Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging

Abstract Background artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of inflammatory cells for cellular phenotyping diagnosis of nasal polyps and to explore the clinical significance of different phenotypes of nasal polyps on the WSI. Methods a total of 453 patients were enrolled in our study. For the development of AICEP 2.0, 179 patients (WSIs) were obtained from the Third Affiliated Hospital of Sun Yat-Sen University (3HSYSU) from January 2008 to December 2018. A total of 24,625 patches were automatically extracted from the regions of interest under a 400× HPF by Openslide and the number of inflammatory cells in these patches was counted by two pathologists. For the application of AICEP 2.0 in a prospective cohort, 158 patients aged 14–70 years old with chronic rhinosinusitis with nasal polyps (CRSwNP) who had undergone endoscopic sinus surgery at 3HSYSU from June 2020 to December 2020 were included for preoperative demographic characteristics. For the application of AICEP 2.0 in a retrospective cohort, 116 patients with CRSwNP who had undergone endoscopic sinus surgery from May 2016 to June 2017 were enrolled for the recurrence rate. The proportion of inflammatory cells of these patients on WSI was calculated by our AICEP 2.0. Findings for AICEP 2.0, the mean absolute errors of the ratios of eosinophils, lymphocytes, neutrophils, and plasma cells were 1.64%, 2.13%, 1.06%, and 1.22%, respectively. The four phenotypes of nasal polyps were significantly different in clinical characteristics (including asthma, itching, sneezing, total IgE, peripheral eosinophils%, tissue eosinophils%, tissue neutrophils%, tissue lymphocytes%, tissue plasma cells%, and recurrence rate; P