Heatmap analysis for artificial intelligence explainability in diabetic retinopathy detection: illuminating the rationale of deep learning decisions.

Background: The opaqueness of artificial intelligence (AI) algorithms decision processes limit their application in healthcare. Our objective was to explore discrepancies in heatmaps originated from slightly different retinal images from the same eyes of individuals with diabetes, to gain insights into the deep learning (DL) decision process.

Methods: Pairs of retinal images from the same eyes of individuals with diabetes, composed of images obtained before and after pupil dilation, underwent automatic analysis by a convolutional neural network for the presence of diabetic retinopathy (DR), output being a score ranging from 0 to 1. Gradient-based Class Activation Maps (GradCam) allowed visualization of activated areas. Pairs of images with discordant DL scores or outputs within the pair were objectively compared to the concordant pairs, regarding the sum of activations of Class Activation Mapping (CAM), the number of activated areas, and DL score differences. Heatmaps of discordant pairs were also qualitatively assessed.

Results: Algorithmic performance for the detection of DR attained 89.8% sensitivity, 96.3% specificity and area under the receiver operating characteristic (ROC) curve of 0.95. Out of 210 comparable pairs of images, 20 eyes and 10 eyes were considered discordant according to DL score difference and regarding DL output, respectively. Comparison of concordant versus discordant groups showed statistically significant differences for all objective variables. Qualitative analysis pointed to subtle differences in image quality within discordant pairs.

Conclusions: The successfully established relationship among objective parameters extracted from heatmaps and DL output discrepancies reinforces the role of heatmaps for DL explainability, fostering acceptance of DL systems for clinical use.