Publicação
Artificial Intelligence in the diagnosis of dry eye
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Emamian, Mohammad Hassan | |
| dc.contributor.author | Aliyari, Roqayeh | |
| dc.contributor.author | Lança, Carla | |
| dc.contributor.author | Grzybowski, Andrzej | |
| dc.contributor.editor | Grzybowski, Andrzej | |
| dc.date.accessioned | 2025-12-17T10:16:13Z | |
| dc.date.available | 2025-12-17T10:16:13Z | |
| dc.date.issued | 2025-07 | |
| dc.description.abstract | Dry eye is a multifactorial disease, and its prevalence reaches 50% in some regions of the world. The disease is characterized by several signs and symptoms, and there are various diagnostic methods. However, diagnostic methods available have limitations, with some tests having poor reliability and reproducibility. So far, there is no gold standard test for diagnosing dry eye disease (DED), as there is no linear association between signs and symptoms, and often patients who have multiple signs of the disease report few symptoms or even have no symptoms. The above issues have led to difficulties in diagnosing DED. New imaging modalities, such as anterior segment optical coherence tomography (OCT), infrared meibography, in vivo confocal microscopy, tear interferometry, and non-invasive tear break-up time (TBUT), have emerged to allow objective measurements. However, the interpretation of results is based on subjective judgment. Artificial intelligence (AI) can help to solve these problems and contribute to decision-making by interpreting the results objectively. During the past years and especially after 2014, several research studies on the use of AI in the diagnosis, classification, and monitoring of DED have been published, and the results seem promising. Machine learning and deep learning methods have shown high sensitivity, specificity, and accuracy. Implementation of AI has significantly increased the speed of diagnosing DED and its causes; however, it has not yet been integrated into clinical practice. AI will play an important role in diagnosing and managing dry eye soon. AI can also be used for analysis of big data, which may predict estimates of DED prevalence and its risk factors. This will be a big step to identify individual risk factors for upscaling precision medicine in DED. | eng |
| dc.identifier.citation | Emamian MH, Aliyari R, Lança C, Grzybowski A. Artificial Intelligence in the diagnosis of dry eye. In: Grzybowski A, editor. Artificial Intelligence in ophthalmology. Cham: Springer; 2025. p. 333-48. | |
| dc.identifier.doi | 10.1007/978-3-031-83756-2_24 | |
| dc.identifier.isbn | 9783031837555 | |
| dc.identifier.isbn | 9783031837562 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/22336 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer Nature Switzerland | |
| dc.relation.hasversion | https://link.springer.com/chapter/10.1007/978-3-031-83756-2_24 | |
| dc.relation.ispartof | Artificial Intelligence in Ophthalmology | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Ophthalmology | |
| dc.subject | Dry eye disease | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Machine learning | |
| dc.subject | Diagnostic imaging modalities | |
| dc.title | Artificial Intelligence in the diagnosis of dry eye | eng |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 348 | |
| oaire.citation.startPage | 333 | |
| oaire.citation.title | Artificial Intelligence in ophthalmology | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Lança | |
| person.givenName | Carla | |
| person.identifier.ciencia-id | 601A-6412-BF2F | |
| person.identifier.orcid | 0000-0001-9918-787X | |
| relation.isAuthorOfPublication | 0320b455-ee19-4670-8bf2-10dce9de1bec | |
| relation.isAuthorOfPublication.latestForDiscovery | 0320b455-ee19-4670-8bf2-10dce9de1bec |