During ARVO 2024,
- AI-Assisted Automated Screening of Retinal Anomalies in OCT Images: A Deep Learning Approach
- All that Glitters is not Gold: Are Current Retina Foundation Models Able to Efficiently Detect Hypertensive Retinopathy?
- Domain Generalization for Diabetic Retinopathy Grading through Vision-Language Foundation Models
OCT Model:
DIAGNOS Convolutional Neural Network (CNN) models, based on OCT images, have achieved remarkable accuracy in identifying subtle changes in retinal morphology indicative of various diseases, such as macular edema, diabetic retinopathy, and age-related macular degeneration. These models, trained on large-scale datasets, extract relevant features from images automatically, enabling early detection of retinal anomalies. Early intervention facilitated by these models has the potential to prevent or delay vision loss and associated complications.
Hypertensive Retinopathy:
The early detection of Hypertensive Retinopathy (HR) is crucial to prevent irreversible damage to the retinal microcirculation as well as risk prediction tools in cardiovascular disease prevention.
Vision Language Foundation Model:
These innovative AI systems provide objective assessments and assist clinicians in interpreting complex Retinal Fundus and OCT images. By enhancing diagnostic confidence and reducing variability in interpretation among practitioners,
"We are excited to present our latest advancements in AI-driven retinal imaging at ARVO 2024," said
Here are the titles of our presentations with the link to the ARVO program.
- AI-Assisted Automated Screening of Retinal Anomalies in OCT Images: A Deep Learning Approach.
Hadi Chakor , Waziha Kabir, Riadh Kobbi, Jihed Chelbi, Marc-AndréRacine , Julio Silva-Rodríguez,Balamurali Murugesan ,Jose Dolz andIsmail Ben Ayed . - All that glitters is not gold: are current retina foundation models able to efficiently detect hypertensive retinopathy? Julio Silva-Rodríguez,
Hadi Chakor , Riadh Kobbi,Balamurali Murugesan , Waziha Kabir, Jihed Chelbi, Marc-AndréRacine ,Jose Dolz andIsmail Ben Ayed - Domain generalization for diabetic retinopathy grading through vision-language foundation models.
Balamurali Murugesan , Julio Silva-Rodríguez,Hadi Chakor , Riadh Kobbi, Waziha Kabir, Jihed Chelbi, Marc-AndréRacine ,Jose Dolz andIsmail Ben Ayed .
Program link: https://eppro02.ativ.me/src/EventPilot/php/express/web/planner.php?id=ARVO24
About
Additional information is available at www.diagnos.com and www.sedar.com
Neither the
This news release contains forward-looking information. There can be no assurance that forward-looking information will prove to be accurate, as actual results and future events could differ materially from those anticipated in these statements.
For further information, please contact: Mr.André Larente , PresidentDIAGNOS Inc. Tel: 450-678-8882 ext. 224 alarente@diagnos.com
Source:
2024 GlobeNewswire, Inc., source