BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown.
METHODS: This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set.
RESULTS: In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74-0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86-1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73-1.00).
CONCLUSIONS: AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk.
BACKGROUND: This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).

UNASSIGNED: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness.
UNASSIGNED: The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning.
UNASSIGNED: A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging.
UNASSIGNED: Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices.
UNASSIGNED: In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

OBJECTIVE: To highlight the influence of preocular and ocular vascular circulatory dynamics on the vascular density (VD) of retinal capillary plexuses (RCPs) and choriocapillaris (CC) in patients with and without cardiovascular risk (CVR) factors.
METHODS: A retrospective observational study in patients with and without CVR factors (type 1 and 2 diabetes, arterial hypertension, and hypercholesterolemia). Fluorescein (FA) and indocyanine (ICGA) angiography circulatory times were arterial time (FAAT), start (FAstartLF) and end (FAendLF) of laminar flow, and arterial time (ICGAAT), respectively. OCT angiography VDs were superficial (VDSCP) and deep (VDDCP) RCPs and CC (VDCC) VDs. Correlation and regression analysis were performed after adjusting for confounding factors.
RESULTS: 177 eyes of 177 patients (mean age: 65.2 ± 15.9 years, n = 92 with and 85 without CVR) were included. VDSCP and VDDCP were significantly inversely correlated with FAAT, FAstartLF and FAendLF likewise VDCC with ICGAAT. Correlations were stronger in patients without CVR than with CVR. CVR, FAAT, FAstartLF and FAendLF were more strongly correlated with VDDCP than VDSCP. FAAT, FAstartLF and FAendLF significantly impacted VDSCP and VDDCP, likewise ICGAAT impacted VDDCP. VDDCP was most strongly impacted by FAAT and FAstartLF.
CONCLUSIONS: Ocular and pre-ocular circulatory dynamics significantly impacted RCPs and CC VDs, especially deep RCP.

BACKGROUND: An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome.
OBJECTIVE: This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI).
METHODS: The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply.
RESULTS: Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries.
CONCLUSIONS: A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases.

(1) Background: Previously, VESsel GENeration (VESGEN) software was used to map and quantify vascular changes observed on fluorescein angiography (FA) in subjects (n = 15 eyes) with retinal pathology ranging from mild non-proliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR). In the current study, we used VESGEN for the assessment of individuals with early-stage NPDR imaged by FA (Cohort 1) and by optical coherence tomography angiography (OCTA; Cohort 2). (2) Methods: Cohort 1 included type 2 diabetics (T2D), represented 21 eyes (ranging from no DR to moderate DR), and also included nondiabetic controls (NDC; n = 15 eyes). Cohort 2 consisted of 23 eyes from T2D subjects (including no DR subjects and moderate DR subjects) and NDC (n = 18 eyes). (3) Results: In the FA-VESGEN study, total tortuosity (Tv) of microvessels (G ≥ 6) increased in T2D with mild DR compared to the controls. In contrast, the VESGEN analysis of OCTA images showed that vessel length (characterized as density) was lower in T2D subjects before the diagnosis of DR and following the diagnosis of DR when compared to the controls. Additionally, T2D showed a significant decrease in vessel area (density). (4) Conclusions: FA elucidated the vessel morphology of small-generation microvessels to a greater degree than OCTA; however, OCTA identified changes in vessel density better than FA. VESGEN analysis can be used with both standard FA and OCTA to facilitate our understanding of early events in DR, including before the clinical diagnosis of DR.

Stargardt disease (STGD1), associated with biallelic variants in the ABCA4 gene, is the most common heritable macular dystrophy and is currently untreatable. To identify potential treatment targets, we characterized surviving STGD1 photoreceptors. We used clinical data to identify macular regions with surviving STGD1 photoreceptors. We compared the hyperreflective bands in the optical coherence tomographic (OCT) images that correspond to structures in the STGD1 photoreceptor inner segments to those in controls. We used adaptive optics scanning light ophthalmoscopy (AO-SLO) to study the distribution of cones and AO-OCT to evaluate the interface of photoreceptors and retinal pigment epithelium (RPE). We found that the profile of the hyperreflective bands differed dramatically between patients with STGD1 and controls. AO-SLOs showed patches in which cone densities were similar to those in healthy retinas and others in which the cone population was sparse. In regions replete with cones, there was no debris at the photoreceptor-RPE interface. In regions with sparse cones, there was abundant debris. Our results raise the possibility that pharmaceutical means may protect surviving photoreceptors and so mitigate vision loss in patients with STGD1.

