Developing a continuous severity scale for MacTel type 2 using Deep Learning and implications for disease grading.

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Abstract

Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for Macular Telangiectasia Type II (MacTel) by combining a DL classification model with Uniform Manifold Approximation and Projection (UMAP).We used a DL network to learn a feature representation of MacTel severity from discrete severity labels, and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale.A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants.We trained a multi-view DL classifier using multiple B-scans from OCT volumes to learn the discrete 7-step Chew et al. MacTel severity scale. The classifiers’ last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2D manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against Chew et al. was calculated. Additionally, the UMAP scale was assessed in the kappa agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease, and compared against the UMAP scale.Classification accuracy for the DL classifier, and kappa agreement vs clinical experts for UMAP.The multi-view DL classifier achieved top-1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric shows a clear continuous gradation of MacTel severity that has a Spearman Rank Correlation of 0.84 with the Chew et al. scale. Furthermore, the continuous UMAP metric achieved kappa agreements of 0.56-0.63 with 5 clinical experts, which was comparable to inter-observer kappas.Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases, and may lead to more accurate diagnosis, improved understanding of disease progression and key imaging features for pathologies.Copyright © 2023 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.