Advances in multimodal imaging in ophthalmology

AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices

BACKGROUND

Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging.

OBJECTIVE

This study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD.

METHODS

The Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process.

RESULTS

Preliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024.

CONCLUSIONS

This systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52602.

Retinal Changes in Parkinson's Disease: A Non-invasive Biomarker for Early Diagnosis

Parkinson's disease (PD) is caused due to degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc) which leads to the depletion of dopamine in the body. The lack of dopamine is mainly due to aggregation of misfolded α-synuclein which causes motor impairment in PD. Dopamine is also required for normal retinal function and the light-dark vision cycle. Misfolded α-synuclein present in inner retinal layers causes vision-associated problems in PD patients. Hence, individuals with PD also experience structural and functional changes in the retina. Mutation in LRRK2, PARK2, PARK7, PINK1, or SNCA genes and mitochondria dysfunction also play a role in the pathophysiology of PD. In this review, we discussed the different etiologies which lead to PD and future prospects of employing non-invasive techniques and retinal changes to diagnose the onset of PD earlier.

Practical applications of vitreous imaging for the treatment of vitreous opacities with YAG vitreolysis

PURPOSE

To demonstrate the methodology and efficacy of using scanning laser ophthalmoscopy (SLO) and dynamic optical coherence tomography (OCT) to identify and treat symptomatic vitreous floaters using yttrium-aluminum garnet laser vitreolysis (YLV).

METHODS

This is a case series highlighted from a cross sectional retrospective study conducted at the Vitreous Retina Macula Specialists of Toronto. Forty eyes from thirty-five patients were treated with YLV between November 2018 and December 2020 for symptomatic floaters and imaged with SLO and dynamic OCT. Patients were re-treated with YLV if they reported ongoing significant vision symptoms during follow-up which correlated to visible opacities on exam and or imaging. Three cases will be highlighted to present the practical applications of SLO and dynamic OCT imaging for YLV treatment.

RESULTS

Forty treated eyes were enrolled in this study, with twenty-six eyes (65%) requiring at least one repeat YLV treatment following the first treatment due to ongoing symptomatic floaters. Following the first YLV, there was a significant improvement in overall mean best corrected visual acuity compared to before treatment (0.11 ± 0.20 LogMAR units vs. 0.14 ± 0.20 LogMAR units, p = 0.02 (paired t test)). Case 1 demonstrates a dense, solitary vitreous opacity that has been localized with dynamic OCT imaging to track its movements and retinal shadowing with the patient's eye movements. Case 2 shows the utility of adjusting the fixation target to monitor the movement of vitreous opacities in real-time. Case 3 exhibits an association between decreased symptom burden and vitreous opacity density after YLV.

CONCLUSION

Image-guided YLV facilitates the localization and confirmation of vitreous opacities. SLO and dynamic OCT of the vitreous can provide a real-time evaluation of floater size, movement, and morphology, to help clinicians target treatment and monitoring of symptomatic floaters.

[Intraoperative optical coherence tomography in vitreoretinal surgery]

This article reviews literature on the use of intraoperative optical coherence tomography (iOCT) in vitreoretinal surgery, describes the historical aspects of the development of this technology from portable devices to optical coherence tomographs integrated into the surgical microscope, considers the advantages, limitations and disadvantages of this technology, which are now becoming obvious due to the accumulated experience. The review also explores the prospects for the development of iOCT and possible ways to solve its problems. In addition, the review presents and systematizes clinical findings that can be revealed with iOCT in such diseases as rhegmatogenous retinal detachment, complications of proliferative diabetic retinopathy, macular pathology, etc.

Real-time OCT image denoising using a self-fusion neural network

Optical coherence tomography (OCT) has become the gold standard for ophthalmic diagnostic imaging. However, clinical OCT image-quality is highly variable and limited visualization can introduce errors in the quantitative analysis of anatomic and pathologic features-of-interest. Frame-averaging is a standard method for improving image-quality, however, frame-averaging in the presence of bulk-motion can degrade lateral resolution and prolongs total acquisition time. We recently introduced a method called self-fusion, which reduces speckle noise and enhances OCT signal-to-noise ratio (SNR) by using similarity between from adjacent frames and is more robust to motion-artifacts than frame-averaging. However, since self-fusion is based on deformable registration, it is computationally expensive. In this study a convolutional neural network was implemented to offset the computational overhead of self-fusion and perform OCT denoising in real-time. The self-fusion network was pretrained to fuse 3 frames to achieve near video-rate frame-rates. Our results showed a clear gain in peak SNR in the self-fused images over both the raw and frame-averaged OCT B-scans. This approach delivers a fast and robust OCT denoising alternative to frame-averaging without the need for repeated image acquisition. Real-time self-fusion image enhancement will enable improved localization of OCT field-of-view relative to features-of-interest and improved sensitivity for anatomic features of disease.