Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17-19 Years) Candidates to Airforce Cadets

Abstract

Purpose: Artificial intelligence (AI)-assisted ultra-widefield (UWF) fundus photographic interpretation is beneficial to improve the screening of fundus abnormalities. Therefore we constructed an AI machine-learning approach and performed preliminary training and validation.

Methods: We proposed a two-stage deep learning-based framework to detect early retinal peripheral degeneration using UWF images from the Chinese Air Force cadets' medical selection between February 2016 and June 2022. We developed a detection model for the localization of optic disc and macula, which are used to find the peripheral areas. Then we developed six classification models for the screening of various retinal cases. We also compared our proposed framework with two baseline models reported in the literature. The performance of the screening models was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval.

Results: A total of 3911 UWF fundus images were used to develop the deep learning model. The external validation included 760 UWF fundus images. The results of comparison study revealed that our proposed framework achieved competitive performance compared to existing baselines while also demonstrating significantly faster inference time. The developed classification models achieved an average AUC of 0.879 on six different retinal cases in the external validation dataset.

Conclusions: Our two-stage deep learning-based framework improved the machine learning efficiency of the AI model for fundus images with high resolution and many interference factors by maximizing the retention of valid information and compressing the image file size.

Translational relevance: This machine learning model may become a new paradigm for developing UWF fundus photography AI-assisted diagnosis.

Figure 1.

Schematic diagram of label setting. (A) A typical example for label “facula”, which was defined as a block highlight area. (B) A typical example for label “degeneration”. (C) A typical example for label “hyperpigmentation”. (D) A typical example for label “WWOP”.

Figure 2.

Study framework of the proposed methods for the detection. OD, optic disc; MA, macular.

Figure 3.

The comparisons of Resizing method's, Path-based method's and Edge-sensitive method's (our proposed method's) AUROC curves with 6 cases classification on the external validation dataset.

Figure 4.

The Grad-CAM visualization results and corresponding ground truth for the detection of retinal peripheral lesions. In this study, Grad-CAM is applied for the visualization analysis.