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ISSN 2063-5346
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DIGITAL DECISION MAKING WITH ARTIFICIAL INTELIGENCE: A CNN BASED PREDICTION OF MALOCCLUSION USING DEEP LEARNING IN DENTISTRY

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Lakshmi T.K. , Dheeba J
» doi: 10.31838/ecb/2023.12.sa1.079

Abstract

In every medical and allied sector, treatment planning, management and chair side efficiency is very crucial where decision making comes to scenario. In this current digital era, computer assisted decision making is slowly replacing the manual process supporting the medical / dental fields for effective and efficient utilization of computer resources and technologies. Artificial Intelligence and virtual reality in health care has been giving successful outcomes where few of the manual procedures has been made completely automated. Now- a – days Artificial intelligence is predominantly used in almost all the medical applications for disease detection, identification, diagnosis, pre & post treatment planning, patient management, computer assisted surgeries and many more. The current paper focusses on the dentistry – which focusses on oral diseases. Proper tooth alignment is very important when concerned with aesthetics and beautiful smile. Generally people will face the problem of irregularities on the teeth and jaws which spoils the aesthetics of the face during smile. The problem of misalignment is common now a days and there are treatments to correct the alignment where the specialization in dentistry, orthodontics comes in to scenario. In the current paper, malocclusion problem from orthodontics is addressed using Artificial Intelligence, deep learning model named Convolutional neural networks (CNN). The problem of Periodontitis related to gum disease depends on bacterial accumulation that may also occurs because of malocclusion. Therefore the current paper focuses on detection / classification of malocclusion. The input dataset is the RGB images of the patients’ tooth suffering from malocclusion and normally aligned tooth images. CNN is used to classify normal images from malocclusion images and encouraging results are obtained with the prediction accuracy of 98.95 %. Therefore deep learning technologies can be used for prediction of malocclusion, to design aligners during correction of tooth alignment, in prediction of whether tooth extraction is necessary or not thereby supporting orthodontists in effective decision making and proper treatment planning.

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