According to FDI World Dental Federation, the definition of oral health is “multi-faceted and includes the ability to speak, smile, smell, taste, touch, chew, swallow and convey a range of emotions through facial expressions with confidence and without pain, discomfort and disease of the craniofacial complex” (World Dental Federation 2016). Hence there is a close relationship between malocclusion in orthodontics and oral health. Malocclusion can affect oral health by increasing the incidence of cavities, periodontitis, increasing the risk of injury and difficulty chewing, swallowing, breathing and speaking, not only that, it also affects aesthetics, self-image, communication and even career advancement. In this study, we used the Index of Orthodontic Treatment Need (IOTN) - the rating system used to assess the need of orthodontic treatment on dental health grounds of UK National Health Service (NHS) to develop the classification criteria for orthodontic malocclusion, based on analysis and labelling of 2510 intraoral images (follow IMI National Guidelines Orthodontic Photography) of 502 patients aged between 12 and 30 years old had completed permanent teeth growth who visited for orthodontic check-up in Hanoi. The research process of extracting essential features from intraoral images is presented in this study. The malocclusion levels of each feature are also labelled with the values based on extension theory. Then we used Extension Neural Network which combines extension theory and neural network to help the process of classifying the Orthodontic treatment needs through intraoral images faster and more accurately, saving time for the orthodontists. The automatic imaging diagnosis is becoming a promising diagnostic method for orthodontics. It can provide important and objective diagnostics for both the physician and the patient and directly affects the determination of the need for treatment and insurance coverage, reduce the time and workload of the clinical check-up.

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