Glaucoma detection and staging from visual field images using machine learning techniques.
<h4>Purpose</h4>In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using on...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316919 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540242045829120 |
|---|---|
| author | Nahida Akter Jack Gordon Sherry Li Mikki Poon Stuart Perry John Fletcher Thomas Chan Andrew White Maitreyee Roy |
| author_facet | Nahida Akter Jack Gordon Sherry Li Mikki Poon Stuart Perry John Fletcher Thomas Chan Andrew White Maitreyee Roy |
| author_sort | Nahida Akter |
| collection | DOAJ |
| description | <h4>Purpose</h4>In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.<h4>Methods</h4>A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma. The two popular pre-trained DL models: ResNet18 and VGG16, were used to train the PD images using five-fold cross-validation (CV) and observed the performance using balanced, pre-augmented data (n = 476 images), imbalanced original data (n = 265) and feature extraction. The trained images were further investigated using the Grad-CAM visualization technique. Moreover, four ML models were trained from the global indices: mean deviation (MD), pattern standard deviation (PSD) and visual field index (VFI), using five-fold CV to compare the classification performance with the DL model's result.<h4>Results</h4>The DL model, ResNet18 trained from balanced, pre-augmented PD images, achieved high accuracy in classifying the groups with an overall F1-score: 96.8%, precision: 97.0%, recall: 96.9%, and specificity: 99.0%. The highest F1 score was 87.8% for ResNet18 with the original dataset and 88.7% for VGG16 with feature extraction. The DL models successfully localized the affected VF loss in PD plots. Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%.<h4>Conclusion</h4>The DL model trained from PD plots was promising in differentiating normal and glaucomatous groups and performed similarly to conventional global indices. Hence, the evidence-based DL model trained from PD images demonstrated that the DL model could stage glaucoma using only PD plots like Mills criteria. This automated DL model will assist clinicians in precision glaucoma detection and progression management during extensive glaucoma screening. |
| format | Article |
| id | doaj-art-41cba02ceb174f5681a2b5979ffc7638 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-41cba02ceb174f5681a2b5979ffc76382025-02-05T05:31:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031691910.1371/journal.pone.0316919Glaucoma detection and staging from visual field images using machine learning techniques.Nahida AkterJack GordonSherry LiMikki PoonStuart PerryJohn FletcherThomas ChanAndrew WhiteMaitreyee Roy<h4>Purpose</h4>In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.<h4>Methods</h4>A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma. The two popular pre-trained DL models: ResNet18 and VGG16, were used to train the PD images using five-fold cross-validation (CV) and observed the performance using balanced, pre-augmented data (n = 476 images), imbalanced original data (n = 265) and feature extraction. The trained images were further investigated using the Grad-CAM visualization technique. Moreover, four ML models were trained from the global indices: mean deviation (MD), pattern standard deviation (PSD) and visual field index (VFI), using five-fold CV to compare the classification performance with the DL model's result.<h4>Results</h4>The DL model, ResNet18 trained from balanced, pre-augmented PD images, achieved high accuracy in classifying the groups with an overall F1-score: 96.8%, precision: 97.0%, recall: 96.9%, and specificity: 99.0%. The highest F1 score was 87.8% for ResNet18 with the original dataset and 88.7% for VGG16 with feature extraction. The DL models successfully localized the affected VF loss in PD plots. Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%.<h4>Conclusion</h4>The DL model trained from PD plots was promising in differentiating normal and glaucomatous groups and performed similarly to conventional global indices. Hence, the evidence-based DL model trained from PD images demonstrated that the DL model could stage glaucoma using only PD plots like Mills criteria. This automated DL model will assist clinicians in precision glaucoma detection and progression management during extensive glaucoma screening.https://doi.org/10.1371/journal.pone.0316919 |
| spellingShingle | Nahida Akter Jack Gordon Sherry Li Mikki Poon Stuart Perry John Fletcher Thomas Chan Andrew White Maitreyee Roy Glaucoma detection and staging from visual field images using machine learning techniques. PLoS ONE |
| title | Glaucoma detection and staging from visual field images using machine learning techniques. |
| title_full | Glaucoma detection and staging from visual field images using machine learning techniques. |
| title_fullStr | Glaucoma detection and staging from visual field images using machine learning techniques. |
| title_full_unstemmed | Glaucoma detection and staging from visual field images using machine learning techniques. |
| title_short | Glaucoma detection and staging from visual field images using machine learning techniques. |
| title_sort | glaucoma detection and staging from visual field images using machine learning techniques |
| url | https://doi.org/10.1371/journal.pone.0316919 |
| work_keys_str_mv | AT nahidaakter glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT jackgordon glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT sherryli glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT mikkipoon glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT stuartperry glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT johnfletcher glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT thomaschan glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT andrewwhite glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques AT maitreyeeroy glaucomadetectionandstagingfromvisualfieldimagesusingmachinelearningtechniques |