PT - JOURNAL ARTICLE AU - Zhiyan Xu AU - Weisen Wang AU - Jingyuan Yang AU - Jianchun Zhao AU - Dayong Ding AU - Feng He AU - Di Chen AU - Zhikun Yang AU - Xirong Li AU - Weihong Yu AU - Youxin Chen TI - Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks AID - 10.1136/bjophthalmol-2020-315817 DP - 2021 Apr 01 TA - British Journal of Ophthalmology PG - 561--566 VI - 105 IP - 4 4099 - http://bjo.bmj.com/content/105/4/561.short 4100 - http://bjo.bmj.com/content/105/4/561.full SO - Br J Ophthalmol2021 Apr 01; 105 AB - Aims To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images.Methods A retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset.Results On a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen’s κ 0.828), exceeds slightly over the best expert (Human1, Cohen’s κ 0.810). For recognising PCV, the model outperformed the best expert as well.Conclusion A bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.