Background: Provocation tests are widely used in allergology to objectively reveal patients' sensitivity to specific allergens. The objective quantification of an allergic reaction is a crucial characteristic of these tests. Because of the absence of objective quantitative measurements, the conjunctival provocation test (CPT) is a less frequently used method despite its sensitivity and simplicity.
Objective: We developed a new artificial intelligence (AI)-based method, called AllergoEye, for quantitative evaluation of conjunctival allergic reactions and validated it in a clinical study.
Methods: AllergoEye was implemented as a 2-component system. The first component is based on an Android smartphone camera for screening and imaging the patient's eye, and the second is personal computer-based for image analysis and quantification. For the validation of AllergoEye, an open-label, prospective, monocentric study was carried out on 41 patients. Standardized CPT was performed with sequential titration of grass allergens in 4 dilutions, with the reaction evaluated by subjective/qualitative symptom scores and by quantitative AllergoEye scores.
Results: AllergoEye demonstrated high sensitivity (98%) and specificity (90%) as compared with human estimation of allergic reaction. Tuning cutoff thresholds allowed us to increase the specificity of AllergoEye to 97%, at which point the correlation between detected sensitivity to allergen and specific IgE carrier-polymer system class becomes obvious. Strikingly, such correlation was not found with sensitivity to allergen detected on the basis of subjective and qualitative symptom scores.
Conclusion: The clinical validation demonstrated that AllergoEye is a sensitive and efficient instrument for objective measurement of allergic reactions in CPT for clinical studies as well as for routine therapy control.
Keywords: Allergy; artificial intelligence (AI); conjunctival provocation test; deep learning.
© 2023 The Author(s).