Advances in Corneal Diagnostics Using Machine Learning
This paper provides comprehensive insights into the cornea and its diseases, with a particular focus on keratoconus. This paper explores the cornea’s function in maintaining ocular health, detailing its anatomy, pathological conditions, and the latest developments in diagnostic techniques. Keratocon...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/12/1198 |
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| author | Noor T. Al-Sharify Salman Yussof Nebras H. Ghaeb Zainab T. Al-Sharify Husam Yahya Naser Sura M. Ahmed Ong Hang See Leong Yeng Weng |
| author_facet | Noor T. Al-Sharify Salman Yussof Nebras H. Ghaeb Zainab T. Al-Sharify Husam Yahya Naser Sura M. Ahmed Ong Hang See Leong Yeng Weng |
| author_sort | Noor T. Al-Sharify |
| collection | DOAJ |
| description | This paper provides comprehensive insights into the cornea and its diseases, with a particular focus on keratoconus. This paper explores the cornea’s function in maintaining ocular health, detailing its anatomy, pathological conditions, and the latest developments in diagnostic techniques. Keratoconus is discussed extensively, covering its subtypes, etiology, clinical manifestations, and the application of the Q-value for quantification. Several diagnostic techniques, such as corneal topography, are crucial points of discussion. This paper also examines the use of machine learning models, specifically Decision Tree and Nearest Neighbor Analysis, which enhance the accuracy of diagnosing based on topographical corneal parameters from corneal topography. These models provide valuable insights into disease progression and aid in clinical decision making. Integrating these technologies in medical research opens promising avenues for enhanced disease detection. Our findings demonstrate the effectiveness of Decision Tree and Nearest Neighbor Analysis in classifying and predicting conditions based on corneal parameters. The Decision Tree achieved classification accuracy of 62% for training and 65.7% for testing, while Nearest Neighbor Analysis yielded 65.4% for training and 62.6% for holdout samples. These models offer valuable insights into the progression and severity of keratoconus, aiding clinicians in treatment and management decisions. |
| format | Article |
| id | doaj-art-7d687430675c4e62836627c3bf763b16 |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-7d687430675c4e62836627c3bf763b162025-08-20T02:57:06ZengMDPI AGBioengineering2306-53542024-11-011112119810.3390/bioengineering11121198Advances in Corneal Diagnostics Using Machine LearningNoor T. Al-Sharify0Salman Yussof1Nebras H. Ghaeb2Zainab T. Al-Sharify3Husam Yahya Naser4Sura M. Ahmed5Ong Hang See6Leong Yeng Weng7Department of Electrical & Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaInstitute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, MalaysiaBiomedical Engineering Department, Al Khwarizmi Engineering College, University of Baghdad, Baghdad 10011, IraqDepartment of Pharmacy, Al Hikma University College, Baghdad 10052, IraqDepartment of Electrical & Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaDepartment of Electrical & Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaDepartment of Electrical & Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaDepartment of Electrical & Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaThis paper provides comprehensive insights into the cornea and its diseases, with a particular focus on keratoconus. This paper explores the cornea’s function in maintaining ocular health, detailing its anatomy, pathological conditions, and the latest developments in diagnostic techniques. Keratoconus is discussed extensively, covering its subtypes, etiology, clinical manifestations, and the application of the Q-value for quantification. Several diagnostic techniques, such as corneal topography, are crucial points of discussion. This paper also examines the use of machine learning models, specifically Decision Tree and Nearest Neighbor Analysis, which enhance the accuracy of diagnosing based on topographical corneal parameters from corneal topography. These models provide valuable insights into disease progression and aid in clinical decision making. Integrating these technologies in medical research opens promising avenues for enhanced disease detection. Our findings demonstrate the effectiveness of Decision Tree and Nearest Neighbor Analysis in classifying and predicting conditions based on corneal parameters. The Decision Tree achieved classification accuracy of 62% for training and 65.7% for testing, while Nearest Neighbor Analysis yielded 65.4% for training and 62.6% for holdout samples. These models offer valuable insights into the progression and severity of keratoconus, aiding clinicians in treatment and management decisions.https://www.mdpi.com/2306-5354/11/12/1198ophthalmologycorneal topographydiagnostic modelsvision healthcorneal asphericitydecision tree and nearest neighbor |
| spellingShingle | Noor T. Al-Sharify Salman Yussof Nebras H. Ghaeb Zainab T. Al-Sharify Husam Yahya Naser Sura M. Ahmed Ong Hang See Leong Yeng Weng Advances in Corneal Diagnostics Using Machine Learning Bioengineering ophthalmology corneal topography diagnostic models vision health corneal asphericity decision tree and nearest neighbor |
| title | Advances in Corneal Diagnostics Using Machine Learning |
| title_full | Advances in Corneal Diagnostics Using Machine Learning |
| title_fullStr | Advances in Corneal Diagnostics Using Machine Learning |
| title_full_unstemmed | Advances in Corneal Diagnostics Using Machine Learning |
| title_short | Advances in Corneal Diagnostics Using Machine Learning |
| title_sort | advances in corneal diagnostics using machine learning |
| topic | ophthalmology corneal topography diagnostic models vision health corneal asphericity decision tree and nearest neighbor |
| url | https://www.mdpi.com/2306-5354/11/12/1198 |
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