Abstract: TH-PO0587
Deep Learning-Based Screening of Alport Syndrome Using Retinal Optical Coherence Tomography Images
Session Information
- Monogenic Kidney Diseases: Glomerular
November 06, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1201 Genetic Diseases of the Kidneys: Monogenic Kidney Diseases
Author
- Dai, Xuantong, Shanghai Jiao Tong University School of Medicine Affiliated Xinhua Hospital, Shanghai, China
Background
Prior studies indicate that temporal retinal thinning demonstrated by optical coherence tomography (OCT) is associated with Alport syndrome (AS). We aim to develop a deep-learning (DL) algorithm (termed DEEP-ASK) to differentiate between retinal images of individuals with AS vs other causes of glomerular hematuria.
Methods
Data sets of three-dimensional (3D) OCT scans were collected from patients in the Shanghai Registry of Alport syndrome. The structure of the DEEP-ASK network is constructed upon a contrastive DL model pre-trained on 2D scans and a transformer framework. The area under the curve (AUC) was calculated with the probabilities for overall prediction results of the DEEP-ASK model.
Results
21120 OCT scans images from 93 AS patients (mean age 33.44±15.80), along with age- and sex-matched control participants (48 glomerulonephritis patients with primary cause and 24 normal subjects) collected between June 2024 to May 2025 were included. The DEEP-ASK model achieved a mean AUC of 0.95 for AS diagnosis, outperforming that of the temporal retinal thickness index (0.63).
Conclusion
Our preliminary results support the potential value of DEEP-ASK model in noninvasive screening and early diagnosis of AS.
Figure:Overall study design of the development and validation of the DEEP-ASK system
Funding
- Clinical Revenue Support