Advances in multimodal imaging in ophthalmology

Intravitreal conbercept for chronic central serous chorioretinopathy with occult CNV: a retrospective clinical study based on multimodal ophthalmic imaging

Purpose

This study aimed to evaluate the therapeutic efficacy and safety of intravitreal conbercept in patients with chronic central serous chorioretinopathy (cCSC) complicated by occult choroidal neovascularization (CNV), and to explore its potential in improving visual function and various ophthalmic parameters.

Methods

This retrospective, longitudinal, comparative study included 50 patients diagnosed with cCSC and occult CNV. Patients underwent intravitreal conbercept injections and were monitored over a six-month period. Comprehensive ophthalmic evaluation included best-corrected visual acuity (BCVA), central macular thickness (CMT), subretinal fluid (SRF) status, subfoveal choroidal thickness (SFCT), and optical coherence tomography angiography (OCTA). OCTA parameters such as foveal avascular zone (FAZ) area and CNV lesion characteristics were analyzed pre- and post-treatment. Patients were categorized based on changes in CNV lesion size to identify prognostic factors influencing treatment response.

Results

Significant improvements were observed in mean BCVA from baseline (0.78 ± 0.50 vs. 0.32 ± 0.31, p < 0.01) in all 50 eyes of the patients, except for one eye. Additionally, there were significant improvements in CMT, SRF status, SFCT, FAZ area, and CNV lesion size post-treatment (p < 0.05). Pearson correlation analysis indicated a positive correlation between baseline BCVA and CMT (r = 0.3615, p = 0.0116). Changes in BCVA post-treatment correlated with alterations in CMT, SRF diameter, and CNV lesion size. Patients with a favorable treatment response had significantly lower baseline CMT (312.17 ± 57.39 vs. 428.86 ± 114.54, p < 0.05) and CNV vessel diameter (17.46 ± 2.72 vs. 24.84 ± 4.02, p < 0.01) compared to those with unfavorable responses.

Conclusion

Intravitreal conbercept injection was found to be safe and effective in improving BCVA and various ophthalmic parameters in patients with cCSC complicated by occult CNV, with no significant adverse effects observed during the study period. Baseline CMT, SRF diameter, CNV lesion size, and mean CNV vessel diameter were identified as valuable indicators for assessing treatment response and prognosis. These findings provide important insights for the clinical management and prognostic evaluation of cCSC patients with occult CNV, highlighting the utility of multimodal imaging in assessing treatment outcomes.

Interpretation of Clinical Retinal Images Using an Artificial Intelligence Chatbot

Purpose

To assess the performance of Chat Generative Pre-Trained Transformer-4 in providing accurate diagnoses to retina teaching cases from OCTCases.

Design

Cross-sectional study.

Subjects

Retina teaching cases from OCTCases.

Methods

We prompted a custom chatbot with 69 retina cases containing multimodal ophthalmic images, asking it to provide the most likely diagnosis. In a sensitivity analysis, we inputted increasing amounts of clinical information pertaining to each case until the chatbot achieved a correct diagnosis. We performed multivariable logistic regressions on Stata v17.0 (StataCorp LLC) to investigate associations between the amount of text-based information inputted per prompt and the odds of the chatbot achieving a correct diagnosis, adjusting for the laterality of cases, number of ophthalmic images inputted, and imaging modalities.

Main Outcome Measures

Our primary outcome was the proportion of cases for which the chatbot was able to provide a correct diagnosis. Our secondary outcome was the chatbot's performance in relation to the amount of text-based information accompanying ophthalmic images.

Results

Across 69 retina cases collectively containing 139 ophthalmic images, the chatbot was able to provide a definitive, correct diagnosis for 35 (50.7%) cases. The chatbot needed variable amounts of clinical information to achieve a correct diagnosis, where the entire patient description as presented by OCTCases was required for a majority of correctly diagnosed cases (23 of 35 cases, 65.7%). Relative to when the chatbot was only prompted with a patient's age and sex, the chatbot achieved a higher odds of a correct diagnosis when prompted with an entire patient description (odds ratio = 10.1, 95% confidence interval = 3.3-30.3, P < 0.01). Despite providing an incorrect diagnosis for 34 (49.3%) cases, the chatbot listed the correct diagnosis within its differential diagnosis for 7 (20.6%) of these incorrectly answered cases.

Conclusions

This custom chatbot was able to accurately diagnose approximately half of the retina cases requiring multimodal input, albeit relying heavily on text-based contextual information that accompanied ophthalmic images. The diagnostic ability of the chatbot in interpretation of multimodal imaging without text-based information is currently limited. The appropriate use of the chatbot in this setting is of utmost importance, given bioethical concerns.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.