性肺结核或人工判读为参考,绘制接收器操作特征曲线(ROC),通过 ROC 曲线下面积(AUROC)评估 AI-CAD 的性能。此外,还考察了其在柬埔寨社区 ACF 中的适用性:结果:AI-CAD 的结核病评分与根据结核病严重程度进行的 CXR 分级有明显相关性,其作为细菌学参考的 AUROC 为 0.86(95% 置信区间为 0.83-0.89)。以分流为阈值,所需的人工读片和细菌学检查分别降至 21% 和 15%,可检测出 95% 的 ACF Xpert 阳性肺结核。就筛查而言,我们可以发现 98% 的 Xpert 阳性肺结核病例:结论:AI-CAD 适用于结核病高负担地区的社区 ACF,因为这些地区缺乏有经验的 CXR 图像阅读者。在发展中国家使用 AI-CAD 有可能扩大社区 ACF 的 CXR 筛查范围,同时大幅减少人工读片员和实验室劳动力的工作量。还需要进一步研究,通过增加样本量和比较人工智能-计算机断层扫描与更多人工读片机的性能,将结果推广到其他国家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Tropical Medicine and Health
Tropical Medicine and Health TROPICAL MEDICINE-
CiteScore
7.00
自引率
2.20%
发文量
90
审稿时长
11 weeks
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