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Automatic detection of genetic diseases in pediatric age using pupillometry
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Iadanza, Ernesto, Goretti, Francesco, Sorelli, Michele, Melillo, Paolo, Pecchia, Leandro, Simonelli, Francesca and Gherardelli, Monica (2020) Automatic detection of genetic diseases in pediatric age using pupillometry. IEEE Access, 8 (1). pp. 34949-34961. doi:10.1109/ACCESS.2020.2973747 ISSN 2169-3536.
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WRAP-automatic-detection-genetic-diseases-pediatric-age-using-pupillometry-Pecchia-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1791Kb) | Preview |
Official URL: http://doi.org/10.1109/ACCESS.2020.2973747
Abstract
Inherited retinal diseases cause severe visual deficits in children. They are classified in outer and inner retina diseases, and often cause blindness in childhood. The diagnosis for this type of illness is challenging, given the wide range of clinical and genetic causes (with over 200 causative genes). It is routinely based on a complex pattern of clinical tests, including invasive ones, not always appropriate for infants or young children. A different approach is thus needed, that exploits Chromatic Pupillometry, a technique increasingly used to assess outer and inner retina functions. This paper presents a novel Clinical Decision Support System (CDSS), based on Machine Learning using Chromatic Pupillometry in order to support diagnosis of Inherited retinal diseases in pediatric subjects. An approach that combines hardware and software is proposed: a dedicated medical equipment (pupillometer) is used with a purposely designed custom machine learning decision support system. Two distinct Support Vector Machines (SVMs), one for each eye, classify the features extracted from the pupillometric data. The designed CDSS has been used for diagnosis of Retinitis Pigmentosa in pediatric subjects. The results, obtained by combining the two SVMs in an ensemble model, show satisfactory performance of the system, that achieved 0.846 accuracy, 0.937 sensitivity and 0.786 specificity. This is the first study that applies machine learning to pupillometric data in order to diagnose a genetic disease in pediatric age.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine R Medicine > RE Ophthalmology T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Artificial intelligence, Artificial intelligence -- Medical applications, Diagnosis -- Decision making -- Data processing, Clinical medicine -- Decision making -- Data processing, Decision support systems, Pupillometry | ||||||
| Journal or Publication Title: | IEEE Access | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2169-3536 | ||||||
| Official Date: | 13 February 2020 | ||||||
| Dates: |
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| Volume: | 8 | ||||||
| Number: | 1 | ||||||
| Page Range: | pp. 34949-34961 | ||||||
| DOI: | 10.1109/ACCESS.2020.2973747 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Copyright Holders: | authors | ||||||
| Date of first compliant deposit: | 2 March 2020 | ||||||
| Date of first compliant Open Access: | 3 March 2020 | ||||||
| RIOXX Funder/Project Grant: |
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