Patient Specific Classification of Dental Root Canal and Crown Shape

Dumont, Maxime; Prieto, Juan-Carlos; Brosset, Serge; Cevidanes, Lúcia Helena Soares; Bianchi, Jonas; Ruellas, Antônio Carlos de Oliveira; Gurgel, Marcela; Massaro, Camila; Castillo, Aron Aliaga–Del; Ioshida, Marcos; Yatabe, Marília; Benavides, Erika; Ríos, Héctor F.; Soki, Fabiana N.; Neiva, Gisele F.; Aristizabal, Juan Fernando; Rey, Diego; Álvarez, María Antonia; Najarian, Kayvan; Gryak, Jonathan; Styner, Martin; Fillion‐Robin, Jean‐Christophe; Paniagua, Beatriz; Soroushmehr, S. M. Reza

doi:10.1007/978-3-030-61056-2_12

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…While the unique non‐extraction sample had available previously acquired small field of view high‐resolution CBCTs, the estimation of the dental long axis using DDMs is much improved with the new methods presented here. Ongoing work machine learning approaches 40 are refining the methods presented in this study to synthesize information from crown, root, and root canal morphology towards planning tooth movement without the need for ionizing radiation scans. Such advances will be applicable to the evaluation of biomechanics of tooth movement with any treatment modality, and generalizable for planning crown restorations and implant loading axes.…”

Section: Discussionmentioning

confidence: 99%

Dental long axes using digital dental models compared to cone‐beam computed tomography

Cong

Massaro

Ruellas

et al. 2021

Orthod Craniofacial Res

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Objective Standard methods of evaluating tooth long axes are not comparable (digital dental models [DDMs], panoramic and cephalometric radiographs) or expose patients to more radiation (cone‐beam computed tomography [CBCT]). This study aimed to compare angular changes in tooth long axes using DDMs vs using CBCTs. Settings and sample population Secondary data analysis of DDMs and CBCTs, taken before and after orthodontic treatment with piezocision of 24 patients. Methods Angular changes in tooth long axes were evaluated using landmarks on first molars (centre of the occlusal surface and centre of the furcation), canines and incisors (cusp tip and centre of the root at the cementoenamel junction). Wilcoxon test, intraclass correlation coefficient (ICC) and Bland‐Altman plots were used to test intra‐ and inter‐rater agreement and compare DDM and CBCT measurements. Results The mesiodistal angulation and buccolingual inclination DDM measurements were reproducible. Overall mean differences between DDM and CBCT measurements of mesiodistal angulation, 1.9°±1.5°, and buccolingual inclination, 2.2 ± 2.2°, were not significant for all teeth. ICC between DDM and CBCT measurements ranged from good (0.85 molars) to excellent (0.94 canines; 0.96 incisors). The percentages of measurements outside the range of ±5 were 17.4% for molars, 13.8% for canines and 4.5% for incisors. Conclusions DDM assessment of changes in tooth long axes has good reproducibility and yields comparable measurements to those obtained from CBCT within a 5° range. These findings lay the groundwork for machine learning approaches that synthesize crown and root canal information towards planning tooth movement without the need for ionizing radiation scans.

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Section: Discussionmentioning

confidence: 99%

Dental long axes using digital dental models compared to cone‐beam computed tomography

Cong

Massaro

Ruellas

et al. 2021

Orthod Craniofacial Res

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“…The condition of teeth is a significant factor of influence for stomatognathic system health. The classification and segmentation of tooth and root canal by ML methods has achieved promising results both at the two-dimensional and three-dimensional levels ( Dumont et al, 2020 ; Zhang et al, 2018 ). Dumont et al (2020) combined an intraoral scanner-acquired crown form with a CBCT-obtained root form to realize the clinical labeling of spatial crown and root morphology.…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren

Luo

et al. 2021

PeerJ

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Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

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“…In addition, it provides tools for automatic segmentation of root canals (RootCanalSeg), teeth and gums (DentalModelSeg). 74 Overcoming the registration challenges created by merging the information from different imaging modalities and DDM, a work in progress, will enable reliable quantitative assessments of teeth movement (Figure 4).…”

Section: Dental Decision Optimizationmentioning

confidence: 99%

“…The CDSS for dental applications deployed in the DSCI integrates dental crowns’ and root canals’ relevant clinical information from the DDMs and CBCT scans, respectively (Figure 4). In addition, it provides tools for automatic segmentation of root canals (RootCanalSeg), teeth and gums (DentalModelSeg) 74 . Overcoming the registration challenges created by merging the information from different imaging modalities and DDM, a work in progress, will enable reliable quantitative assessments of teeth movement (Figure 4).…”

Section: Data Storage For Computation and Integrationmentioning

confidence: 99%

Clinical decision support systems in orthodontics: A narrative review of data science approaches

Turkestani

Bianchi

Deleat‐Besson

et al. 2021

Orthod Craniofacial Res

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Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

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Patient Specific Classification of Dental Root Canal and Crown Shape

Cited by 10 publications

References 16 publications

Dental long axes using digital dental models compared to cone‐beam computed tomography

Dental long axes using digital dental models compared to cone‐beam computed tomography

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Clinical decision support systems in orthodontics: A narrative review of data science approaches

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