Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning

Künstliche Intelligenz (KI) zur Früherkennung von Netzhauterkrankungen

Künstliche Intelligenz (KI) hat sich zu einem transformativen Werkzeug auf dem Gebiet der Augenheilkunde entwickelt und revolutioniert die Diagnose und Behandlung von Krankheiten. Diese Arbeit gibt einen umfassenden Überblick über KI-Anwendungen bei verschiedenen Netzhauterkrankungen und zeigt ihr Potenzial, die Effizienz von Vorsorgeuntersuchungen zu erhöhen, Frühdiagnosen zu erleichtern und die Patientenergebnisse zu verbessern. Wir erklären die grundlegenden Konzepte der KI, einschließlich des maschinellen Lernens (ML) und des Deep Learning (DL), und deren Anwendung in der Augenheilkunde und heben die Bedeutung von KI-basierten Lösungen bei der Bewältigung der Komplexität und Variabilität von Netzhauterkrankungen hervor. Wir gehen auch auf spezifische Anwendungen der KI im Zusammenhang mit Netzhauterkrankungen wie diabetischer Retinopathie (DR), altersbedingter Makuladegeneration (AMD), makulärer Neovaskularisation, Frühgeborenen-Retinopathie (ROP), retinalem Venenverschluss (RVO), hypertensiver Retinopathie (HR), Retinopathia pigmentosa, Morbus Stargardt, Morbus Best (Best’sche vitelliforme Makuladystrophie) und Sichelzellenretinopathie ein. Wir konzen­trieren uns auf die aktuelle Landschaft der KI-Technologien, einschließlich verschiedener KI-Modelle, ihrer Leistungsmetriken und klinischen Implikationen. Darüber hinaus befassen wir uns mit den Herausforderungen und Schwierigkeiten bei der Integration von KI in die klinische Praxis, einschließlich des «Black-Box-Phänomens», der Verzerrungen bei der Darstellung von Daten und der Einschränkungen im Zusammenhang mit der ganzheitlichen Bewertung von Patienten. Abschließend wird die kollaborative Rolle der KI an der Seite des medizinischen Fachpersonals hervorgehoben, wobei ein synergetischer Ansatz für die Erbringung von Gesundheitsdienstleistungen befürwortet wird. Es wird betont, wie wichtig es ist, KI als Ergänzung und nicht als Ersatz für menschliche Expertise einzusetzen, um ihr Potenzial zu maximieren, die Gesundheitsversorgung zu revolutionieren, Ungleichheiten in der Gesundheitsversorgung zu verringern und die Patientenergebnisse in der sich entwickelnden medizinischen Landschaft zu verbessern.

Photoreceptor assessment in age-related macular degeneration

Clinical trials investigating drugs for various stages of age-related macular degeneration (AMD) are actively underway and there is a strong interest in outcomes that demonstrate a structure-function-correlation. The ellipsoid zone (EZ), a crucial anatomical feature affected in this disease, has emerged as a strong contender. There is significant interest in evaluating EZ metrics on Optical Coherence Tomography (OCT), such as integrity and reflectivity, as disruption of this photoreceptor-rich layer may indicate disease progression. Loss of photoreceptor integrity in the junctional zone of geographic atrophy (GA) has been shown to exceed the areas of retinal pigment epithelial (RPE) atrophy, thus predicting future GA expansion. Furthermore, reduced visual acuity and retinal sensitivity have been correlated with loss of EZ integrity, underscoring a structure-function relationship. Photoreceptor integrity has also recently been acknowledged by the Food and Drug Administration (FDA), supporting its use as a primary endpoint in clinical trials investigating treatments for GA. However, the segmentation of this EZ still poses challenges. Continuous enhancements in OCT resolution and advancements in automated segmentation algorithms contribute to improved assessment of the EZ, strengthening its potential as an imaging biomarker for assessing photoreceptor function. It remains to be seen whether the EZ will serve as a surrogate marker for intermediate AMD. This article aims to provide an overview of the current understanding and knowledge of the EZ, while addressing ongoing challenges encountered in its assessment and interpretation.

Advancing the visibility of outer retinal integrity in neovascular age-related macular degeneration with high-resolution OCT

OBJECTIVE

To compare the visibility and accessibility of the outer retina in neovascular age-related macular degeneration (nAMD) between 2 OCT devices.

