Orthodontics in the era of big data analytics

Allareddy, Veerasathpurush; Venugopalan, Shankar Rengasamy; Nalliah, Romesh; Caplin, Jennifer; Lee, Min Kyeong; Allareddy, Veerajalandhar

doi:10.1111/ocr.12279

Cited by 31 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Domestic pig molars provide an ideal model to study the tooth development process. In the future, big data, artificial intelligence, and cloud computing may help gather data on tooth development, acquire more accurate estimation results, and guide precise diagnoses and treatment in dental practice (Allareddy et al, 2019; Park & Park, 2018). Meanwhile, such investigations on natural tooth germ development can provide valuable information for human tooth regeneration (Wang et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Comparison of actual porcine tooth crown development stages and computer image analysis

Dai

Lian

Ling

et al. 2020

The Anatomical Record

View full text Add to dashboard Cite

Tooth developmental stage evaluation is important in dental and chronological age estimation, and it is important for accurate diagnoses and appropriate treatment in dental practice. It is routinely assessed by clinical observations and radiographic techniques. This study aimed at ascertaining tooth developmental stage judgments made by examiners and Mimics software according to the Nolla method with radiographs. Meanwhile, the true tooth developmental stages would be explored with histological analysis. Twenty freshly slaughtered porcine heads were collected and hemisected, and both the left and right mandibular samples were numbered. The developmental stages of the second and third permanent molars (M2 and M3) were evaluated by examiners and Mimics software analysis. The ratio of the radiopaque calcified area to the dental follicle (RCA/DF) at different stages was calculated. Both non-decalcified and decalcified samples were processed for histologic observation. The results showed significant differences between RCA/DF ratios from different developmental stages. There was a high positive correlation between the examiners' evaluation results and Mimics analysis results. Radiograph judgments and histology observation results were consistent from Stages 2-6. However, radiograph images of Stage 1 samples showed only crypts present, while under a surgical operating microscope, a bell-shaped tooth germ was observed. This was also confirmed by normal and hard tissue histology. In conclusion, radiograph judgments made by either examiners or Mimics software were both reliable. Mimics analysis can be a useful tool in evaluating tooth developmental stages. However, judgments need to be made cautiously in early developmental stages.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of actual porcine tooth crown development stages and computer image analysis

Dai

Lian

Ling

et al. 2020

The Anatomical Record

View full text Add to dashboard Cite

show abstract

“…Hence, communication between data analysts, data scientists and clinicians is important during all phases of CDSSs development. 47 Within dentistry, different ML algorithms have been utilized based on the size of the data, variables/features to analyse, and the objective of the model. ML can involve unsupervised or supervised learning.…”

Section: Data Processingmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Consequently, automatic methods based on deep neural networks have been tested for several purposes, which are as follows: classification, image registration, segmentation, lesion detection, image retrieval, image guided therapy, image generation, and enhancement . Most recently, radiomics and AI research have been advancing in the dental field, revealing the potential of these technologies to substantially improve clinical care …”

Section: Radiomics and DL Applications In Radiologymentioning

confidence: 99%

Radiomics and Machine Learning in Oral Healthcare

Leite

Vasconcelos

Willems

et al. 2020

Proteomics Clinical Apps

View full text Add to dashboard Cite

The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.

show abstract

Orthodontics in the era of big data analytics

Cited by 31 publications

References 35 publications

Comparison of actual porcine tooth crown development stages and computer image analysis

Comparison of actual porcine tooth crown development stages and computer image analysis

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

Radiomics and Machine Learning in Oral Healthcare

Contact Info

Product

Resources

About