Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China

The low sensitivity and false-negative results of nucleic acid testing greatly affect its performance in diagnosing and discharging patients with coronavirus disease (COVID-19). Chest computed tomography (CT)-based evaluation of pneumonia may indicate a need for isolation. Therefore, this radiologic modality plays an important role in managing patients with suspected COVID-19. Meanwhile, deep learning (DL) technology has been successful in detecting various imaging features of chest CT. This study applied a novel DL technique to standardize the discharge criteria of COVID-19 patients with consecutive negative respiratory pathogen nucleic acid test results at a "square cabin" hospital.

DL was used to evaluate the chest CT scans of 270 hospitalized COVID-19 patients who had two consecutive negative nucleic acid tests (sampling interval >1 day). The CT scans evaluated were obtained after the patients' second negative test result. The standard criterion determined by DL for patient discharge was a total volume ratio of lesion to lung <50%.

The mean number of days between hospitalization and DL was 14.3 (± 2.4). The average intersection over union was 0.7894. Two hundred and thirteen (78.9%) patients exhibited pneumonia, of whom 54.0% (115/213) had mild interstitial fibrosis. Twenty-one, 33, and 4 cases exhibited vascular enlargement, pleural thickening, and mediastinal lymphadenopathy, respectively. Of the latter, 18.8% (40/213) had a total volume ratio of lesions to lung ≥50% according to our severity scale and were monitored continuously in the hospital. Three cases had a positive follow-up nucleic acid test during hospitalization. None of the 230 discharged cases later tested positive or exhibited pneumonia progression.

The novel DL enables the accurate management of hospitalized patients with COVID-19 and can help avoid cluster transmission or exacerbation in patients with false-negative acid test.