The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications
This study introduces an Artificial Intelligence framework based on the Deep Learning model Bidirectional Encoder Representations from Transformers framework trained on a dataset from 2000–2023. The AI tool categorizes articles into six classes: Contactology, Low Vision, Refractive Surgery, Pediatri...
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MDPI AG
2025-02-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/2/77 |
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| author | Luis F. F. M. Santos Miguel Ángel Sánchez-Tena Cristina Alvarez-Peregrina José-María Sánchez-González Clara Martinez-Perez |
| author_facet | Luis F. F. M. Santos Miguel Ángel Sánchez-Tena Cristina Alvarez-Peregrina José-María Sánchez-González Clara Martinez-Perez |
| author_sort | Luis F. F. M. Santos |
| collection | DOAJ |
| description | This study introduces an Artificial Intelligence framework based on the Deep Learning model Bidirectional Encoder Representations from Transformers framework trained on a dataset from 2000–2023. The AI tool categorizes articles into six classes: Contactology, Low Vision, Refractive Surgery, Pediatrics, Myopia, and Dry Eye, with supervised learning enhancing classification accuracy, achieving F1-Scores averaging 86.4%, AUC at 0.98, Precision at 87%, and Accuracy at 86.8% via one-shot training, while Epoch training showed 85.9% Accuracy and 92.8% Precision. Utilizing the Artificial Intelligence model outputs, the Autoregressive Integrated Moving Average model provides forecasts from all classes through 2030, predicting decreases in research interest for Contactology, Low Vision, and Refractive Surgery but increases for Myopia and Dry Eye due to rising prevalence and lifestyle changes. Stability is expected in pediatric research, highlighting its focus on early detection and intervention. This study demonstrates the effectiveness of AI in enhancing diagnostic precision and strategic planning in optometry, with potential implications for broader clinical applications and improved accessibility to eye care. |
| format | Article |
| id | doaj-art-c1274bf819fb44a2afcee9640531deef |
| institution | OA Journals |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-c1274bf819fb44a2afcee9640531deef2025-08-20T02:03:42ZengMDPI AGTechnologies2227-70802025-02-011327710.3390/technologies13020077The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting ApplicationsLuis F. F. M. Santos0Miguel Ángel Sánchez-Tena1Cristina Alvarez-Peregrina2José-María Sánchez-González3Clara Martinez-Perez4Aeronautical, Tourism and Aviation Department, School of Business, Engineering and Aeronautics, ISEC Lisboa, Instituto Superior de Educação e Ciências, Alameda das Linhas de Torres, 179, 1750-142 Lisbon, PortugalOptometry and Vision Department, Faculty of Optics and Optometry, Complutense University of Madrid, 28040 Madrid, SpainOptometry and Vision Department, Faculty of Optics and Optometry, Complutense University of Madrid, 28040 Madrid, SpainVision Sciences Research Group (CIVIUS), Department of Physics of Condensed Matter, Optics Area, Pharmacy School, University of Seville, 41004 Seville, SpainSchool of Management, Engineering and Aeronautics, ISEC Lisboa, Instituto Superior de Educação e Ciências, Alameda das Linhas de Torres, 179, 1750-142 Lisbon, PortugalThis study introduces an Artificial Intelligence framework based on the Deep Learning model Bidirectional Encoder Representations from Transformers framework trained on a dataset from 2000–2023. The AI tool categorizes articles into six classes: Contactology, Low Vision, Refractive Surgery, Pediatrics, Myopia, and Dry Eye, with supervised learning enhancing classification accuracy, achieving F1-Scores averaging 86.4%, AUC at 0.98, Precision at 87%, and Accuracy at 86.8% via one-shot training, while Epoch training showed 85.9% Accuracy and 92.8% Precision. Utilizing the Artificial Intelligence model outputs, the Autoregressive Integrated Moving Average model provides forecasts from all classes through 2030, predicting decreases in research interest for Contactology, Low Vision, and Refractive Surgery but increases for Myopia and Dry Eye due to rising prevalence and lifestyle changes. Stability is expected in pediatric research, highlighting its focus on early detection and intervention. This study demonstrates the effectiveness of AI in enhancing diagnostic precision and strategic planning in optometry, with potential implications for broader clinical applications and improved accessibility to eye care.https://www.mdpi.com/2227-7080/13/2/77optometrydeep learningdata sciencepredictive modelingAI assisted diagnosticknowledge engineering |
| spellingShingle | Luis F. F. M. Santos Miguel Ángel Sánchez-Tena Cristina Alvarez-Peregrina José-María Sánchez-González Clara Martinez-Perez The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications Technologies optometry deep learning data science predictive modeling AI assisted diagnostic knowledge engineering |
| title | The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications |
| title_full | The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications |
| title_fullStr | The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications |
| title_full_unstemmed | The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications |
| title_short | The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications |
| title_sort | role of artificial intelligence in optometric diagnostics and research deep learning and time series forecasting applications |
| topic | optometry deep learning data science predictive modeling AI assisted diagnostic knowledge engineering |
| url | https://www.mdpi.com/2227-7080/13/2/77 |
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