Diagnostic accuracy of handheld fundus photography: A comparative study of three commercially available cameras

Comparison of Three Handheld Fundus Cameras for Assessment of the Vertical Cup-To-Disk Ratio

Purpose To compare the quality of optic nerve photographs from three different handheld fundus cameras and to assess the reproducibility and agreement of vertical cup-to-disk ratio (VCDR) measurements from each camera. Methods Adult patients from a comprehensive ophthalmology clinic and an intravitreous injection clinic in northern Thailand were recruited for this cross-sectional study. Each participant had optic nerve photography performed with each of 3 handheld cameras: the Volk iNview, Volk Pictor Plus, and Peek Retina. Images were graded for VCDR in a masked fashion by two photo-graders and images with > 0.2 discrepancy in VCDR were assessed by a third photo-grader. Results A total of 355 eyes underwent imaging with three different handheld fundus cameras. Optic nerve images were judged ungradable in 130 (37%) eyes imaged with Peek Retina, compared to 36 (10%) and 55 (15%) eyes imaged with the iNview and Pictor Plus, respectively. For 193 eyes with gradable images from all 3 cameras, inter-rater reliability for VCDR measurements was poor or moderate for each of the cameras, with intraclass correlation coefficients ranging from 0.41 to 0.52. A VCDR ≥ 0.6 was found in 6 eyes on iNview images, 9 eyes on Pictor Plus images, and 3 eyes on Peek images, with poor agreement between cameras (e.g., no eyes graded as VCDR ≥ 0.6 on images from both the iNview and Pictor Plus). Conclusions Inter-rater reliability of VCDR grades from 3 handheld cameras was poor. Cameras did not agree on which eyes had large VCDRs.

Mobile Diagnostic Clinics

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Artificial intelligence and hemodynamic studies in optical coherence tomography angiography for diabetic retinopathy evaluation: A review

Diabetic retinopathy (DR) is a rapidly emerging retinal abnormality worldwide, which can cause significant vision loss by disrupting the vascular structure in the retina. Recently, optical coherence tomography angiography (OCTA) has emerged as an effective imaging tool for diagnosing and monitoring DR. OCTA produces high-quality 3-dimensional images and provides deeper visualization of retinal vessel capillaries and plexuses. The clinical relevance of OCTA in detecting, classifying, and planning therapeutic procedures for DR patients has been highlighted in various studies. Quantitative indicators obtained from OCTA, such as blood vessel segmentation of the retina, foveal avascular zone (FAZ) extraction, retinal blood vessel density, blood velocity, flow rate, capillary vessel pressure, and retinal oxygen extraction, have been identified as crucial hemodynamic features for screening DR using computer-aided systems in artificial intelligence (AI). AI has the potential to assist physicians and ophthalmologists in developing new treatment options. In this review, we explore how OCTA has impacted the future of DR screening and early diagnosis. It also focuses on how analysis methods have evolved over time in clinical trials. The future of OCTA imaging and its continued use in AI-assisted analysis is promising and will undoubtedly enhance the clinical management of DR.

Bridging the Camera Domain Gap With Image-to-Image Translation Improves Glaucoma Diagnosis

Purpose

The purpose of this study was to improve the automated diagnosis of glaucomatous optic neuropathy (GON), we propose a generative adversarial network (GAN) model that translates Optain images to Topcon images.

Methods

We trained the GAN model on 725 paired images from Topcon and Optain cameras and externally validated it using an additional 843 paired images collected from the Aravind Eye Hospital in India. An optic disc segmentation model was used to assess the disparities in disc parameters across cameras. The performance of the translated images was evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), 95% limits of agreement (LOA), Pearson's correlations, and Cohen's Kappa coefficient. The evaluation compared the performance of the GON model on Topcon photographs as a reference to that of Optain photographs and GAN-translated photographs.

Results

The GAN model significantly reduced Optain false positive results for GON diagnosis, with RMSE, PSNR, and SSIM of GAN images being 0.067, 14.31, and 0.64, respectively, the mean difference of VCDR and cup-to-disc area ratio between Topcon and GAN images being 0.03, 95% LOA ranging from -0.09 to 0.15 and -0.05 to 0.10. Pearson correlation coefficients increased from 0.61 to 0.85 in VCDR and 0.70 to 0.89 in cup-to-disc area ratio, whereas Cohen's Kappa improved from 0.32 to 0.60 after GAN translation.

Conclusions

Image-to-image translation across cameras can be achieved by using GAN to solve the problem of disc overexposure in Optain cameras.

Translational Relevance

Our approach enhances the generalizability of deep learning diagnostic models, ensuring their performance on cameras that are outside of the original training data set.