Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography
Issued Date
2022-10-01
Resource Type
eISSN
21642591
Scopus ID
2-s2.0-85139514843
Pubmed ID
36219163
Journal Title
Translational Vision Science and Technology
Volume
11
Issue
10
Rights Holder(s)
SCOPUS
Bibliographic Citation
Translational Vision Science and Technology Vol.11 No.10 (2022)
Suggested Citation
Wongchaisuwat P., Thamphithak R., Jitpukdee P., Wongchaisuwat N. Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography. Translational Vision Science and Technology Vol.11 No.10 (2022). doi:10.1167/tvst.11.10.16 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/85486
Title
Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography
Author's Affiliation
Other Contributor(s)
Abstract
Objective: To develop an automated polypoidal choroidal vasculopathy (PCV) screening model to distinguish PCV from wet age-related macular degeneration (wet AMD). Methods: A retrospective review of spectral domain optical coherence tomography (SD-OCT) images was undertaken. The included SD-OCT images were classified into two distinct categories (PCV or wet AMD) prior to the development of the PCV screening model. The automated detection of PCV using the developed model was compared with the results of gold-standard fundus fluorescein angiography and indocyanine green (FFA + ICG) angiography. A framework of SHapley Additive exPlanations was used to interpret the results from the model. Results: A total of 2334 SD-OCT images were enrolled for training purposes, and an additional 1171 SD-OCT images were used for external validation. The ResNet attention model yielded superior performance with average area under the curve values of 0.8 and 0.81 for the training and external validation data sets, respectively. The sensitivity/specificity calculated at a patient level was 100%/60% and 85%/71% for the training and external validation data sets, respectively. Conclusions: A conventional FFA + ICG investigation to differentiate PCV from wet AMD requires intense health care resources and adversely affects patients. A deep learning algorithm is proposed to automatically distinguish PCV from wet AMD. The developed algorithm exhibited promising performance for further development into an alternative PCV screening tool. Enhancement of the model’s performance with additional data is needed prior to implementation of this diagnostic tool in real-world clinical practice. The invisibility of disease signs within SD-OCT images is the main limitation of the proposed model. Translational Relevance: Basic research of deep learning algorithms was applied to differentiate PCV from wet AMD based on OCT images, benefiting a diagnosis process and minimizing a risk of ICG angiogram.