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Learning about diagnostic features and related clinical information fromdental radiographs is important for dental research. However, the lack ofexpert-annotated data and convenient search tools poses challenges. Our primaryobjective is to design a search tool that uses a user's query for oral-relatedresearch. The proposed framework, Contrastive LAnguage Image REtrieval Searchfor dental research, Dental CLAIRES, utilizes periapical radiographs andassociated clinical details such as periodontal diagnosis, demographicinformation to retrieve the best-matched images based on the text query. Weapplied a contrastive representation learning method to find images describedby the user's text by maximizing the similarity score of positive pairs (truepairs) and minimizing the score of negative pairs (random pairs). Our modelachieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We alsodesigned a graphical user interface that allows researchers to verify themodel's performance with interactions.

Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research