ArticlePloS one
Development of an artificial intelligence-based algorithm to classify images acquired with an intraoral scanner of individual molar teeth into three categories.
Article in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review.Medical science monitor : international medical journal of experimental and clinical research · 2024Pooled it
- AI-Based Intraoral Videography for Automated Dental Inspection and Charting in Children With Mixed Dentition.International journal of telemedicine and applications · 2026Article
- Recent Advances in Intraoral Scanners.Journal of dental research · 2024Review
- Application of Convolutional Neural Networks for Determining Gender and Age in Forensic Dentistry.Cureus · 2024Article
- Automatic removal of soft tissue from 3D dental photo scans; an important step in automating future forensic odontology identification.Scientific reports · 2024Article
- Assessment of fully digitalized workflow for implant-prosthetic rehabilitation in temporomandibular disorders patients: A clinical study.The Saudi dental journal · 2023Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- Texture-Based Neural Network Model for Biometric Dental Applications.Journal of personalized medicine · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundForensic dentistry identifies deceased individuals by comparing postmortem dental charts, oral-cavity pictures and dental X-ray images with antemortem records. However, conventional forensic dentistry methods are time-consuming and thus unable to rapidly identify large numbers of victims following a large-scale disaster.
objectiveOur goal is to automate the dental filing process by using intraoral scanner images. In this study, we generated and evaluated an artificial intelligence-based algorithm that classified images of individual molar teeth into three categories: (1) full metallic crown (FMC); (2) partial metallic restoration (In); or (3) sound tooth, carious tooth or non-metallic restoration (CNMR).
methodsA pre-trained model was created using oral-cavity pictures from patients. Then, the algorithm was generated through transfer learning and training with images acquired from cadavers by intraoral scanning. Cross-validation was performed to reduce bias. The ability of the model to classify molar teeth into the three categories (FMC, In or CNMR) was evaluated using four criteria: precision, recall, F-measure and overall accuracy.
resultsThe average value (variance) was 0.952 (0.000140) for recall, 0.957 (0.0000614) for precision, 0.952 (0.000145) for F-measure, and 0.952 (0.000142) for overall accuracy when the algorithm was used to classify images of molar teeth acquired from cadavers by intraoral scanning.
conclusionWe have created an artificial intelligence-based algorithm that analyzes images acquired with an intraoral scanner and classifies molar teeth into one of three types (FMC, In or CNMR) based on the presence/absence of metallic restorations. Furthermore, the accuracy of the algorithm reached about 95%. This algorithm was constructed as a first step toward the development of an automated system that generates dental charts from images acquired by an intraoral scanner. The availability of such a system would greatly increase the efficiency of personal identification in the event of a major disaster.
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