#RetinalImaging

Premier Science

@premierscience.bsky.social

6 hours ago

A Non-Invasive Deep Learning Approach for Early Detection of Peripheral Arterial Disease–Diabetic Foot Ulcers Using Retinal Imaging: A Prospective Cohort Study - Premier Science Retinal fundus imaging, Peripheral arterial disease diagnosis, Diabetic foot ulcer risk prediction, Efficientnet-B3 convolutional network

doi.org/10.70389/PJS...

#deeplearning #diabeticfootulcers #retinalimaging #arterialdisease

Bridge2AI

@bridge2ai.bsky.social

1 month ago

Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis - PubMed Retinal image analysis has enjoyed groundbreaking advances in the last ten years due to seismic improvements in image analysis techniques from the field of computer vision. Previous reviews in deep learning and artificial intelligence (AI) (Schmidt-Erfurth et al., 2018; Ting et al., 2019) have eithe …

Review by Wu Y, Lee CS, and Lee AY: Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis 🔗 pubmed.ncbi.nlm.nih.gov/41352580/

#ai #ml #aritificialintelligence #retinalimaging #medsky #biomedicalinformatics #medtwitter

Simon J. Clark

@prof-clark.bsky.social

1 month ago

Diagnostic Performance of Ring Aperture Retro Mode Imaging for Detecting Pigment Migration in Age-Related Macular Degeneration - PubMed <span><b>Background/Objectives</b>: Pigment migration is a key biomarker of progression in age-related macular degeneration (AMD). This study assessed the diagnostic performance of ring aperture…

New study shows ring aperture Retro mode imaging detects pigment migration in AMD with high sensitivity—matching en face OCT and outperforming standard fundus imaging. #AMD #RetinalImaging #Ophthalmology #MedicalImaging #Diagnostics #VisionScience buff.ly/IiLyyjg

JMIR Publications

@jmirpub.bsky.social

4 months ago

Eye and Systemic Disease Management Changes After Teleophthalmology Screening in Primary Care: Retrospective Cross-Sectional Pilot Study of 200 Consecutive Patients Background: Undiagnosed ocular and systemic diseases are common in primary care populations, and many can be detected through retinal imaging before symptoms develop. Asynchronous store-and-forward teleophthalmology offers a scalable way to integrate eye screening into primary care, yet its broader impact beyond diabetic retinopathy detection remains underexplored. Objective: This study evaluated the outcomes of asynchronous store-and-forward teleophthalmology screening in a primary care clinic, including detection and triage of ocular conditions and subsequent changes in eye and systemic management. Methods: This was a retrospective cross-sectional analysis of the first 200 patients screened. Each patient underwent non-mydriatic external and posterior eye imaging, which was reviewed by a remote reading eye clinician. Reports included findings, triage decisions (routine monitoring vs in-person referral), and management recommendations. Subsequent changes in care were extracted from primary care and in-person specialist consult notes. Results: Of 200 patients (mean age 62, range 11-100), 71.5% had positive eye findings, and 40% were referred for in-person eye exams. Only 9% of referrals were for diabetic retinopathy; most were for glaucoma suspects, age-related macular degeneration, cataracts, or other ocular indicators. Image quality was high: 98% of fundus images were at least partially adequate. Among 35 patients with documented follow-up, 88% of in-person eye evaluations confirmed the remote findings. Eye management changes were initiated in 11 patients, while systemic changes occurred in 70, including new prescriptions for AREDS2 vitamins, antihypertensives, diabetes medications, and lipid-lowering agents. Conclusions: Asynchronous teleophthalmology screening in a primary care setting effectively identified both ocular and systemic conditions, leading to meaningful changes in care. The low rate of diabetic retinopathy among referrals highlights the broader diagnostic value of retinal imaging beyond diabetes management. This care model offers a scalable, high-yield strategy for proactive disease detection and interdisciplinary intervention at the primary care level.

JMIR Formative Res: Eye and Systemic Disease Management Changes After Teleophthalmology Screening in Primary Care: Retrospective Cross-Sectional Pilot Study of 200 Consecutive Patients #Teleophthalmology #EyeHealth #SystemicDisease #PrimaryCare #RetinalImaging

BE·LIGHT Project

@belightproject.bsky.social

5 months ago

🌟 BE-LIGHT on the move! 🌟
This year we joined key conferences: CNC (Mérida), BER (Granada) & ECBO (Munich), sharing advances in retinal oximetry, eyetracking & 3D skin lesion reconstruction 🚀👁️🌈
#BELIGHT #BER2025 #ECBO2025 #CNC2025 #Photonics #AI #Oximetry #Eyetracking #Retinalimaging

Institut Langevin

@instlangevin.bsky.social

6 months ago

Pedro Mecê wins ERC Starting Grant 2025 for MIRACLE-AD👏 The project uses advanced retinal imaging to detect early signs of Alzheimer’s, mapping neuronal & vascular responses at cellular resolution. 🧠👁
A brilliant blend of physics, optics & clinical medicine!
#ERC #RetinalImaging #Neuroscience

40:40 Recruitment - celia@4040recruitment.co.uk

@4040rec.bsky.social

7 months ago

#Alzheimersdisease #AI #artificialintelligence #Alzheimers #heartdisease #dementia #burdenofdementia #AIsupportedretinascans #retinascans #retina #npjDigitalMedicine #EyeAD #retinalimaging #opticalcoherencetomographyangiography #OCTA #AIinmedicine #retinalscans
pmlive.com/intelligence...

