The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus

Pigment epithelial detachment composition indices in central serous chorioretinopathy as a biomarker for disease activity: A computational methodology and 1 year outcomes

PURPOSE

Investigation of pigment epithelial detachment (PED) characteristics in central serous chorioretinopathy (CSCR) is underrepresented in the literature. We present a novel computational approach to quantify PED composition indices (PEDCI) in CSCR and track changes over time.

METHODS

34 eyes with active CSCR were analyzed quarterly over a 1-year period. Cases were categorized into acute and chronic CSCR depending on a symptom duration of less than 3 months or more than 3 months respectively. PED, retinal and choroidal dimensions were manually measured, and interval changes were compared using repeated measures of variance ANOVA. PED composition analysis involved manual segmentation followed by automated sub segmentation of PED areas to identify serous, neovascular and fibrous tissues. PEDCI for each component was compared among cases of acute and chronic CSCR.

RESULTS

CMT and NSD-h decreased by 65.2 µm (p = 0.01), and 86.5 µm (p < 0.01) respectively at 12 months. At baseline, 7/17 acute CSCR eyes and 8/15 chronic CSCR eyes had a concomitant PED; acute cases had both serous and neovascular components (PEDCI-S: 16.95%, PEDCI-N: 40.3%), whereas chronic cases only had a neovascular component (PEDCI-S: 0%, PEDCI-N: 30.5%). At 12-month follow-up, 6/7 of acute CSCR group and 6/8 chronic CSCR group had a concomitant PED; PEDCI-S was largest for acute CSCR (53.4%) and PEDCI-N was largest for chronic CSCR (46.7%).

CONCLUSION

We identify a novel biomarker PEDCI to differentiate acute and chronic CSCR with higher PEDCI-S in acute CSCR, and higher PEDCI-N in chronic CSCR.

Regulatory and Implementation Considerations for Artificial Intelligence

Successful artificial intelligence (AI) implementation is predicated on the trust of clinicians and patients, and is achieved through a culture of responsible use, focusing on regulations, standards, and education. Otolaryngologists can overcome barriers in AI implementation by promoting data standardization through professional societies, engaging in institutional efforts to integrate AI, and developing otolaryngology-specific AI education for both trainees and practitioners.

Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes

Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection bias, we stacked convolutional and recurrent neural networks (CNN-RNN) to analyze complete optical coherence tomography (OCT) cubes in a row and predict diabetic macular edema (DME), in a real-world diabetic retinopathy screening program. A retrospective cohort study was carried out. Throughout 4-years, 5314 OCT cubes from 4408 subjects who attended to the diabetic retinopathy (DR) screening program were included. We arranged twenty-two (22) pre-trained CNNs in parallel with a bidirectional RNN layer stacked at the bottom, allowing the model to make a prediction for the whole OCT cube. The staff of retina experts built a ground truth of DME later used to train a set of these CNN-RNN models with different configurations. For each trained CNN-RNN model, we performed threshold tuning to find the optimal cut-off point for binary classification of DME. Finally, the best models were selected according to sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) with their 95% confidence intervals (95%CI). An ensemble of the best models was also explored. 5188 cubes were non-DME and 126 were DME. Three models achieved an AUROC of 0.94. Among these, sensitivity, and specificity (95%CI) ranged from 84.1-90.5 and 89.7-93.3, respectively, at threshold 1, from 89.7-92.1 and 80-83.1 at threshold 2, and from 80.2-81 and 93.8-97, at threshold 3. The ensemble model improved these results, and lower specificity was observed among subjects with sight-threatening DR. Analysis by age, gender, or grade of DME did not vary the performance of the models. CNN-RNN models showed high diagnostic accuracy for detecting DME in a real-world setting. This engine allowed us to detect extra-foveal DMEs commonly overlooked in other studies, and showed potential for application as the first filter of non-referable patients in an outpatient center within a population-based DR screening program, otherwise ended up in specialized care.

Challenges of artificial intelligence in medicine and dermatology

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.