Publications by authors named "N Jaccard"
CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review.
Clin Med Insights Endocrinol Diabetes
October 2023
Article Synopsis
- AI has shown high accuracy in detecting diabetic retinopathy (DR) in children and young adults with diabetes, highlighting the need for more research in this area.
- A study conducted on 1,274 participants aged 3-26 found that 19.4% had any DR, with 2.35% classified as referable DR by AI.
- The performance metrics indicated that AI had strong sensitivity and specificity rates, especially in younger children with less diabetes duration, suggesting it could be a valuable resource in low-resource settings for screening purposes.
AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.
IEEE Trans Med Imaging
January 2024
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images.
View Article and Find Full Text PDFBackground: Evidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.
Methods: Consented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to June 2021 at four diabetes clinics in Rwanda. Additionally, images were graded by a UK National Health System-certified retinal image grader.
Purpose: This trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda.
Design: The Rwanda Artificial Intelligence for Diabetic Retinopathy Screening (RAIDERS) study was an investigator-masked, parallel-group randomized controlled trial.
Participants: Patients ≥ 18 years of age with known diabetes who required referral for DR based on AI interpretation.
Glaucomatous optic neuropathy is the leading cause of irreversible blindness worldwide. Diagnosis and monitoring of disease involves integrating information from the clinical examination with subjective data from visual field testing and objective biometric data that includes pachymetry, corneal hysteresis, and optic nerve and retinal imaging. This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error.
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