Prediction of the Facial Growth Direction is Challenging

Kaźmierczak, Stanisław; Juszka, Zofia; Vandevska‐Radunovic, Vaska; Maal, T.J.J.; Fudalej, Piotr; Mańdziuk, Jacek

doi:10.1007/978-3-030-92310-5_77

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Now AI automated facial surface analysis sourcing from CBCT, Lidar from ubiquitous smartphones, or any other face scanner can be utilized for increased diagnostic precision and efficiency [ 44 , 59 , 86 , 87 , 88 ]. Facial growth and aging predictions represent another specialty leaving the human domain as AI algorithms are taking over prediction and planning [ 55 , 89 , 90 ]. Various companies have spent the last decade gathering big data regarding teeth movement resulting from aligner treatments, which have provided them with an essential foundation for advanced AI implementations supporting effective teeth movement.…”

Section: Resultsmentioning

confidence: 99%

“…The results of the second objective of this paper, visualized by means of the Venn diagrams in Figure 7 , show that a significant proportion of dental AI utilization is not focused on any particular dental field (17.1%). AI is most often implemented in orthodontics, especially in treatment planning, where automation for CBCT segmentation and 3D cephalometric analysis based on advanced 3D CNN algorithms is used [ 30 , 44 , 55 , 59 , 85 , 86 , 87 , 88 , 89 , 90 ]. It is also possible to predict aging and facial growth by AI prediction and planning.…”

Section: Discussionmentioning

confidence: 99%

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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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“…These methods could also constitute a promising perspective of our work insofar as they have already proved to be of interest in our scientific field. Indeed Kaźmierczak et al 55 adopted this approach to obtain a good prediction of the direction of facial growth in patients with malocclusions, and consequently to provide a more personalised treatment and increased chances of success.…”

Section: Discussionmentioning

confidence: 99%

An interpretable machine learning approach to study the relationship beetwen retrognathia and skull anatomy

Makaremi,

Vafaei Sadr,

Marcy

et al. 2023

Sci Rep

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Mandibular retrognathia (C2Rm) is one of the most common oral pathologies. Acquiring a better understanding of the points of impact of C2Rm on the entire skull is of major interest in the diagnosis, treatment, and management of this dysmorphism, but also permits us to contribute to the debate on the changes undergone by the shape of the skull during human evolution. However, conventional methods have some limits in meeting these challenges, insofar as they require defining in advance the structures to be studied, and identifying them using landmarks. In this context, our work aims to answer these questions using AI tools and, in particular, machine learning, with the objective of relaying these treatments automatically. We propose an innovative methodology coupling convolutional neural networks (CNNs) and interpretability algorithms. Applied to a set of radiographs classified into physiological versus pathological categories, our methodology made it possible to: discuss the structures impacted by retrognathia and already identified in literature; identify new structures of potential interest in medical terms; highlight the dynamic evolution of impacted structures according to the level of gravity of C2Rm; provide for insights into the evolution of human anatomy. Results were discussed in terms of the major interest of this approach in the field of orthodontics and, more generally, in the field of automated processing of medical images.

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