UNASSIGNED: Handheld optical coherence tomography (HH-OCT) systems enable point-of-care ophthalmic imaging in bedridden, uncooperative, and pediatric patients. Handheld spectrally encoded coherence tomography and reflectometry (HH-SECTR) combines OCT and spectrally encoded reflectometry (SER) to address critical clinical challenges in HH-OCT imaging with real-time en face retinal aiming for OCT volume alignment and volumetric correction of motion artifacts that occur during HH-OCT imaging.
UNASSIGNED: We aim to enable robust clinical translation of HH-SECTR and improve clinical ergonomics during point-of-care OCT imaging for ophthalmic diagnostics.
UNASSIGNED: HH-SECTR is redesigned with (1) optimized SER optical imaging for en face retinal aiming and retinal tracking for motion correction, (2) a modular aluminum form factor for sustained alignment and probe stability for longitudinal clinical studies, and (3) one-handed photographer-ergonomic motorized focus adjustment.
UNASSIGNED: We demonstrate an HH-SECTR imaging probe with micron-scale optical-optomechanical stability and use it for in vivo human retinal imaging and volumetric motion correction.
UNASSIGNED: This research will benefit the clinical translation of HH-SECTR for point-of-care ophthalmic diagnostics.

OBJECTIVE: To assess the sensitivity and specificity of the \"triple layer sign\" (TLS) (retinal pigment epithelium (RPE), neovascular tissue, and Bruch\'s membrane) on structural optical coherence tomography (OCT) images for the diagnosis of treatment-naïve non-exudative type-1 macular neovascularization (NE-MNV) in age-related macular degeneration (AMD).
METHODS: Cross-sectional study.
METHODS: Two masked retinal experts evaluated the presence of the TLS in eyes with NE-MNV and controls with an RPE elevation without exudation due to other causes than NE-MNV in AMD [e.g., medium-large drusen, cuticular drusen, basal laminar deposits (BlamD)].
RESULTS: 130 eyes of 98 consecutive patients met the study criteria; 40 eyes of 40 patients satisfied the criteria for being included in the NE-MNV secondary to AMD group (27 females, 13 males, with a mean age of 73.8 ± 8.0 years), and 90 eyes of 58 patients met the criteria to be included in the control group (31 eyes were included in the medium-to-large drusen sub-group, 32 eyes in the cuticular drusen sub-group, and 27 eyes in the BlamD group. The TLS was observed in 39/40 patients with NE-MNV and 8/90 controls. The sensitivity and specificity of the TLS for the diagnosis of NE-MNV were 97% and 91%, respectively.
CONCLUSIONS: The TLS on OCT demonstrated high sensitivity and specificity values in detecting treatment-naive type 1 NE-MNV.

(1) Background: The fundus examination is one of the best and popular methods in the assessment of vascular status in the human body. Direct viewing of retinal vessels by ophthalmoscopy has been utilized in judging hypertensive change or arteriosclerosis. Recently, fundus imaging with the non-mydriatic scanning laser ophthalmoscope (SLO) has been widely used in ophthalmological clinics since it has multimodal functions for optical coherence tomography or angiography with contrast agent dye. The purpose of this study was to examine the utility in detecting arteriosclerosis of retinal vessels in SLO images; (2) Methods: Both color and blue standard field SLO images of eyes with diabetic retinopathy (DR) were examined retrospectively. Retinal arteriosclerosis in color SLO images was graded according to the Scheie classification. Additionally, characteristics of retinal arterioles in blue SLO images were identified and examined for their relevance to arteriosclerosis grades, stages of DR or general complications; (3) Results: Relative to color fundus images, blue SLO images showed distinct hyper-reflective retinal arterioles against a monotone background. Irregularities of retinal arterioles identified in blue SLO images were frequently observed in the eyes of patients with severe arteriosclerosis (Grade 3: 79.0% and Grade 4: 81.8%). Furthermore, the findings on arterioles were more frequently associated with the eyes of DR patients with renal dysfunction (p < 0.05); (4) Conclusions: While color SLO images are equally as useful in assessing retinal arteriosclerosis as photography or ophthalmoscopy, the corresponding blue SLO images show arteriosclerotic lesions with high contrast in a monotone background. Retinal arteriosclerosis in eyes of advanced grades or advanced DR frequently show irregularities of retinal arterioles in the blue images. The findings of low, uneven, or discontinuous attenuation were easier to find in blue than in color SLO images. Consequently, blue SLO images can show pathological micro-sclerosis in retinal arterioles and are potentially one of the safe and practical methods for the vascular assessment of diabetic patients.

This review critically assesses the applicability of retinal periphery imaging technology, scrutinizing its practical limitations and potential advancements within ophthalmology. It underscores the significant costs and the need to evaluate the clinical utility of optical coherence tomography (OCT) and OCT angiography devices. It emphasizes how clinicians should consider their practice-specific use-cases while investing in devices with capabilities like ultra-widefield OCT, autofluorescence imaging, and angiography. The paper also highlights the challenges associated with image acquisition, such as artifact management and patient cooperation for extended fixation periods. This review outlines the utility of these modalities in various retinal pathologies, as well as their contribution to telemedicine and personalized care, facilitated by artificial intelligence for improved image processing, quantification, and interpretation. These techniques potentially offer a more comprehensive understanding of peripheral retinal conditions and associated pathologies, thus influencing clinical decision-making, particularly in remote regions with limited specialist access.