Objective To evaluate the diagnostic efficacy of artificial vision,radiomics(RAD)and deep learning(DL)in distinguishing atypical usual interstitial pneumonia(UIP)from nonspecific interstitial pneumonia(NSIP).Methods A retrospective analysis was conducted on 300 patients diagnosed with interstitial pneumonia(IP).A total of 56 cases of atypical UIP and 57 cases of NSIP were included.All patients underwent CT examination before biopsy,and were randomly divided into training and test groups.DL and RAD artificial intelligence models were developed and trained.The consistency of artificial vision assessment was performed via Kappa test and receiver operating characteristic(ROC)curve analysis was performed via logistic regression.Then,the differences in the area under the curve(AUC)of different models were compared via DeLong analysis.The diagnostic efficiency of the model was evaluated via AUC and accuracy(ACC)as the main indicators and compared with the artificial vision evaluation.Results Two radiologists showed significant consistency in the CT diagnosis and interpretation of atypical UIP and NSIP(Kappa=0.852,P＜0.01).On high-resolution computed tomography(HRCT),the atypical UIP patients mainly showed basal bronchiectasis,honeycombing,and reticular opacities,while,the NSIP patients showed basal reticular opacities,ground glass opacity(GGO)and subpleural avoidance sign.The probability of GGO exceeding the reticular opacities of the NSIP patients was significantly higher than that of the atypical UIP patients(P＜0.05).The DL model showed significantly higher diagnostic efficiency than RAD and artificial vision in the differential diagnosis of atypical UIP and NSIP(AUC:0.94 vs 0.85 vs 0.65,P＜0.01).Conclusion CT and RAD have a certain significant diagnostic value for atypical UIP and NSIP.Atypical UIP and NSIP can be more accurately differentiated and diagnosed based on the DL model,thereby,the management of IP is further strengthened in clinical practice.