A novel deep learning-based perspective for tooth numbering and caries detection

被引:15
作者
Ayhan, Baturalp [1 ]
Ayan, Enes [2 ]
Bayraktar, Yusuf [1 ]
机构
[1] Kirikkale Univ, Fac Dent, Dept Restorat Dent, Kirikkale, Turkiye
[2] Kirikkale Univ, Fac Engn & Architecture, Dept Comp Engn, Kirikkale, Turkiye
关键词
Artificial intelligence; Digital bitewing radiography; Deep learning; Detection; Numbering; Dental caries; CONVOLUTIONAL NEURAL-NETWORK; BITE-WING RADIOGRAPHY; PROXIMAL CARIES; DENTAL-CARIES; OCCLUSAL CARIES; TEETH; CLASSIFICATION; DIAGNOSIS; ACCURACY; FILM;
D O I
10.1007/s00784-024-05566-w
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
ObjectivesThe aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms.MethodsThe dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process.ResultsAccording to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1-Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively.ConclusionsThe proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently.Clinical significanceCNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.
引用
收藏
页数:17
相关论文
共 76 条
[1]  
Abdinian Mehrdad, 2015, J Dent (Tehran), V12, P290
[2]   Dental caries paradigms in diagnosis and diagnostic research [J].
Baelum, Vibeke ;
Heidmann, Jens ;
Nyvad, Bente .
EUROPEAN JOURNAL OF ORAL SCIENCES, 2006, 114 (04) :263-277
[3]   What is an appropriate caries diagnosis? [J].
Baelum, Vibeke .
ACTA ODONTOLOGICA SCANDINAVICA, 2010, 68 (02) :65-79
[4]  
Barr A, 1981, Artificial Intelligence, P3
[5]   Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs [J].
Bayraktar, Yusuf ;
Ayan, Enes .
CLINICAL ORAL INVESTIGATIONS, 2022, 26 (01) :623-632
[6]   An artificial intelligence approach to automatic tooth detection and numbering in panoramic radiographs [J].
Bilgir, Elif ;
Bayrakdar, Ibrahim Sevki ;
Celik, Ozer ;
Orhan, Kaan ;
Akkoca, Fatma ;
Saglam, Hande ;
Odabas, Alper ;
Aslan, Ahmet Faruk ;
Ozcetin, Cemre ;
Killi, Musa ;
Rozylo-Kalinowska, Ingrid .
BMC MEDICAL IMAGING, 2021, 21 (01)
[7]  
Bradski G, 2000, DR DOBBS J, V25, P120
[8]   Detecting caries lesions of different radiographic extension on bitewings using deep learning [J].
Cantu, Anselmo Garcia ;
Gehrung, Sascha ;
Krois, Joachim ;
Chaurasia, Akhilanand ;
Rossi, Jesus Gomez ;
Gaudin, Robert ;
Elhennawy, Karim ;
Schwendicke, Falk .
JOURNAL OF DENTISTRY, 2020, 100
[9]   Caries Detection with Near-Infrared Transillumination Using Deep Learning [J].
Casalegno, F. ;
Newton, T. ;
Daher, R. ;
Abdelaziz, M. ;
Lodi-Rizzini, A. ;
Schuermann, F. ;
Krejci, I ;
Markram, H. .
JOURNAL OF DENTAL RESEARCH, 2019, 98 (11) :1227-1233
[10]   Accuracy of extraoral bite-wing radiography in detecting proximal caries and crestal bone loss [J].
Chan, Micah ;
Dadul, Tenzin ;
Langlais, Robert ;
Russell, David ;
Ahmad, Mansur .
JOURNAL OF THE AMERICAN DENTAL ASSOCIATION, 2018, 149 (01) :51-58