Comparison of machine learning approaches for structure–function modeling in glaucoma

Wong, Damon Wing Kee; Chua, Jacqueline; Bujor, Inna; Chong, Rachel S.; Nongpiur, Monisha E.; Vithana, Eranga N.; Husain, Rahat; Aung, Tin; Popa-Cherecheanu, Alina; Schmetterer, Leopold

doi:10.1111/nyas.14844

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…Wong et al 41 compared several machine learning approaches for the estimation of MD from RNFL thickness. They obtain MAEdecr% rates up to 26% using gradient-boosted trees in an external test set, which is half of the MAEdecr% reported in the current study (54%).…”

Section: Discussionmentioning

confidence: 99%

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning

Hemelings

Elen

Barbosa‐Breda

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose Standard automated perimetry is the gold standard to monitor visual field (VF) loss in glaucoma management, but it is prone to intrasubject variability. We trained and validated a customized deep learning (DL) regression model with Xception backbone that estimates pointwise and overall VF sensitivity from unsegmented optical coherence tomography (OCT) scans. Methods DL regression models have been trained with four imaging modalities (circumpapillary OCT at 3.5 mm, 4.1 mm, and 4.7 mm diameter) and scanning laser ophthalmoscopy en face images to estimate mean deviation (MD) and 52 threshold values. This retrospective study used data from patients who underwent a complete glaucoma examination, including a reliable Humphrey Field Analyzer (HFA) 24-2 SITA Standard (SS) VF exam and a SPECTRALIS OCT. Results For MD estimation, weighted prediction averaging of all four individuals yielded a mean absolute error (MAE) of 2.89 dB (2.50–3.30) on 186 test images, reducing the baseline by 54% (MAEdecr%). For 52 VF threshold values’ estimation, the weighted ensemble model resulted in an MAE of 4.82 dB (4.45–5.22), representing an MAEdecr% of 38% from baseline when predicting the pointwise mean value. DL managed to explain 75% and 58% of the variance ( R 2 ) in MD and pointwise sensitivity estimation, respectively. Conclusions Deep learning can estimate global and pointwise VF sensitivities that fall almost entirely within the 90% test–retest confidence intervals of the 24-2 SS test. Translational Relevance Fast and consistent VF prediction from unsegmented OCT scans could become a solution for visual function estimation in patients unable to perform reliable VF exams.

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Section: Discussionmentioning

confidence: 99%

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning

Hemelings

Elen

Barbosa‐Breda

et al. 2022

Trans. Vis. Sci. Tech.

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“…Wong et al reported several machine learning models that used the RNFL thickness measurements to estimate the mean deviation (MD) and total deviation (TD) values of the 24-2 map [26]. In their comparative analysis, all models outperformed their proposed baseline.…”

Section: Visual Field Estimation With Artificial Intelligencementioning

confidence: 99%

Artificial intelligence for glaucoma: state of the art and future perspectives

Correia Barão,

Hemelings,

Abegão Pinto

et al. 2023

Current Opinion in Ophthalmology

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Purpose of review To address the current role of artificial intelligence (AI) in the field of glaucoma. Recent findings Current deep learning (DL) models concerning glaucoma diagnosis have shown consistently improving diagnostic capabilities, primarily based on color fundus photography and optical coherence tomography, but also with multimodal strategies. Recent models have also suggested that AI may be helpful in detecting and estimating visual field progression from different input data. Moreover, with the emergence of newer DL architectures and synthetic data, challenges such as model generalizability and explainability have begun to be tackled. Summary : While some challenges remain before AI is routinely employed in clinical practice, new research has expanded the range in which it can be used in the context of glaucoma management and underlined the relevance of this research avenue.

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Detecting Glaucoma Worsening Using Optical Coherence Tomography Derived Visual Field Estimates

Pham,

Bradley,

Hou

et al. 2024

Preprint

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Objective: Multiple studies have attempted to generate visual field (VF) mean deviation (MD) estimates using cross-sectional optical coherence tomography (OCT) data. However, whether such models offer any value in detecting longitudinal VF progression is unclear. We address this by developing a machine learning (ML) model to convert OCT data to MD and assessing its ability to detect longitudinal worsening. Design: Retrospective, longitudinal study Participants: A model dataset of 70,575 paired OCT/VFs to train an ML model converting OCT to VF-MD. A separate progression dataset of 4,044 eyes with ≥ 5 paired OCT/VFs to assess the ability of OCT-derived MD to detect worsening. Progression dataset eyes had two additional unpaired VFs (≥ 7 total) to establish a 'ground truth' rate of progression defined by MD slope. Methods: We trained an ML model using paired VF/OCT data to estimate MD measurements for each OCT scan (OCT-MD). We used this ML model to generate longitudinal OCT-MD estimates for progression dataset eyes. We calculated MD slopes after substituting/supplementing VF-MD with OCT-MD and measured the ability to detect progression. We labeled true progressors using a ground truth MD slope <0.5 dB/year calculated from ≥ 7 VF-MD measurements. We compared the area under the curve (AUC) of MD slopes calculated using both VF-MD (with <7 measurements) and OCT-MD. Because we found OCT-MD substitution had a statistically inferior AUC to VF-MD, we simulated the effect of reducing OCT-MD mean absolute error (MAE) on the ability to detect worsening. Main Outcome Measures: AUC Results: OCT-MD estimates had an MAE of 1.62 dB. AUC of MD slopes with partial OCT-MD substitution was significantly worse than the VF-MD slope. Supplementing VF-MD with OCT-MD also did not improve AUC, regardless of MAE. OCT-MD estimates needed an MAE ≤ 1.00 dB before AUC was statistically similar to VF-MD alone. Conclusion: ML models converting OCT data to VF-MD with error levels lower than published in prior work (MAE: 1.62 dB) were inferior to VF-MD data for detecting trend-based VF progression. Models converting OCT data to VF-MD must achieve better prediction errors (MAE ≤ 1 dB) to be clinically valuable at detecting VF worsening.

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Comparison of machine learning approaches for structure–function modeling in glaucoma

Cited by 4 publications

References 43 publications

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning

Artificial intelligence for glaucoma: state of the art and future perspectives

Detecting Glaucoma Worsening Using Optical Coherence Tomography Derived Visual Field Estimates

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