Multi-modal Deep Learning and Its Applications in Ophthalmic Artificial Intelligence

Abstract

Deep learning, for its powerful learning capability and high usability, has been a prevalent algorithm of machine learning and a core technique for artificial intelligence(AI) in medicine and healthcare. Due to the importance of medical imaging in many tasks such as health screening, disease diagnosis, precise treatment, and prognosis prediction, deep learning of structural analysis and semantic understanding for medical images is becoming an important interdisciplinary research direction. In clinical scenarios, in order to achieve a more accurate diagnosis, doctors need to simultaneously refer to multiple modalities of medical imaging for a comprehensive analysis and judgment. This article introduced the basic concepts and working principles of multimodal deep learning in such scenarios, reviewed recent research progress on applying multi-modal deep learning in both generic medical fields and ophthalmology, and discussed technical challenges and also envision potential applications of multi-modal deep learning in AI-assisted ophthalmology.

Keywords

• multi-modal deep learning
• ophthalmology
• artificial intelligence
• assisted diagnosis