Revista medica de Chile · 2025
Amending AI Software Accuracy for Diabetic Retinopathy Detection Using Conditional Probability and the Appropriate Reference Standard.
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Segovia Christian, Salinas-Toro Daniela, Moraga Carla, Sepúlveda Maylyn
Аннотация
AIM: To evaluate and correct the reported sensitivity and specificity values of DART (Diagnóstico Automatizado de Retinografías Telemáticas), an automated Artificial Intelligence based (AI-based) screening tool used for diabetic retinopathy (DR) detection in the Chilean public healthcare system, by employing the appropriate gold standard and conditional probability.
METHODS: Data were obtained from the clinical validation of DART. DR detection capabilities were assessed for three methods 1) Fundoscopy, 2) Retinography and 3) AI- DART. To estimate the true sensitivity and specificity of DART, conditional probability was applied using three hypothetical sensitivities level for method 2: A) Optimistic (90%), B) Moderate (80%), and C) Pessimistic (70%). Based on these scenarios, corrected sensitivity and specificity values for DART were calculated, along with false negative/positive rates (%FN/%FP), and predictive values (NPV/PPV).
RESULTS: In all scenarios, corrected sensitivity and specificity values for DART were significantly lower than those reported in the original validation study. Compared to method 3 (AI-based), method 2 (retinography by and ophthalmologist) consistently demonstrated superior performance across all metrics, including FN%, FP%, NPV and PPV values.
CONCLUSION: While the integration of new AI-based technologies like DART in healthcare offer promise for enhancing patient care, their implementation must be preceded by validation using the correct gold standard. Reliable clinical decision-making depends on trustworthy diagnostic parameters.
METHODS: Data were obtained from the clinical validation of DART. DR detection capabilities were assessed for three methods 1) Fundoscopy, 2) Retinography and 3) AI- DART. To estimate the true sensitivity and specificity of DART, conditional probability was applied using three hypothetical sensitivities level for method 2: A) Optimistic (90%), B) Moderate (80%), and C) Pessimistic (70%). Based on these scenarios, corrected sensitivity and specificity values for DART were calculated, along with false negative/positive rates (%FN/%FP), and predictive values (NPV/PPV).
RESULTS: In all scenarios, corrected sensitivity and specificity values for DART were significantly lower than those reported in the original validation study. Compared to method 3 (AI-based), method 2 (retinography by and ophthalmologist) consistently demonstrated superior performance across all metrics, including FN%, FP%, NPV and PPV values.
CONCLUSION: While the integration of new AI-based technologies like DART in healthcare offer promise for enhancing patient care, their implementation must be preceded by validation using the correct gold standard. Reliable clinical decision-making depends on trustworthy diagnostic parameters.