According to Pneumonia Diagnosis and Treatment Program for New Coronavirus Infection (trial fifth edition), "suspected cases with pneumonia imaging characteristics "has been included in the Hubei Province clinical diagnostic criteria, and CT imaging is one of the important bases for diagnosis and evaluation . However, due to the large number of patients, multiple focus in lung, rapid change progression, and the need for multiple re-examination in a short period of time, the accurate diagnosis and quantitative analysis of imaging doctors are facing great challenges. In recent years, the excellent performance of AI assisted diagnosis technology in medical field has received great attention, especially in the screening and diagnosis of pulmonary nodules, which is more sensitive than imaging physicians. AI as an auxiliary diagnostic technique can help imaging physicians improve their work efficiency and diagnostic accuracy [16–18]. CT image data of 200 patients with COVID-19 were collected in experiment, and the images were input into the AI assisted diagnostic software based on deep learning model for focus detection. The results showed that clinical common type patients CT showed unilateral or double lung multiple focus, a slice or wedge shaped GGO, the vessels and bronchi could be seen inside, often accompanied by thickening of interlobular septum, "paving stones" sign and bronchial inflatable sign, and lung consolidation could be seen inside some lesions. The CT of clinically severe and critically ill patients showed a wide range of lesion, GGO, solid-shadow and fiber-strip shadow, with "paving stone" sign, bronchial inflatable sign. After 3–5 days of clinical treatment, CT re-examination showed that, among the 200 patients, the total volume of focus, the volume of internal GGO and the volume of real variable region of 21 patients were decreased accordingly, indicating that the patients' condition had a certain degree of improvement. In 6 patients, the total volume of focus, the volume of internal GGO or the volume of real variable region were increased, indicating that the patients' condition had a certain degree of deterioration. In 3 cases, the total volume of focus, the volume of internal GGO or the volume of real variable region did not change significantly, and the change of the disease was not obvious. It can be seen that the deep learning model-based AI software focus detection method can be applied in the diagnosis of COVID-19 and can effectively evaluate the patient's condition.
Deep learning techniques can effectively accomplish the tasks of image detection, recognition and classification, so the introduction of deep learning techniques in the field of imaging may help radiologists to complete various tasks of detection and diagnosis [19, 20]. Pulmonary nodule detection using AI algorithm is an important part of AI medical field . Results show that the range of focus markers of the two detection methods is consistent, but the detection rate of AI software focus detection method based on deep learning model is significantly lower than that of manual detection method (P < 0.05), while the misdiagnosis rate and missed diagnosis rate are significantly higher than that of manual detection method (P < 0.05). It can be seen that the detection ability of AI software focus detection method based on deep learning model is not as good as that of manual detection method at present. This may be related to the current insufficient number of uAI-Discover-NCP learning cases at present, the deep learning algorithm needs to make a trade-off between sensitivity and specificity of the focus detection, which is still in the exploratory testing phase. In addition, the detection of focus has a certain relationship with their size and nature. Computer deep learning and depth algorithms can be extended with the expansion of data, with the improvement of AI assisted diagnostic performance, it is expected to improve the accuracy of focus detection, reduce the number of daily tasks that take time and effort, and liberate part of the workload of imaging doctors. Brown (2020) studies have shown that the progression of chest CT in patients during hospitalization is able to predict patients' response to treatment and help distinguish between critical and non-critical patients . Results of this experiment also suggest that chest CT can reflect the value of changes in pneumonia, but whether these changes affect the prognosis of patient needs further analysis
To sum up, the false detection rate and missed diagnosis rate of AI assisted diagnostic software based on deep learning model are higher than that of imaging physician's manual detection method, and the detection ability still needs to be improved. Overall, however, AI assisted diagnostic CT has a good ability to detect lesion in COVID-19 patients, and can effectively identify the focus, provide relevant data information such as the total volume of the focus, the internal GGO and the volume of solid change, and show the variation of lesion range and internal density, which not only provide objective imaging support for COVID-19 diagnosis and public health management, but also improve the efficiency of imaging physicians.