Current status of home monitoring technology for age-related macular degeneration

Busquets, Miguel; Sabbagh, Osama

doi:10.1097/icu.0000000000000756

Cited by 15 publications

(10 citation statements)

References 16 publications

(18 reference statements)

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“…Close monitoring is therefore essential for the detection of CNV reactivation, at which point treatment needs to be resumed. Whether quarterly monitoring by a vision care Cao et al,17 provider (as was performed in this study) is sufficient for the early detection of CNV reactivation, or additional measures (e.g., home monitoring (23)) would be of benefit, remains unanswered.…”

Section: Discussionmentioning

confidence: 98%

Aflibercept more effectively weans patients with neovascular age-related macular degeneration off therapy compared with bevacizumab

Sanchez¹,

Patel²,

Yang³

et al. 2023

Journal of Clinical Investigation

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Section: Discussionmentioning

confidence: 98%

Aflibercept more effectively weans patients with neovascular age-related macular degeneration off therapy compared with bevacizumab

Sanchez¹,

Patel²,

Yang³

et al. 2023

Journal of Clinical Investigation

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“…The importance of predicting the conversion to exudative AMD within 2 years is that it can impact the development of follow-up and monitoring schedules for a patient and for potentially selecting a higher-risk group of patients who may benefit from more expensive home-monitoring strategies [ 22 – 24 ]. A personalized monitoring approach could potentially allow earlier detection of these patients, thereby leading to earlier therapeutic intervention and better visual outcomes.…”

Section: Discussionmentioning

confidence: 99%

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers

Rudas

Chiang²,

Corradetti³

et al. 2023

PLOS Digit Health

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Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

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“…The importance of predicting the conversion to exudative AMD within 2 years is that it can impact the development of follow-up and monitoring schedules for a patient and for potentially selecting a higher-risk group of patients who may benefit from more expensive home-monitoring strategies. (Busquets and Sabbagh 2021) (Ho et al 2021) (Pershing and Stein 2017) A personalized monitoring approach could potentially allow earlier detection of these patients, thereby leading to earlier therapeutic intervention and better visual outcomes.…”

Section: Discussionmentioning

confidence: 99%

Automated large-scale AMD progression prediction using machine-read OCT biomarkers

Rudas

Chiang

Corradetti

et al. 2022

Preprint

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Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. While anti-vascular growth factor injections can be used to treat macular neovascularization (MNV), there are currently no treatments available to halt or reverse geographic atrophy, which is the late-stage of nonneovascular AMD. There is a great interest in detecting early biomarkers associated with a higher risk for AMD progression in order to design early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers, and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm significantly outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

show abstract

Current status of home monitoring technology for age-related macular degeneration

Cited by 15 publications

References 16 publications

Aflibercept more effectively weans patients with neovascular age-related macular degeneration off therapy compared with bevacizumab

Aflibercept more effectively weans patients with neovascular age-related macular degeneration off therapy compared with bevacizumab

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers

Automated large-scale AMD progression prediction using machine-read OCT biomarkers

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