Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Machine learning in dental, oral and craniofacial imaging: a review of recent progress. Advances in Clinical and Experimental Medicine: Official Organ Wroclaw Medical University. Machine learning in orthodontics: challenges and perspectives. We conclude that deep learning can improve the accuracy, speed, and efficiency of classification, archiving, and monitoring of orthodontic images. Furthermore, human experts with deep learning assistance required an average of 8.10 min to classify images in the external dataset, much shorter than 18.93 min. This was 236 times faster than a human expert (18.93 min). Our model automatically classified orthodontic images in an external dataset with an accuracy of 0.994 and macro area under the curve of 1.00 in 0.08 min. This evaluation was performed using a dataset of >14,000 images encompassing all 14 categories of orthodontic images. We comprehensively evaluated the ability of a deep learning model based on Deep hidden IDentity (DeepID) features to classify and archive photographs and radiographs. This study is aimed at developing an effective artificial intelligence tool for the automated classification and monitoring of orthodontic images. However, the conventional system of orthodontic image acquisition, which includes manual classification, archiving, and monitoring, is time-consuming and prone to errors caused by fatigue. One of the main requirements for orthodontic treatment is continuous image acquisition.

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