METHODS

In this prospective, cross-sectional exploratory study, differences in thickness and loss of individual outer retinal layers in eyes with nAMD and in age-matched healthy eyes between a next-level High-Res OCT device and the conventional SPECTRALIS OCT (both Heidelberg Engineering GmbH, Heidelberg, Germany) were analyzed. Eyes with nAMD and at least 250 nL of retinal fluid, quantified by an approved deep-learning algorithm (Fluid Monitor, RetInSight, Vienna, Austria), fulfilled the inclusion criteria. The outer retinal layers were segmented using automated layer segmentation and were corrected manually. Layer loss and thickness were compared between both devices using a linear mixed-effects model and a paired t test.

RESULTS

Nineteen eyes of 17 patients with active nAMD and 17 healthy eyes were included. For nAMD eyes, the thickness of the retinal pigment epithelium (RPE) differed significantly between the devices (25.42 μm [95% CI, 14.24-36.61] and 27.31 μm [95% CI, 16.12-38.50] for high-resolution OCT and conventional OCT, respectively; p = 0.033). Furthermore, a significant difference was found in the mean relative external limiting membrane loss (p = 0.021). However, the thickness of photoreceptors, RPE integrity loss, and photoreceptor integrity loss did not differ significantly between devices in the central 3 mm. In healthy eyes, a significant difference in both RPE and photoreceptor thickness between devices was shown (p < 0.001).

CONCLUSION

Central RPE thickness was significantly thinner on high-resolution OCT compared with conventional OCT images explained by superior optical separation of the RPE and Bruch's membrane.

Association between three-dimensional morphological features and functional indicators of neovascular age-related macular degeneration

PURPOSE

To investigate the correlation between morphological lesions and functional indicators in eyes with neovascular age-related macular degeneration (nAMD).

METHODS

This was a prospective observational study of treatment-naïve nAMD eyes. Various morphological lesions and impaired retinal structures were manually measured at baseline and month-3 in three-dimensional optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images, including the volumes (mm3) of macular neovascularization (MNV), avascular subretinal hyperreflective material (avascular SHRM), subretinal fluid (SRF), intraretinal fluid (IRF), serous pigment epithelial detachment (sPED) and the impaired area (mm2) of ellipsoid zone (EZ), external limiting membrane (ELM) and outer nuclear layer (ONL).

RESULTS

Sixty-three eyes were included. The volume of avascular SHRM showed persistent positive associations with the area of EZ damage, both at baseline, month-3, and change values (all P < 0.001). Poor BCVA (month-3) was associated with larger volumes of baseline IRF (β = 0.377, P < 0.001), avascular SHRM (β = 0.306, P = 0.032), and ELM impairment area (β = 0.301, P = 0.036) in multivariate model. EZ and ELM impairment were primarily associated with baseline avascular SHRM (β = 0.374, p = 0.003; β = 0.388, P < 0.001, respectively), while ONL impairment primarily associated with MNV (β = 0.475, P < 0.001).

CONCLUSION

The utilization of three-dimensional measurements elucidates the intrinsic connections among various lesions and functional outcomes. In particular, avascular SHRM plays an important role in prognosis of nAMD.

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.

Deep-Learning-Based Visualization and Volumetric Analysis of Fluid Regions in Optical Coherence Tomography Scans

Retinal volume computation is one of the critical steps in grading pathologies and evaluating the response to a treatment. We propose a deep-learning-based visualization tool to calculate the fluid volume in retinal optical coherence tomography (OCT) images. The pathologies under consideration are Intraretinal Fluid (IRF), Subretinal Fluid (SRF), and Pigmented Epithelial Detachment (PED). We develop a binary classification model for each of these pathologies using the Inception-ResNet-v2 and the small Inception-ResNet-v2 models. For visualization, we use several standard Class Activation Mapping (CAM) techniques, namely Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM, and Self-Matching CAM, to visualize the pathology-specific regions in the image and develop a novel Ensemble-CAM visualization technique for robust visualization of OCT images. In addition, we demonstrate a Graphical User Interface that takes the visualization heat maps as the input and calculates the fluid volume in the OCT C-scans. The volume is computed using both the region-growing algorithm and selective thresholding technique and compared with the ground-truth volume based on expert annotation. We compare the results obtained using the standard Inception-ResNet-v2 model with a small Inception-ResNet-v2 model, which has half the number of trainable parameters compared with the original model. This study shows the relevance and usefulness of deep-learning-based visualization techniques for reliable volumetric analysis.

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.