Abstract
Purpose The purpose of this study is to evaluate the application efficiency of artificial intelligence (AI) image-assisted diagnosis system in chest CT examination of corona virus disease 2019 (COVID-19). Methods A total of 33 cases of COVID-19 patients who underwent chest CT in Hefei Binhu Hospital between January 2020 and March 2020 were retrospectively included. All patients were tested positive for novel coronavirus nucleic acid by fluorescent reverse transcription-polymerasechain reaction (RT-PCR). The pneumonia screening function of the AI image-assisted diagnosis system was employed for the 103 chest CT examinations of the 33 cases. The diagnosis of four senior radiologists were used as the standard for synchronous under blind state. The sensitivity, specificity, misdiagnosis rate, missed diagnosis rate and other evaluation indexes of the COVID-19 performed by the AI image-assisted diagnosis system were analyzed, and an dynamic evaluation on the CT reexamination was conducted. Results Out of the 103 chest CT examinations, there were 88 cases of true positive, 1 case of false positive, 12 cases of true negative and 2 cases of false negative. The sensitivity was 97.78% (88/90); the specificity was 92.31% (12/13); the positive predictive value was 98.88% (88/89); the negative predictive value was 85.71% (12/14); the accuracy was 97.09% (100/103); the Youden index was 90.09%; the positive likelihood ratio was 12.711 and the negative likelihood ratio was 0.024. There were 790 identified lesions in these CT examinations in total, of which 569 were true positive and 221 were false positive. There were also 64 missed diagnosis markers. The detection rate of all lesions was 89.89% and the rate of false positives was 27.97%. In the last CT scan, the lesion size were smaller and the percentage of lesions in total lung volume along with the mean density of lesions was lower than that of the first CT scan. Conclusion The AI image-assisted diagnosis system has certain clinical application value in the early diagnosis and follow-up evaluation of chest CT examination of COVID-19.
Rapid Implementation of COVID-19 AI Assisted Diagnosis System Based on Supercomputing Platform
