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基于人工智能的即时肺超声用于筛查 COVID-19 肺炎:与 CT 扫描的比较。

Artificial intelligence-based point-of-care lung ultrasound for screening COVID-19 pneumoniae: Comparison with CT scans.

机构信息

Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.

Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.

出版信息

PLoS One. 2023 Mar 16;18(3):e0281127. doi: 10.1371/journal.pone.0281127. eCollection 2023.

Abstract

BACKGROUND

Although lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19).

METHODS

We enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive. The sample size calculation was not performed given the retrospective all-comer nature of the study.

RESULTS

A total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The CT-validated pneumonia was seen in 71.4% of patients at total 577 lung zones (53.3%). The 12-zone AI-POCUS for detecting CT-validated pneumonia in the patient-level showed the accuracy of 94.5% (85.1%- 98.1%), sensitivity of 92.3% (79.7%- 97.3%), specificity of 100% (80.6%- 100%), positive predictive value of 95.0% (89.6% - 97.7%), and Kappa of 0.33 (0.27-0.40). When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9% (72.2%- 91.3%), 77.5% (62.5%- 87.7%), and 100% (80.6%- 100%), respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3% (61.4%- 69.1%), 37.2% (32.0%- 42.7%), and 97.8% (95.2%- 99.0%), respectively.

INTERPRETATION

AI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.

摘要

背景

尽管肺部超声已被报道为一种便携、经济有效且准确的方法来检测肺炎,但由于其解释难度,尚未得到广泛应用。在这里,我们旨在研究一种新型人工智能基于点的肺部超声(AI-POCUS)在 2019 年冠状病毒病(COVID-19)中的有效性。

方法

我们纳入了 2021 年 8 月和 9 月连续因 COVID-19 入院且接受计算机断层扫描(CT)的患者。在 CT 扫描后 24 小时内,由一位新手观察者使用袖珍设备进行 12 区 AI-POCUS。还对 15 名对照受试者进行了扫描。此外,还评估了不包括背部胸部的简化 8 区扫描的准确性。一个肺区中检测到三个以上的 B 线被认为是区域水平阳性,任何肺区中存在阳性 AI-POCUS 被认为是患者水平阳性。鉴于该研究的回顾性性质,未进行样本量计算。

结果

使用 AI-POCUS 评估了 56 名受试者的 577 个肺区(59.4±14.8 岁,23%为女性)。疾病发作的平均天数为 9 天,14%的患者需要机械通气。在 577 个肺区中,有 71.4%的患者 CT 验证有肺炎(53.3%)。在患者水平上,用于检测 CT 验证肺炎的 12 区 AI-POCUS 显示出 94.5%(85.1%-98.1%)的准确性、92.3%(79.7%-97.3%)的灵敏度、100%(80.6%-100%)的特异性、95.0%(89.6%-97.7%)的阳性预测值和 0.33(0.27-0.40)的 Kappa 值。简化为 8 区扫描时,准确性、灵敏度和特异性分别为 83.9%(72.2%-91.3%)、77.5%(62.5%-87.7%)和 100%(80.6%-100%)。AI-POCUS 的区域水平准确性、灵敏度和特异性分别为 65.3%(61.4%-69.1%)、37.2%(32.0%-42.7%)和 97.8%(95.2%-99.0%)。

结论

使用新型袖珍超声系统的 AI-POCUS 即使由新手观察者使用,也与 CT 验证的 COVID-19 肺炎具有极好的一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/10019704/a399e59a219c/pone.0281127.g001.jpg

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