Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.
Corneal tomography
Feature selection
Keratoconus
Machine learning
Severity staging
Smart web
Journal
Eye and vision (London, England)
ISSN: 2326-0254
Titre abrégé: Eye Vis (Lond)
Pays: England
ID NLM: 101664982
Informations de publication
Date de publication:
08 Jul 2024
08 Jul 2024
Historique:
received:
15
12
2023
accepted:
17
06
2024
medline:
9
7
2024
pubmed:
9
7
2024
entrez:
8
7
2024
Statut:
epublish
Résumé
This study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements. A clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience. The performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality. The developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.
Sections du résumé
BACKGROUND
BACKGROUND
This study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements.
METHODS
METHODS
A clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience.
RESULTS
RESULTS
The performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality.
CONCLUSION
CONCLUSIONS
The developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.
Identifiants
pubmed: 38978067
doi: 10.1186/s40662-024-00394-1
pii: 10.1186/s40662-024-00394-1
doi:
Types de publication
Journal Article
Langues
eng
Pagination
28Informations de copyright
© 2024. The Author(s).
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