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Can a computer identify carious lesions in dental x-rays as accurately as humans?

To ascertain whether parity between human and computer diagnostic abilities has really been achieved, a pilot study was conducted comparing the performance of three experienced dental radiograph readers with a computer vision/machine learning (CV/ML) system for identifying caries. The human and digital analysts annotated a sample of more than 10,000 dental X-rays, scoring them for the presence or absence of caries. The study compared levels of diagnostic agreement among the human readers and compared those with the computational results from the CV/ML system. The results showed that the CV/ML system was better at predicting the existence of caries on the basis of radiographic images than the human readers.

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