Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners

Lian, Chunfeng; Wang, Li; Wu, Tai-Hsien; Wang, Fan; Yap, Pew Thian; Ko, Ching‐Chang; Shen, Dinggang

doi:10.1109/tmi.2020.2971730

Cited by 111 publications

(67 citation statements)

References 30 publications

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“…In the implementation of VR technology, the key points are modeling and interaction. In the medical field, images from computed tomography [7,23], magnetic resonance imaging (MRI) [24], and dental scanners [25] can be used to rehabilitate virtual models. Interactions mainly contain visual interaction and tactile sensation interaction, which are actualized by display device and force feedback device, respectively.…”

Section: Virtual Realitymentioning

confidence: 99%

The Current Situation and Future Prospects of Simulators in Dental Education

Fan

et al. 2021

J Med Internet Res

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The application of virtual reality has become increasingly extensive as this technology has developed. In dental education, virtual reality is mainly used to assist or replace traditional methods of teaching clinical skills in preclinical training for several subjects, such as endodontics, prosthodontics, periodontics, implantology, and dental surgery. The application of dental simulators in teaching can make up for the deficiency of traditional teaching methods and reduce the teaching burden, improving convenience for both teachers and students. However, because of the technology limitations of virtual reality and force feedback, dental simulators still have many hardware and software disadvantages that have prevented them from being an alternative to traditional dental simulators as a primary skill training method. In the future, when combined with big data, cloud computing, 5G, and deep learning technology, dental simulators will be able to give students individualized learning assistance, and their functions will be more diverse and suitable for preclinical training. The purpose of this review is to provide an overview of current dental simulators on related technologies, advantages and disadvantages, methods of evaluating effectiveness, and future directions for development.

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Section: Virtual Realitymentioning

confidence: 99%

The Current Situation and Future Prospects of Simulators in Dental Education

Fan

et al. 2021

J Med Internet Res

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“…With regard to the selection of reference standards, all studies were considered as "low" risk of concern as expert judgment and clinical or pathological examination was applied as the reference standard. Concerns regarding the risk of bias were relatively high in the domain of index test, as ten [16,17,[20][21][22][23][24][25][26][27] of the included studies did not test their AI models on independent images unused for developing the algorithms. Table 1 exhibits the included studies regarding the use of AI for 3D imaging in DMFR.…”

Section: Current Use Of Ai For 3d Imaging In Dmfrmentioning

confidence: 99%

“…These algorithms can speed up the digital workflow and reduce human error. Furthermore, Lian et al proposed an automated tooth labeling algorithm based on intraoral scanning [25]. This algorithm can simplify the process of tooth position rearrangements in orthodontic treatment planning.…”

Section: Current Use Of Ai For Intraoral 3d Imaging and Facial Scanningmentioning

confidence: 99%

Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

Hung

Yeung

Tanaka

et al. 2020

IJERPH

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The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.

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“…Though with agreeable interpretability, these methods cannot generalize to various teeth morphologies and usually require human involvement for postcorrection. Current advances employed deep learning techniques to develop more generic solutions (Zhao et al 2006;Zou et al 2015;Li and Wang 2016;Xu et al 2018;Lian et al 2019;Zanjani, Moin, Verheij, et al 2019;Lian et al 2020;Sun et al 2020). These methods showed better performance than geometry-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning

et al. 2021

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Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4,000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. A clinical performance test of 500 patients with malocclusion and/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.

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Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners

Cited by 111 publications

References 30 publications

The Current Situation and Future Prospects of Simulators in Dental Education

The Current Situation and Future Prospects of Simulators in Dental Education

Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning

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