Joacy Pedro Franco David, Alexandre Antonio Marques Rosa, Rafael Scherer, Cláudio Eduardo Corrêa Teixeira, Douglas Costa
{"title":"基于人工智能的标准自动视界判读和报告模型。","authors":"Joacy Pedro Franco David, Alexandre Antonio Marques Rosa, Rafael Scherer, Cláudio Eduardo Corrêa Teixeira, Douglas Costa","doi":"10.5935/0004-2749.2024-0270","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Standard automated perimetry has been the standard method for measuring visual field changes for several years. It can measure an individual's ability to detect a light stimulus from a uniformly illuminated background. In the management of glaucoma, the primary objective of perimetry is the identification and quantification of visual field abnormalities. It also serves as a longitudinal evaluation for the detection of disease progression. The development of artificial intelligence--based models capable of interpreting tests could combine technological development with improved access to healthcare.</p><p><strong>Methods: </strong>In this observational, cross-sectional, descriptive study, we used an artificial intelligence-based model [Inception V3] to interpret gray-scale crops from standard automated perimetry that were performed in an ophthalmology clinic in the Brazilian Amazon rainforest between January 2018 and December 2022.</p><p><strong>Results: </strong>The study included 1,519 standard automated perimetry test results that were performed using Humphrey HFA-II-i-750 (Zeiss Meditech). The Subsequently, 70%, 10%, and 20% of the dataset were used for training, validation, and testing, respectively. The model achieved 80% (68.23%-88.9%) sensitivity and 94.64% (88.8%-98%) specificity for detecting altered perimetry results. Furthermore, the area under the receiver operating characteristic curve was 0.93.</p><p><strong>Conclusions: </strong>The integration of artificial intelligence in the diagnosis, screening, and monitoring of pathologies represents a paradigm shift in ophthalmology, enabling significant improvements in safety, efficiency, availability, and accessibility of treatment.</p>","PeriodicalId":8397,"journal":{"name":"Arquivos brasileiros de oftalmologia","volume":"88 5","pages":"e20240270"},"PeriodicalIF":1.2000,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence-based model for the interpretation and reporting of standard automated perimetry.\",\"authors\":\"Joacy Pedro Franco David, Alexandre Antonio Marques Rosa, Rafael Scherer, Cláudio Eduardo Corrêa Teixeira, Douglas Costa\",\"doi\":\"10.5935/0004-2749.2024-0270\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Standard automated perimetry has been the standard method for measuring visual field changes for several years. It can measure an individual's ability to detect a light stimulus from a uniformly illuminated background. In the management of glaucoma, the primary objective of perimetry is the identification and quantification of visual field abnormalities. It also serves as a longitudinal evaluation for the detection of disease progression. The development of artificial intelligence--based models capable of interpreting tests could combine technological development with improved access to healthcare.</p><p><strong>Methods: </strong>In this observational, cross-sectional, descriptive study, we used an artificial intelligence-based model [Inception V3] to interpret gray-scale crops from standard automated perimetry that were performed in an ophthalmology clinic in the Brazilian Amazon rainforest between January 2018 and December 2022.</p><p><strong>Results: </strong>The study included 1,519 standard automated perimetry test results that were performed using Humphrey HFA-II-i-750 (Zeiss Meditech). The Subsequently, 70%, 10%, and 20% of the dataset were used for training, validation, and testing, respectively. The model achieved 80% (68.23%-88.9%) sensitivity and 94.64% (88.8%-98%) specificity for detecting altered perimetry results. Furthermore, the area under the receiver operating characteristic curve was 0.93.</p><p><strong>Conclusions: </strong>The integration of artificial intelligence in the diagnosis, screening, and monitoring of pathologies represents a paradigm shift in ophthalmology, enabling significant improvements in safety, efficiency, availability, and accessibility of treatment.</p>\",\"PeriodicalId\":8397,\"journal\":{\"name\":\"Arquivos brasileiros de oftalmologia\",\"volume\":\"88 5\",\"pages\":\"e20240270\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Arquivos brasileiros de oftalmologia\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5935/0004-2749.2024-0270\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arquivos brasileiros de oftalmologia","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5935/0004-2749.2024-0270","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Artificial intelligence-based model for the interpretation and reporting of standard automated perimetry.
Purpose: Standard automated perimetry has been the standard method for measuring visual field changes for several years. It can measure an individual's ability to detect a light stimulus from a uniformly illuminated background. In the management of glaucoma, the primary objective of perimetry is the identification and quantification of visual field abnormalities. It also serves as a longitudinal evaluation for the detection of disease progression. The development of artificial intelligence--based models capable of interpreting tests could combine technological development with improved access to healthcare.
Methods: In this observational, cross-sectional, descriptive study, we used an artificial intelligence-based model [Inception V3] to interpret gray-scale crops from standard automated perimetry that were performed in an ophthalmology clinic in the Brazilian Amazon rainforest between January 2018 and December 2022.
Results: The study included 1,519 standard automated perimetry test results that were performed using Humphrey HFA-II-i-750 (Zeiss Meditech). The Subsequently, 70%, 10%, and 20% of the dataset were used for training, validation, and testing, respectively. The model achieved 80% (68.23%-88.9%) sensitivity and 94.64% (88.8%-98%) specificity for detecting altered perimetry results. Furthermore, the area under the receiver operating characteristic curve was 0.93.
Conclusions: The integration of artificial intelligence in the diagnosis, screening, and monitoring of pathologies represents a paradigm shift in ophthalmology, enabling significant improvements in safety, efficiency, availability, and accessibility of treatment.
期刊介绍:
The ABO-ARQUIVOS BRASILEIROS DE OFTALMOLOGIA (ABO, ISSN 0004-2749 - print and ISSN 1678-2925 - (ABO, ISSN 0004-2749 - print and ISSN 1678-2925 - electronic version), the official bimonthly publication of the Brazilian Council of Ophthalmology (CBO), aims to disseminate scientific studies in Ophthalmology, Visual Science and Health public, by promoting research, improvement and updating of professionals related to the field.