[Artificial intelligence-assisted prediction of olfactory disorders in patients with chronic rhinosinusitis].

Objective

To analyze the influencing factors and perform the prediction of olfactory disorders in patients with chronic rhinosinusitis（CRS） based on artificial intelligence.

Methods

The data of 75 patients with CRS who underwent nasal endoscopic surgery from October 2021 to February 2023 in the Department of Otorhinolaryngology Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University were analyzed retrospectively. There were 53 males and 22 females enrolled in the study, with a median age of 42.0 years old. The CRS intelligent microscope interpretation system was used to calculate the proportion of area glands and blood vessels occupy in the pathological sections of each patient, and the absolute value and proportion of eosinophils, lymphocytes, plasma cells and neutrophils. The patients were grouped according to the results of the Sniffin' Sticks smell test, and the clinical baseline data, differences in nasal mucosal histopathological characteristics, laboratory test indicators and sinus CT were compared between the groups. Determine the independent influencing factors of olfactory disorders and receiver operating characteristic curves（ROC） were used to evaluate the performance of the prediction model. Statistical analysis was performed using SPSS 25.0 software.

Results

Among the 75 CRS patients, 25 cases（33.3%） had normal olfaction and 50 cases（66.7%） had olfactory disorders. Multivariate Logistic regression analysis showed that tissue eosinophils percentage（OR=1.032, 95%CI 1.002-1.064, P=0.036）, Questionnaire of olfactory disorders-Negative statement（QOD-NS）（OR=1.079, 95%CI 1.004-1.160, P=0.040） and Anterior olfactory cleft score（AOCS）（OR=2.672, 95%CI 1.480-4.827, P=0.001） were independent risk factors for olfactory disorders in CRS patients. Further research found that the area under the ROC curve（AUC） of the combined prediction model established by the tissue eosinophil percentage, QOD-NS and AOCS was 0.836（95%CI 0.748-0.924, P<0.001）, which is better than the above single factor prediction model in predicting olfactory disorders in CRS.

Conclusion

Based on pathological artificial intelligence, tissue eosinophil percentage, QOD-NS and AOCS are independent risk factors for olfactory disorders in CRS patients, and the combination of the three factors has a good predictive effect on CRS olfactory disorders.