Glaucoma detection based on optical coherence tomography imaging - Li, Chi (Nanyang Technological University, 2023)
The thesis evaluates the machine learning (ML) and deep learning (DL) approaches’ performance in accurately detecting glaucoma based on optical coherence tomography tabular data and images from individuals of different ethnicities. While numerous studies have employed ML and DL techniques for glaucoma identification, their performance has not been evaluated across diverse ethnic groups. In addition, a DL approach utilizing the Swin Transformer architecture trained on the thickness map images of the retinal nerve fiber layer (RNFL) was also evaluated. This Swin transformer DL model demonstrated an AUC of 0.97 in the internal testing dataset (Asian) and 0.88 in the external testing dataset (Caucasian). However, like the ML classifiers trained on measured data, the DL approach which was trained on raw thickness map images also exhibited poor reproducibility across different datasets. To address these issues, a cross-sectional study design was employed to investigate both ML and DL’s model performance in glaucoma detection using OCT data from individuals of different ethnicities. The study included 514 Asian participants, consisting of 257 with glaucoma and 257 controls, to develop ML and DL classifiers. The trained classifiers were subsequently evaluated on two separate participant groups comprising 356 Asians and 138 Caucasians. Two machine learning classifiers were created using the two types of RNFL thickness, one using the original values extracted from OCT machines (measured RNFL), and the other generated from the compensation model. The compensation model is a multivariate regression trained on normal individuals. It corrects the 12-clock RNFL thicknesses for multiple demographic and anatomical parameters. Additionally, a deep learning model was developed using the Swin Transformer architecture based on the measured RNFL thickness map images from OCT. Explainable artificial intelligence techniques (CAM and SHAP) were utilized to better interpret the results. Performance metrics such as the area under the receiver operating characteristic curve (AUC), accuracy and sensitivity were employed to examine the effectiveness of different glaucoma detection models. Both machine learning (AUC = 0.96) and deep learning (AUC = 0.97) models demonstrated superior performance compared to the raw measured data (baseline, AUC = 0.93), in the internal testing dataset (Asian). However, in the external testing dataset (Caucasian), ML models utilizing the compensated data (AUC = 0.93) exhibited significantly better performance compared to ML models using the original measured data (AUC = 0.83) and the baseline (AUC = 0.82). Furthermore, the ML and DL models trained on measured data exhibited inadequate generalization ability across different ethnicities, whereas the ML model using the compensated data maintained its performance in the external testing dataset. These findings caution against the indiscriminate application of ML and DL models to patient cohorts of different ethnicities. They also suggest that incorporating the compensation model into the development of ML models may enhance their performance in glaucoma detection across diverse ethnicities. Overall, the study highlights the importance of accounting for anatomical variations across different ethnic groups when developing machine-learning models for glaucoma detection using OCT data.