Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy
Journal
International journal of environmental research and public health
Journal Volume
19
Journal Issue
3
Date Issued
2022-01-21
Author(s)
Tsai, Ching-Yao
Chen, Chueh-Tan
Chen, Guan-An
Yeh, Chun-Fu
Kuo, Chin-Tzu
Hsiao, Ya-Chuan
Hu, Hsiao-Yun
Tsai, I-Lun
Wang, Ching-Hui
Chen, Jian-Ren
Huang, Su-Chen
Lu, Tzu-Chieh
Abstract
Deep learning (DL) algorithms are used to diagnose diabetic retinopathy (DR). However, most of these algorithms have been trained using global data or data from patients of a single region. Using different model architectures (e.g., Inception-v3, ResNet101, and DenseNet121), we assessed the necessity of modifying the algorithms for universal society screening. We used the open-source dataset from the Kaggle Diabetic Retinopathy Detection competition to develop a model for the detection of DR severity. We used a local dataset from Taipei City Hospital to verify the necessity of model localization and validated the three aforementioned models with local datasets. The experimental results revealed that Inception-v3 outperformed ResNet101 and DenseNet121 with a foreign global dataset, whereas DenseNet121 outperformed Inception-v3 and ResNet101 with the local dataset. The quadratic weighted kappa score (κ) was used to evaluate the model performance. All models had 5-8% higher κ for the local dataset than for the foreign dataset. Confusion matrix analysis revealed that, compared with the local ophthalmologists' diagnoses, the severity predicted by the three models was overestimated. Thus, DL algorithms using artificial intelligence based on global data must be locally modified to ensure the applicability of a well-trained model to make diagnoses in clinical environments.
Subjects
Taiwan; deep learning algorithms; diabetic retinopathy; model localised; predict
Type
journal article