Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.

This includes a summary of legacy machine learning methods, a description of the capabilities and functions of deep convolutional neural networks …

An increasing number of studies on artificial intelligence (AI) are published in the dental and oral sciences but aspects of these studies suffer from a range of limitations. Standards towards reporting, like the recently published CONSORT-AI extension, can help to improve studies in this emerging field. Watch authors Falk Schwendicke and Joachim Krois of the Charité - Universitätsmedizin Berlin, Germany, discuss the Journal of Dental Research (JDR) article "Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond," moderated by JDR Editor-in-Chief Nicholas Jakubovics, Newcastle University, England. For AI studies in healthcare, only a limited number of randomized controlled trials are available, many studies are low quality and reporting is often insufficient to fully comprehend and possibly replicate these studies. Reporting standards such as the CONSORT (Consolidated Standards of Reporting Trials) statement, which provides evidence-based recommendations for reporting of randomized controlled trials, have been widely adopted by journals and have been shown to increase reporting quality.

An increasing number of studies on artificial intelligence (AI) are published in the dental and oral sciences but aspects of these studies suffer from a range of limitations. Standards towards reporting, like the recently published CONSORT-AI extension, can help to improve studies in this emerging field. Watch authors Falk Schwendicke and Joachim Krois of the Charité - Universitätsmedizin Berlin, Germany, discuss the Journal of Dental Research (JDR) article "Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond," moderated by JDR Editor-in-Chief Nicholas Jakubovics, Newcastle University, England. For AI studies in healthcare, only a limited number of randomized controlled trials are available, many studies are low quality and reporting is often insufficient to fully comprehend and possibly replicate these studies. Reporting standards such as the CONSORT (Consolidated Standards of Reporting Trials) statement, which provides evidence-based recommendations for reporting of randomized controlled trials, have been widely adopted by journals and have been shown to increase reporting quality.