@article {1330440,
	title = {Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs},
	journal = {Diagnostics},
	volume = {10},
	year = {2020},
	pages = {430},
	abstract = {Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69 ({\textpm}0.13), indicating that dentists on average falsely diagnose 31\% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51 ({\textpm}0.14), indicating that on average 49\% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60 ({\textpm}0.04), and an F<sub>1</sub>\&nbsp;score of 0.58 ({\textpm}0.04) corresponding to a PPV of 0.67 ({\textpm}0.05) and TPR of 0.51 ({\textpm}0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.\&nbsp;},
	url = { https://www.mdpi.com/2075-4418/10/6/430},
	author = {Endres, Michael G. and Hillen, Florian and Salloumis, Marios and Sedaghat, Ahmad R. and Stefan M. and Quatela, Olivia and Hanken, Henning and Smeets, Ralf and Beck-Broichsitter, Benedicta and Rendenback, Carsten and Lakhani, Karim R. and Heiland, Max and Gaudin, Robert}
}
