Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M

Romain Bui, Régis Iozzino, Raphaël Richert, Pascal Roy, Loïc Boussel, Cheraz Tafrount and Maxime Ducret ()
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Romain Bui: Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
Régis Iozzino: Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
Raphaël Richert: Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
Pascal Roy: Service de Biostatistique—Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, 69008 Lyon, France
Loïc Boussel: Department of Radiology, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, 69004 Lyon, France
Cheraz Tafrount: Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
Maxime Ducret: Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France

IJERPH, 2023, vol. 20, issue 5, 1-13

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.

Keywords: artificial intelligence; age estimation; dentistry; deep learning; machine learning; neural network; topological analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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