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?:abstract
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PURPOSE: 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 PATIENTS AND METHODS: 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% RESULTS: 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 CONCLUSION: 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
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