A study published in the journal PLOS Digital Health demonstrates the accuracy of an artificial intelligence system developed at USP in Ribeirão Preto for identifying teeth and detecting cavities in x-rays. Approximately 30 researchers from the Faculty of Philosophy, Sciences and Languages at Ribeirão Preto and the School of Dentistry of Ribeirão Preto are collaborating within the Interdisciplinary Research Group in Digital Dentistry (InReDD) on the project. According to professor Alessandra Alaniz Macedo, the technology is based on Convolutional Neural Networks (CNNs), a type of artificial intelligence specialized in image processing; she explained that the system aims to streamline diagnosis and treatment by assisting dentists in radiographic analysis. Convolutional Neural Networks Detect Cavities in Dental X-rays The University of São Paulo’s Ribeirão Preto campus has developed an artificial intelligence system capable of identifying cavities in dental x-rays with a high degree of accuracy, streamlining diagnostic workflows for dentists. This capability stems from the implementation of Convolutional Neural Networks, or CNNs, a specific type of artificial intelligence architecture designed for image processing and pattern recognition. According to professor Alessandra Alaniz Macedo, “Within artificial intelligence, different methods are used to analyze different types of data. Neural networks are one of these approaches and currently produce the best results for various types of problems.” Training the AI to reliably detect cavities requires a supervised learning process where the system is fed thousands of previously analyzed radiographs. Researchers create a dataset, essentially teaching the artificial intelligence to correlate specific image characteristics with the presence of lesions. The model then iteratively compares its own analysis of new radiographs against this established ground truth, correcting errors and refining its accuracy with each iteration. Researchers explained that the model continuously compares its output with the ground truth and corrects its own errors during training, gradually learning to