The aim of the present study is to investigate the value of air bronchogram sign on computed tomography (CT) image in the differential diagnosis of solitary pulmonary consolidation lesions (SPLs).A total of 105 patients (including 39 cases of lung cancer, 43 cases of tuberculosis, and 23 cases of pneumonia) with SPLs were evaluated for the CT features of air bronchogram sign in this retrospective study. The shape and lumen of the bronchi with air bronchogram sign, the length of the involved bronchus with air bronchogram sign, the length of lesion on the same plane and direction, and the ratio between the length of the involved bronchus and that of the lesion were evaluated.In total, there were 172 segmental and subsegmental bronchi involved. There were 62 segmental and subsegmental bronchi involved among 39 lung cancer cases, 77 segmental and subsegmental bronchi involved among 43 tuberculosis cases, and 33 segmental and subsegmental bronchi involved among 23 pneumonia cases. The shape of the bronchi with air bronchogram sign was significantly different among lung cancer, tuberculosis, and pneumonia (P < .05). The lumen of the bronchi with air bronchogram sign was also significantly different among the 3 SPLs (P < .05). The length of the involved bronchus with air bronchogram sign and the ratio between the length of the involved bronchus and that of the lesion were significantly different between lung cancer and tuberculosis (P < .05), or between lung cancer and pneumonia (P < .05), but not between tuberculosis and pneumonia (P > .05). No significant difference was found in the length of lesion among the 3 SPLs (P > .05).The shape and lumen of the bronchi with air bronchogram sign can be used to distinguish lung cancer, tuberculosis, and pneumonia. The length of the involved bronchus with air bronchogram sign and the ratio between the length of the involved bronchus and that of the lesion can be used to distinguish lung cancer from tuberculosis and pneumonia.

The development of computer technology is becoming more and more mature. The application of artificial intelligence to the medical field has made important contributions to medical diagnosis and auxiliary detection. Using the powerful computing power of computers to replace humans in the automatic diagnosis of complex diseases has been enthusiastic by the majority of scientific researchers. In the current medical imaging diagnosis system, the location of the lesion is mainly found by observing the two‐dimensional medical image sequence, and the signs of biological information cannot be accurately displayed. Through medical image analysis technology, two‐dimensional images will be divided into three‐dimensional models after image segmentation, image recognition, and three‐dimensional imaging. In this way, it seems that the doctor can “hold the image volume data,” which can greatly improve the scientific and accurate diagnosis. Sex—this article applies artificial intelligence to medical imaging, combined with embedded technology, RFID technology and signal processing technology, and applies the new coronavirus pneumonia image to the artificial intelligence environment after processing, assisting doctors in diagnosis of the disease, and providing relevant information about patients record and manage the diagnosis and diagnosis, save and accumulate the experience and knowledge of famous doctors through the expert system, and then perform corresponding operations and analysis. Through the medical image intelligent analysis system, the safety risk of medical imaging artificial intelligence diagnosis is reduced from 81% to 11%, which greatly reduces the hidden safety hazards for doctors and patients, reduces the workload of doctors, and also reduces the cost of medical care by 79% It is reduced to 20%, which reduces the waiting time of patients and achieves the purpose of improving the accuracy of diagnosis.