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A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
Published on: July 5, 2022
CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review
Katie Curran1, Noelle Whitestone2, Bedowra Zabeen3
1Centre for Public Health, Queens University Belfast, Belfast, UK.
Insights
Artificial intelligence (AI) accurately detected diabetic retinopathy (DR) in children and young adults. This AI tool shows promise for managing diabetes care in underserved regions.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Diabetology
Background:
- Diabetic retinopathy (DR) detection accuracy in adults using AI is high.
- Limited studies exist on AI's DR detection capabilities in pediatric populations.
Purpose of the Study:
- To evaluate the diagnostic performance of Cybersight AI for detecting diabetic retinopathy in children and young adults.
- To compare AI's DR detection accuracy against a certified optometrist as the reference standard.
Main Methods:
- Screened 1274 children and young adults (3-26 years) with type 1 or type 2 diabetes mellitus.
- Utilized Cybersight AI for grading fundus images and compared results with a human expert.
- Assessed diagnostic test performance using metrics like sensitivity, specificity, and AUC-ROC.
Main Results:
- AI detected any DR in 19.4% and referable DR in 2.35% of participants.
- High sensitivity and specificity were observed for both any DR and referable DR detection.
- AI demonstrated strong performance, particularly in younger children with shorter diabetes duration.
Conclusions:
- Cybersight AI accurately detects diabetic retinopathy in pediatric populations, even with adult-trained algorithms.
- The AI's high specificity is crucial for preventing unnecessary referrals in resource-limited settings.
- AI can potentially alleviate the burden on limited physician resources for pediatric diabetes care.
Background:
Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults.
Methods:
Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values.
Results:
Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes.
Conclusions:
Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.
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