An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs

BMC Medical Imaging

Özer Çelik¹  İbrahim Şevki Bayrakdar¹  Ingrid Rozylo-Kalinowska²  Ahmet Faruk Aslan³  Alper Odabaş³  Kaan Orhan⁴  Elif Bilgir⁵  Hande Sağlam⁵  Fatma Akkoca⁵  Musa Kıllı⁶  Cemre Ozcetin⁷ 

[1] Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir, Turkey;Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, ul. Doktora Witolda Chodźki 6, 20-093, Lublin, Poland;Department of Mathematics-Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey;Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey;Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey;Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey;Private Practice, Eskisehir, Turkey;

关键词: Artificial intelligence;    Deep learning;    Tooth;    Panoramic radiography;   

DOI  :  10.1186/s12880-021-00656-7

来源: Springer

【 摘 要 】

BackgroundPanoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs.MethodsThe data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix.ResultsThe total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively.ConclusionsThe deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making.

【 授权许可】

CC BY