Impact of Image Context on Deep Learning for Classification of Teeth on Radiographs

Krois, Joachim; Schneider, Lisa; Schwendicke, Falk

doi:10.3390/jcm10081635

Cited by 9 publications

(9 citation statements)

References 14 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rapid development of the network, provides a wide range for the application of images, which is the most popular research field .Krois used dnn method to assess the impact of image context information on the accuracy of learning models for tooth classification on panoramic dental radiographs [1] . Multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation.…”

Section: Introductionmentioning

confidence: 99%

A covering model of 3d-teeth analysis for holes filling

Zhu¹,

Zhu²,

Wang³

et al. 2022

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

A covering model of 3d-teeth analysis for holes filling

Zhu¹,

Zhu²,

Wang³

et al. 2022

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Additionally, a few studies used specialized imaging techniques like RadioVisioGraphy or manipulated high-definition photographs of the mouth. with considerable variation across studies ranging from 24 (Wirtz et al, 2018) to 15515 (Krois et al, 2021). Only seven papers (10.29%) utilized datasets comprising more than 2000 radiographs (Bilgir et al, 2021), , , (Cantu et al, 2020), (Krois et al, 2021), (Kyventidis & Angelopoulos, 2021), (Schneider et al, 2023).…”

Section: Datasetsmentioning

confidence: 99%

“…with considerable variation across studies ranging from 24 (Wirtz et al, 2018) to 15515 (Krois et al, 2021). Only seven papers (10.29%) utilized datasets comprising more than 2000 radiographs (Bilgir et al, 2021), , , (Cantu et al, 2020), (Krois et al, 2021), (Kyventidis & Angelopoulos, 2021), (Schneider et al, 2023). In order to analyze RQ1.2, the following data were collected: from sixty-eight selected works, fifty-nine (86.76%) presented a description of the dataset annotation process, fifty-two (76.47%) informed the area of knowledge of the annotators, forty-two (61.76%) cited the number of professionals involved, and twenty-three (33.82%) specified the experience level of these professionals.…”

Section: Datasetsmentioning

confidence: 99%

“…The average number of annotators was 2.24, with standard deviation (SD) as 1.22. Krois et al (2021) was removed from the calculation of the mean and SD because it had a team of fifty annotators, creating a considerable distance from the second largest team with five annotators (Pinheiro et al, 2021;Tuzoff et al, 2019). The average professional experience informed by the twenty-three studies was 6.98 years (SD = 3.98 years).…”

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning to support the detection and classification of teeth, dental caries and restorations: a review

Carneiro,

Zancan,

Tirapelli

et al. 2024

Braz. J. Hea. Rev.

View full text Add to dashboard Cite

Oral health serves as a crucial indicator of individuals' overall well-being and quality of life, making it a pertinent concern regularly addressed by healthcare professionals. Utilizing imaging exams is imperative for detecting and identifying oral diseases and conditions, and the application of Artificial Intelligence (AI) has garnered attention for its potential in this realm. We conducted a systematic literature review focusing on the utilization of Deep Learning techniques in dental radiographs for the detection, segmentation, and classification of teeth, caries, and restorations. Our review encompassed automated searches across prominent databases including the ACM Digital Library, IEEE Xplore Digital Library, PubMed, and Scopus, yielding 393 primary papers published between 2012 and 2023. Following stringent inclusion and exclusion criteria, we thoroughly examined 68 papers, assessing their consistency and adequacy of different aspects in terms of the databases used, techniques implemented, and outcomes reported. It was noted that 41.66% of the analyzed papers lacked clear information regarding approval of data usage from ethics committees. Additionally, despite the interdisciplinary nature of computational techniques in oral health, 38.23% of the surveyed studies were conducted by teams comprising solely professionals from one specific area. Moreover, 66.18% of the papers focused solely on panoramic radiographs, with commonly utilized metrics including accuracy, recall, and precision. Notably, the U-Net and Mask R-CNN networks emerged as the most frequently applied methodologies. Despite the proliferation of investigations in this field, several challenges persist, including the limited availability of public datasets, inadequate detailing of developed methodologies, and a lack of systematization in result presentation. These challenges hinder a fair comparison between studies, presenting a significant obstacle to be addressed for further progress in the field.

show abstract

“…The study [73] was removed from the calculation of the mean and standard deviation because it had a team of fifty annotators, creating a considerable distance from the second largest team with five annotators [31,32]. The average experience of professionals informed by the twenty-three studies was 6.98 years (SD = 3.98 years).…”

Section: Datasetsmentioning

confidence: 99%

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

Carneiro,

Zancan,

Tirapelli

et al. 2023

Preprint

View full text Add to dashboard Cite

Purpose: Oral health is an indicator of individuals' health and quality of life. Consequently, it is a concerning topic faced everyday by healthcare professionals. X-ray images are essential resources for detecting oral problems. Artificial Intelligence has gained attention in the detection of oral diseases and conditions. We carry out a systematic mapping in the literature regarding the application of deep learning in dental radiographs for detection, segmentation, and classification of teeth, caries, and restorations. Methods: Through automated searches in the ACM Digital Library, IEEE Xplore Digital Library, PubMed, and Scopus, 394 primary articles were found, published between 2012-2023. After applying inclusion and exclusion criteria, 69 articles were read and analyzed considering: consistency and adequacy of the used databases, implemented techniques, and results. Results: 40.82% of the analyzed articles did not present clear information on the approval of ethics committees to carry out the research. Although the application of computational techniques in oral health is an interdisciplinary topic, 37.68% of the 69 surveys were conducted by teams composed only of professionals from one area. 66.67% of the articles explored only panoramic radiographs and the most used metrics were accuracy, recall, and precision. The U-Net and Mask R-CNN networks were the most applied. Conclusion: Despite the increase in the number of investigations, some challenges, such as the low availability of public data sets, the lack of details in the developed methodologies, and the lack of standardization when presenting results, complicate a fair comparison between papers, which is an obstacle to be overcome.

show abstract

Impact of Image Context on Deep Learning for Classification of Teeth on Radiographs

Cited by 9 publications

References 14 publications

A covering model of 3d-teeth analysis for holes filling

A covering model of 3d-teeth analysis for holes filling

Deep learning to support the detection and classification of teeth, dental caries and restorations: a review

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

Contact Info

Product

Resources

About