Article ; Online: Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M.
International journal of environmental research and public health
2023 Volume 20, Issue 5
Abstract: Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support ... ...
| Abstract | Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert. |
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| MeSH term(s) | Artificial Intelligence ; Pilot Projects ; Forensic Dentistry ; Age Determination by Teeth/methods |
| Language | English |
| Publishing date | 2023-03-06 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2175195-X |
| ISSN | 1660-4601 ; 1661-7827 |
| ISSN (online) | 1660-4601 |
| ISSN | 1661-7827 |
| DOI | 10.3390/ijerph20054620 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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