Abstract
Background
Common rhinologic diagnoses have similar presentations with a varying degree of overlap. Patterns may exist within clinical data that can be useful for early diagnosis and predicting outcomes.
Objective
To explore the feasibility of artificial intelligence to differentiate patterns in patient data in order to develop clinically-meaningful diagnostic groups.
Methods
A cross-sectional study of prospectively-acquired patient data at a tertiary rhinology clinic was performed. Data extracted included objective findings on nasal endoscopy, patient reported quality of life (PRQOL) instrument ratings, peripheral eosinophil fraction, and past medical history. Unsupervised non-hierarchical cluster analysis was performed to discover patterns in the data using 22 input variables.
Results
A total of 545 patients were analyzed after application of inclusion and exclusion criteria yielding 7 unique patient clusters, highly dependent on PRQOL scores and demographics. The clusters were clinically-relevant with distinct characteristics. Chronic rhinosinusitis without nasal polyposis (CRSsNP) was associated with two clusters having low frequencies of asthma and low eosinophil fractions. Chronic rhinosinusitis with nasal polyposis (CRSwNP) was associated with high frequency of asthma, mean (standard deviation [SD]) NOSE scores of 66 (19) and SNOT-22 scores of 41 (15), and high eosinophil fractions. AR was present in multiple clusters. RARS was associated with the youngest population with mean (SD) NOSE score of 54 (23) and SNOT-22 score of 41 (19).
Conclusion
Broader consideration of initially available clinical data may improve diagnostic efficiency for rhinologic conditions without ancillary studies, using computer-driven algorithms. PRQOL scores and demographic information appeared to be useful adjuncts, with associations to diagnoses in this pilot study.
Keywords
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