To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image-level and performing an explainable macular disease diagnosis at eye-level in optical coherence tomography (OCT) images.26,815 OCT images were collected from 865 eyes, and 9 retinal lesions and 3 macular diseases were labelled by ophthalmologists, including diabetic macular edema (DME) and dry/wet age-related macular degeneration (dry/wet AMD). We applied deep learning to classify retinal lesion at image-level and random forests to achieve an explainable disease diagnosis at eye-level. The performance of the integrated two-stage framework was evaluated and compared with human experts.On testing dataset of 2,480 OCT images from 80 eyes, deep learning model achieved average Area Under Curve (AUC) of 0.978 (95% CI, 0.971-0.983) for lesion classification. And random forests performed accurate disease diagnosis with 0% error rate, which achieved the same accuracy as one of human experts and was better than the other 3 experts. It also revealed that the detection of specific lesions in the center of macular region had more contribution to macular disease diagnosis.The integrated method achieved high accuracy and interpretability in retinal lesion classification and macular disease diagnosis in OCT images, and could have the potential to facilitate the clinical diagnosis.Copyright © 2021 by Ophthalmic Communications Society, Inc.