JMIR Publications

@jmirpub.bsky.social

7 months ago

Performance of a Retinal Imaging Camera With On-Device Intelligence for Primary Care: Retrospective Study Background: Access to screening continues to be a barrier for the early detection of diabetic retinopathy (DR). Primary care-based DR screening could improve access, but operational challenges, such as cost and workflow management, hamper the widespread adoption of retinal camera systems in primary care clinics in the US. Objective: To develop and evaluate a retinal screening system suitable for integration into a primary care workflow. Methods: We developed a non-mydriatic, 45-degree field imaging retinal camera system, the Verily Numetric Retinal Camera (VNRC), able to generate high fidelity retinal images enabled by on-device intelligent features. The VNRC output flows into cloud-based software that accepts and routes digitized images for grading. We evaluated the performance and #usability of the VNRC with two studies. A retrospective performance study compared the performance of VNRC against a reference camera (Crystalvue NFC-700) as well as the correlation between VNRC capture status and gradability (as determined by ophthalmologist graders). The #usability study simulated a primary care setting for a combined cohort of trained and untrained users (corresponding to patients in the simulation) and operators (corresponding to healthcare personnel in the simulation), where respondents completed a questionnaire about their user experience after attempting to capture images with the VNRC. Results: In the comparative performance study (N=108, K=206 images), a total of 98.5% of images captured by the VNRC were graded as sufficient for clinical interpretation compared to 97.1% of Crystalview NFC-700 images (difference in proportion was 0.015, 95% CI: -0.007 - 0.033). In the quality control algorithm evaluation (N = 172, K = 343 images), we found a positive association (φ =0.53) between gradability status (gradable/non-gradable) as determined by ophthalmologists and the capture status (recapture not-needed/needed) as determined by the VNRC quality control algorithm. In the #usability study, (n=15 users, and n=30 operators), all participating users (15/15) indicated that they were able to have both eyes screened easily. Most users and operators indicated agreement (from somewhat agree to strongly agree) with statements describing the imaging process as intuitive (15/15 [100%] and 29/30 [96.7%]), comfortable (15/15 [100%] and 30/30 [100%]), and allowing for a positive experience (15/15 [100%] and 30/30) [100%], of users and operators, respectively. Conclusions: Our findings about the performance and #usability of this retinal camera system support its deployment as an integrated end-to-end retinal service for primary care. These results warrant additional studies to fully characterize real-world #usability across a wider and diverse set of primary care clinics. Clinical Trial: NA

JMIR Formative Res: Performance of a Retinal Imaging Camera With On-Device Intelligence for Primary Care: Retrospective Study #DiabeticRetinopathy #RetinalImaging #PrimaryCare #HealthcareInnovation #MedicalTechnology

MarcosLab UR

@marcoslabur.bsky.social

8 months ago

The #AdaptiveOptics in the #eye Course kicked off yesterday @cvsuor.bsky.social with 6 intensive hours on #AO for #RetinalImaging & #VisualSimulation w @schalleklab.bsky.social & @marcoslabur.bsky.social. More today with out engineers on #HowtoBuild on; #RochesterOptics @urengineering.bsky.social

@xstreamfm.bsky.social

11 months ago

AL. State Agencies:Alabama News Beacon #Medicaid #Healthcare #RetinalImaging #DiseaseDetection #AlabamaNews

Healthcare Today

@healthcaretoday.bsky.social

1 year ago

Heart Eye is to launch Dr.Noon CVD The first AI-powered cardiovascular screening technology using retinal imaging can deliver the risk of a patient’s cardiovascular disease in three minutes.

Heart Eye is to launch Dr.Noon CVD, the first AI-powered cardiovascular screening technology using retinal imaging, which can deliver the risk of a patient’s cardiovascular disease in three minutes.

healthcaretoday.com/article/hear...

#healthcare #AI #cardiovascularscreening #tech #retinalimaging

Jessica Jezebel

@jessica-joy.bsky.social

1 year ago

Macular detachment. This retinal image shows the inner lining of the eye falling in upon itself.

#optech #retinalimaging #occularoddities