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Tooth detection and numbering in panoramic radiographs using an artificial intelligence approach
1
Amrita School of Dentistry, Kochi Amrita Vishwa Vidyapeetham, Kochi Amrita School of Dentistry, Amrita Institute of Medical Science Campus, Ponekkara P.O-682041 India
2
Indian Institute of Science, Bangalore
Publication date: 2023-04-26
Popul. Med. 2023;5(Supplement):A612
ABSTRACT
Introduction:
Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Artificial intelligence (AI) system was used to detect and number teeth on panoramic radiographs.
Methods:
A data set of 600 randomly chosen panoramic radiographs of adults was used to train the system. The teeth numbering module classifies detected teeth images according to the FDI notation. It utilizes the classical VGG-16 CNN for the spatial arrangement of teeth.
Results:
The total numbers of true positive, false positive, and false negative results were recorded and consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively.
Conclusions:
The 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, cephalometric landmark detection, teeth structures segmentation and teeth classification for dental recording.
Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Artificial intelligence (AI) system was used to detect and number teeth on panoramic radiographs.
Methods:
A data set of 600 randomly chosen panoramic radiographs of adults was used to train the system. The teeth numbering module classifies detected teeth images according to the FDI notation. It utilizes the classical VGG-16 CNN for the spatial arrangement of teeth.
Results:
The total numbers of true positive, false positive, and false negative results were recorded and consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively.
Conclusions:
The 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, cephalometric landmark detection, teeth structures segmentation and teeth classification for dental recording.
| ISSN: | 2654-1459 |
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