Deep Learning Model for Static Ocular Torsion Detection Using Synthetically Generated Fundus Images
Purpose: The objective of the study is to develop deep learning models using synthetic fundus images to assess the direction (intorsion versus extorsion) and amount (physiologic versus pathologic) of static ocular torsion. Static ocular torsion assessment is an important clinical tool for classifying vertical ocular misalignment; however, current methods are time-intensive with steep learning curves for frontline providers.
Methods: We used a dataset (n = 276) of right eye fundus images. The disc-foveal angle was calculated using ImageJ to generate synthetic images via image rotation. Using synthetic datasets (n = 12,740 images per model) and transfer learning (the reuse of a pretrained deep learning model on a new task), we developed a binary classifier (intorsion versus extorsion) and a multiclass classifier (physiologic versus pathologic intorsion and extorsion). Model performance was evaluated on unseen synthetic and nonsynthetic data.
Results: On the synthetic dataset, the binary classifier had an accuracy and area under the receiver operating characteristic curve (AUROC) of 0.92 and 0.98, respectively, whereas the multiclass classifier had an accuracy and AUROC of 0.77 and 0.94, respectively. The binary classifier generalized well on the nonsynthetic data (accuracy = 0.94; AUROC = 1.00).
Conclusions: The direction of static ocular torsion can be detected from synthetic fundus images using deep learning methods, which is key to differentiate between vestibular misalignment (skew deviation) and ocular muscle misalignment (superior oblique palsies).
Translational Relevance: Given the robust performance of our models on real fundus images, similar strategies can be adopted for deep learning research in rare neuro-ophthalmologic diseases with limited datasets.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Translational vision science & technology - 12(2023), 1 vom: 03. Jan., Seite 17 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Chen [VerfasserIn] |
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| Anmerkungen: |
Date Completed 13.01.2023 Date Revised 28.02.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1167/tvst.12.1.17 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Deep Learning Model for Static Ocular Torsion Detection Using Synthetically Generated Fundus Images |
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| 500 | |a Date Revised 28.02.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Purpose: The objective of the study is to develop deep learning models using synthetic fundus images to assess the direction (intorsion versus extorsion) and amount (physiologic versus pathologic) of static ocular torsion. Static ocular torsion assessment is an important clinical tool for classifying vertical ocular misalignment; however, current methods are time-intensive with steep learning curves for frontline providers | ||
| 520 | |a Methods: We used a dataset (n = 276) of right eye fundus images. The disc-foveal angle was calculated using ImageJ to generate synthetic images via image rotation. Using synthetic datasets (n = 12,740 images per model) and transfer learning (the reuse of a pretrained deep learning model on a new task), we developed a binary classifier (intorsion versus extorsion) and a multiclass classifier (physiologic versus pathologic intorsion and extorsion). Model performance was evaluated on unseen synthetic and nonsynthetic data | ||
| 520 | |a Results: On the synthetic dataset, the binary classifier had an accuracy and area under the receiver operating characteristic curve (AUROC) of 0.92 and 0.98, respectively, whereas the multiclass classifier had an accuracy and AUROC of 0.77 and 0.94, respectively. The binary classifier generalized well on the nonsynthetic data (accuracy = 0.94; AUROC = 1.00) | ||
| 520 | |a Conclusions: The direction of static ocular torsion can be detected from synthetic fundus images using deep learning methods, which is key to differentiate between vestibular misalignment (skew deviation) and ocular muscle misalignment (superior oblique palsies) | ||
| 520 | |a Translational Relevance: Given the robust performance of our models on real fundus images, similar strategies can be adopted for deep learning research in rare neuro-ophthalmologic diseases with limited datasets | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Bai, Yunong |e verfasserin |4 aut | |
| 700 | 1 | |a Tsang, Ashley |e verfasserin |4 aut | |
| 700 | 1 | |a Bian, Yuhan |e verfasserin |4 aut | |
| 700 | 1 | |a Gou, Yifan |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Yan X |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Matthew |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Tony Y |e verfasserin |4 aut | |
| 700 | 1 | |a Desman, Jacob M |e verfasserin |4 aut | |
| 700 | 1 | |a Taylor, Casey Overby |e verfasserin |4 aut | |
| 700 | 1 | |a Greenstein, Joseph L |e verfasserin |4 aut | |
| 700 | 1 | |a Otero-Millan, Jorge |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tin Yan Alvin |e verfasserin |4 aut | |
| 700 | 1 | |a Kheradmand, Amir |e verfasserin |4 aut | |
| 700 | 1 | |a Zee, David S |e verfasserin |4 aut | |
| 700 | 1 | |a Green, Kemar E |e verfasserin |4 aut | |
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