Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments

You, Wenzhe; Hao, Aimin; Li, Shuai; Wang, Yong; Xia, Bican

doi:10.21203/rs.2.21027/v2

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Examples include using CNNs to detect periapical lesions, dental caries, and odontogenic cystic lesions. However, it indicates that only very few publications 7,8 use digital camera photos as input data. The majority of existing work trained a CNN model using medical images such as radiographs or computed tomography scans that must be obtained by medical devices and are costly for patients.…”

Section: Introductionmentioning

confidence: 99%

“…The models need to "see" enough examples to t the large number of parameters. Previous dental literature used relatively small data sets of, at most, a few thousand images [7][8][9] . Our data set presents a unique opportunity to implement a deep learning method by having access to a sample that is orders of magnitude larger than previous research, collected from the largest international cohort, to-date, of subjects with OFC and controls.…”

Section: Introductionmentioning

confidence: 99%

“…The TL technique can mitigate the lack of data problem as it uses information from other sources to build the model. We have identi ed only 13 publications in the dental image classi cation literature since 2017 that have utilized TL 7,8,[10][11][12][13][14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

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Dental Anomaly Detection Using Intraoral Photos Via Deep Learning

Ragodos¹,

Wang²,

Padilla³

et al. 2021

Preprint

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Children with orofacial clefting (OFC) present with a wide range of dental anomalies. Identifying these anomalies is vital to understand their etiology and to discern the complex phenotypic spectrum of OFC. Such anomalies are currently identified using intra-oral exams by dentists, a costly and time-consuming process. We claim that automating the process of anomaly detection using deep neural networks (DNNs) could increase efficiency and provide reliable anomaly detection while potentially increasing the speed of research discovery. This study characterizes the use of` DNNs to identify dental anomalies by training a DNN model using intraoral photographs from the largest international cohort to date of children with nonsyndromic OFC and controls (OFC1). In this project, the intraoral images were submitted to a Convolutional Neural Network (CNN) model to perform multi-label multi-class classification of 10 dental anomalies. The network predicts whether an individual exhibits any of the 10 anomalies and is able to do so significantly faster than a human rater. For every anomaly except mammalons, F1 scores suggest that our model performs competitively at anomaly detection when compared to a dentist with 8 years of clinical experience. In addition, we use saliency maps to provide a post-hoc interpretation for our model’s predictions. This enables dentists to examine and verify our model’s predictions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dental Anomaly Detection Using Intraoral Photos Via Deep Learning

Ragodos¹,

Wang²,

Padilla³

et al. 2021

Preprint

View full text Add to dashboard Cite

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Dental anomaly detection using intraoral photos via deep learning

Ragodos

Wang

Padilla

et al. 2022

Sci Rep

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Children with orofacial clefting (OFC) present with a wide range of dental anomalies. Identifying these anomalies is vital to understand their etiology and to discern the complex phenotypic spectrum of OFC. Such anomalies are currently identified using intra-oral exams by dentists, a costly and time-consuming process. We claim that automating the process of anomaly detection using deep neural networks (DNNs) could increase efficiency and provide reliable anomaly detection while potentially increasing the speed of research discovery. This study characterizes the use of` DNNs to identify dental anomalies by training a DNN model using intraoral photographs from the largest international cohort to date of children with nonsyndromic OFC and controls (OFC1). In this project, the intraoral images were submitted to a Convolutional Neural Network model to perform multi-label multi-class classification of 10 dental anomalies. The network predicts whether an individual exhibits any of the 10 anomalies and can do so significantly faster than a human rater can. For all but three anomalies, F1 scores suggest that our model performs competitively at anomaly detection when compared to a dentist with 8 years of clinical experience. In addition, we use saliency maps to provide a post-hoc interpretation for our model’s predictions. This enables dentists to examine and verify our model’s predictions.

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Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments

Cited by 2 publications

References 9 publications

Dental Anomaly Detection Using Intraoral Photos Via Deep Learning

Dental Anomaly Detection Using Intraoral Photos Via Deep Learning

Dental anomaly detection using intraoral photos via deep learning

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