CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review.
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 diabet...
| Publicado en: | Clinical Medicine Insights: Endocrinology & Diabetes pp. 1 - 9 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
10/9/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172896614&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172896614 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795514 B3KQ jtl: Clinical Medicine Insights: Endocrinology & Diabetes issn: 11795514 maglogo: Y pubinfo: dt: 10/9/2023 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 172896614 172896614 172896614 10.1177/11795514231203867 172896614 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review. aug: au: Curran, Katie Whitestone, Noelle Zabeen, Bedowra Ahmed, Munir Husain, Lutful Alauddin, Mohammed Hossain, Mohammad Awlad Patnaik, Jennifer L Lanoutee, Gabriella Cherwek, David Hunter Congdon, Nathan Peto, Tunde Jaccard, Nicolas affil: Centre for Public Health, Queens University Belfast, Belfast, UK sug: subj: Diabetic Retinopathy Diagnosis Diabetic Retinopathy Diagnosis Diabetic Retinopathy Risk Factors Diabetes Mellitus, Type 1 Complications Diabetes Mellitus, Type 2 Complications Artificial Intelligence Methods Health Screening Methods Risk Assessment Human Male Female Child, Preschool Child Adolescence Adult ROC Curve Machine Learning Sensitivity and Specificity Predictive Value of Tests Confidence Intervals Diabetes Mellitus, Type 1 Diabetes Mellitus, Type 2 Data Analysis Software kappa Statistic Univariate Statistics Multivariate Analysis Multiple Logistic Regression Descriptive Statistics Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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