Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation

Abstract

Background: Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics.

Methods: We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19.

Findings: In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949-0·959), with a sensitivity of 0·923 (95% CI 0·914-0·932), specificity of 0·851 (0·842-0·860), a positive predictive value of 0·790 (0·777-0·803), and a negative predictive value of 0·948 (0·941-0·954). AI took a median of 0·55 min (IQR: 0·43-0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67-25·71) to draft a report and 23·06 min (15·67-39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947-1·000) and a specificity of 0·875 (95 %CI 0·833-0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718-0·940).

Interpretation: A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19.

Funding: Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

Figure 1

Development and validation of a deep learning algorithm to provide rapid triage in fever clinics and to automatically analyse lung opacities on the basis of chest CT scans (A) Overview of the development and validation of the algorithm. (B) Evaluation of triage efficiency; black lines show the standard workflow in Chinese fever clinics; after a patient's CT examination is completed, a first reader drafts a radiology report in a first-in-first-out order and then a second radiologist revises and approves the first reader's report before sending it to a fever clinician; after receiving the radiological report the fever clinician decides whether the patient qualifies as a suspected case and should receive RT-PCR testing; we proposed that through directly notifying either the second radiologist (ie, scan-to-second-reader triage; red line) or the fever clinician (scan-to-fever-clinician triage; green line) of suspected cases triaged by AI, the workflow in fever clinics could be expedited. AI=artificial intelligence.

Figure 2

Data collection (A) Dataset for algorithm development and internal validation. (B) Dataset for external validation.

Figure 3

AI triage accuracy for the internal validation set, external validation set overall, and three individual hospital datasets The black points indicate sensitivity and specificity thresholds used. AUC=area under the receiver operating curve.