Quantitative Assessment of Automated Optical Coherence Tomography Image Analysis Using a Home-Based Device for Self-Monitoring Neovascular Age-Related Macular Degeneration

Oakley, Jonathan D.; Verdooner, Steven R.; Russakoff, Daniel B.; Brucker, Alexander J.; Seaman, John W.; Sahni, Jayashree; Domenico, Carlo B.; Cozzi, Mariano; Rogers, James V.; Staurenghi, Giovanni

doi:10.1097/iae.0000000000003677

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…( 16) However, the application of NVHO or other home OCT devices is confined due to their relatively small imaging area and low scanning speed, limiting their utility in the detection and measurement of macular fluid. (16)(17)(18) In contrast, our self-imaging OCT device is equipped with scanning parameters comparable to those of the commercial desktop SD-OCT, conferring great advantages not only in the monitoring macular thickness but also in the screening and diagnosis of most retinal diseases.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a self-imaging OCT for remote diagnosis and monitoring of retinal diseases

Liu,

Huang,

Wang

et al. 2023

Br J Ophthalmol

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ObjectivesTo evaluate the feasibility and accuracy of a portable, self-imaging optical coherence tomography (OCT) for measuring central subfield thickness (CST) and achieving diagnostic concordance for retinal lesions compared with clinic-based spectral-domain OCT (SD-OCT).MethodsThis comparative, cross-sectional study was conducted between August 2020 and February 2021. Two groups of adult participants were recruited: (1) a selected cohort of 160 participants with confirmed diagnosis and (2) a consecutive cohort of 315 participants recruited randomly. All participants underwent self-imaging OCT examination, as well as standard OCT examination. CST was automatically calculated for comparisons between the two OCT devices. Diagnostic concordance for retinal lesions and the success rate of self-imaging were assessed within the consecutive cohort.ResultsIn the selected cohort, self-imaging OCT images yielded consistent CST with SD-OCT, with a mean difference of 0.1±7.7 µm for normal eyes, 4.9±10.6 µm for macular oedema, −1.3±9.5 µm for choroidal neovascularisation, 5.0±7.8 µm for epiretinal membrane. The self-imaging OCT also demonstrated good repeatability, with a mean test–retest difference in CST of 0.7±3.9 µm and limits of agreement ranging from −6.9 to 8.3 µm. Additionally, within the consecutive cohort, interdevice κ values ranged for detecting various retinal lesions ranged from 0.8 to 1.0, except in the cases of retinal detachment (κ=0.5). All eyes (100%) in the selected cohort and 242 eyes (76.8%) in the consecutive cohort successfully completed self-imaging. Participants spent less time on self-imaging compared with SD-OCT operated by a technician (66.7±20.1 vs 73.3±32.5, p<0.01). A majority of participants (90%) found the self-imaging process ‘easy’ and ‘comfortable’.Conclusions and relevanceThis study demonstrates that our self-imaging OCT and clinical-used SD-OCT are highly consistent not only in measuring the CST but also in identifying most retinal lesions.

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Section: Discussionmentioning

confidence: 99%

Evaluation of a self-imaging OCT for remote diagnosis and monitoring of retinal diseases

Liu,

Huang,

Wang

et al. 2023

Br J Ophthalmol

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“…A prototype SD-OCT device developed by OCT Health LLC (Sacramento, CA) has been developed for home use [50 ▪ ]. It acquires line or star pattern scans, with scanning speed of 20 000 A-scans per second.…”

Section: Home Monitoring In Ophthalmologymentioning

confidence: 99%

“…Retinal layer segmentation was performed using Orion commercial software [51], and these contours were input to a deep learning algorithm to perform retinal fluid segmentation. The fluid segmentation and quantification from this automated grading were very similar to those from manual grading [50 ▪ ]. The deep learning approach used a U-Net-like architecture previously developed from swept-source OCT cube scans.…”

Section: Home Monitoring In Ophthalmologymentioning

confidence: 99%

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Artificial intelligence for home monitoring devices

Keenan,

Loewenstein

2023

Current Opinion in Ophthalmology

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Purpose of review Home monitoring in ophthalmology is appropriate for disease stages requiring frequent monitoring or rapid intervention, for example, neovascular age-related macular degeneration (AMD) and glaucoma, where the balance between frequent hospital attendance versus risk of late detection is a constant challenge. Artificial intelligence approaches are well suited to address some challenges of home monitoring. Recent findings Ophthalmic data collected at home have included functional (e.g. perimetry), biometric (e.g. intraocular pressure), and imaging [e.g. optical coherence tomography (OCT)] data. Potential advantages include early detection/intervention, convenience, cost, and visual outcomes. Artificial intelligence can assist with home monitoring workflows by handling large data volumes from frequent testing, compensating for test quality, and extracting useful metrics from complex data. Important use cases include machine learning applied to hyperacuity self-testing for detecting neovascular AMD and deep learning applied to OCT data for quantifying retinal fluid. Summary Home monitoring of health conditions is useful for chronic diseases requiring rapid intervention or frequent data sampling to decrease risk of irreversible vision loss. Artificial intelligence may facilitate accurate, frequent, large-scale home monitoring, if algorithms are integrated safely into workflows. Clinical trials and economic evaluations are important to demonstrate the value of artificial intelligence-based home monitoring, towards improved visual outcomes.

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Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration

Borrelli,

Oakley,

Iaccarino

et al. 2023

Eye

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Quantitative Assessment of Automated Optical Coherence Tomography Image Analysis Using a Home-Based Device for Self-Monitoring Neovascular Age-Related Macular Degeneration

Cited by 6 publications

References 21 publications

Evaluation of a self-imaging OCT for remote diagnosis and monitoring of retinal diseases

Evaluation of a self-imaging OCT for remote diagnosis and monitoring of retinal diseases

Artificial intelligence for home monitoring devices

Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration

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