A high performance infrastructure for a deep learning-based orthodontic treatment in dentistry

Chonho Lee (Osaka Univ., Cybermedia Center)

The talk presents an ongoing project to develop a deep learning-based diagnostic system for orthodontic treatment in dentistry. We aim to automate medical tasks such as diagnostic imaging, morphological feature extraction, casenote generation and treatment recommendation, etc. Due to a large amount of heterogeneous dataset including images (facial/oral photo, X-rays) and texts (doctor’s note), doctors struggle against temporal and accuracy limitations when processing and analyzing those data using conventional machines and approaches. DL techniques supported by high performance computing infrastructure remove those limitations and help find unseen healthcare insights. We evaluate the practical use of DL models in medical front and show its effectiveness. The developed system dramatically reduces doctor’s workload and is smoothly expanded to other departments such as otolaryngology and ophthalmology.