Validation of AI-based software for objectification of conjunctival provocation test
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 conjunct...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-08-01
|
| Series: | Journal of Allergy and Clinical Immunology: Global |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772829323000462 |
| _version_ | 1827868395589074944 |
|---|---|
| author | Yury Yarin, PhD Alexandra Kalaitzidou, BSc Kira Bodrova, BSc Ralph Mösges, MD, PhD Yannis Kalaidzidis, PhD |
| author_facet | Yury Yarin, PhD Alexandra Kalaitzidou, BSc Kira Bodrova, BSc Ralph Mösges, MD, PhD Yannis Kalaidzidis, PhD |
| author_sort | Yury Yarin, PhD |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T15:31:05Z |
| format | Article |
| id | doaj.art-ffd3c27c7b6d46e9bf63c3f65113d7ed |
| institution | Directory Open Access Journal |
| issn | 2772-8293 |
| language | English |
| last_indexed | 2024-03-12T15:31:05Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Allergy and Clinical Immunology: Global |
| spelling | doaj.art-ffd3c27c7b6d46e9bf63c3f65113d7ed2023-08-10T04:35:10ZengElsevierJournal of Allergy and Clinical Immunology: Global2772-82932023-08-0123100121Validation of AI-based software for objectification of conjunctival provocation testYury Yarin, PhD0Alexandra Kalaitzidou, BSc1Kira Bodrova, BSc2Ralph Mösges, MD, PhD3Yannis Kalaidzidis, PhD4Practice for ENT und Allergology, Dresden, Germany; Corresponding author: Yury Yarin, PhD, Practice for ENT and Allergology, Dresden Overbeckstrasse 33, Dresden 01399, Germany.Practice for ENT und Allergology, Dresden, GermanyPractice for ENT und Allergology, Dresden, GermanyClinCompetence Cologne GmbH, Cologne, GermanyMax Planck Institute for Molecular Cell Biology and Genetics, Dresden, GermanyBackground: 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.http://www.sciencedirect.com/science/article/pii/S2772829323000462Allergyconjunctival provocation testdeep learningartificial intelligence (AI) |
| spellingShingle | Yury Yarin, PhD Alexandra Kalaitzidou, BSc Kira Bodrova, BSc Ralph Mösges, MD, PhD Yannis Kalaidzidis, PhD Validation of AI-based software for objectification of conjunctival provocation test Journal of Allergy and Clinical Immunology: Global Allergy conjunctival provocation test deep learning artificial intelligence (AI) |
| title | Validation of AI-based software for objectification of conjunctival provocation test |
| title_full | Validation of AI-based software for objectification of conjunctival provocation test |
| title_fullStr | Validation of AI-based software for objectification of conjunctival provocation test |
| title_full_unstemmed | Validation of AI-based software for objectification of conjunctival provocation test |
| title_short | Validation of AI-based software for objectification of conjunctival provocation test |
| title_sort | validation of ai based software for objectification of conjunctival provocation test |
| topic | Allergy conjunctival provocation test deep learning artificial intelligence (AI) |
| url | http://www.sciencedirect.com/science/article/pii/S2772829323000462 |
| work_keys_str_mv | AT yuryyarinphd validationofaibasedsoftwareforobjectificationofconjunctivalprovocationtest AT alexandrakalaitzidoubsc validationofaibasedsoftwareforobjectificationofconjunctivalprovocationtest AT kirabodrovabsc validationofaibasedsoftwareforobjectificationofconjunctivalprovocationtest AT ralphmosgesmdphd validationofaibasedsoftwareforobjectificationofconjunctivalprovocationtest AT yanniskalaidzidisphd validationofaibasedsoftwareforobjectificationofconjunctivalprovocationtest |