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Review
. 2023 Aug 18;13(16):2700.
doi: 10.3390/diagnostics13162700.

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Sifa Ozsari  1 , Mehmet Serdar Güzel  1 , Dilek Yılmaz  2 , Kıvanç Kamburoğlu  3
Affiliations
Review

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Sifa Ozsari et al. Diagnostics (Basel). .

Abstract

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

Keywords: artificial intelligence; deep learning; machine learning; temporomandibular joint; temporomandibular joint diseases.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Anatomy of TMJ [10].
Figure 2
Figure 2
The flow chart of publication selection.
Figure 3
Figure 3
The processing workflow of TMJSeg for segmenting small field of view scans [23].
Figure 4
Figure 4
The spectrum of Data Science to advance TMJ OA diagnosis includes Data capture and acquisition, Data processing with a web-based data management, Data Analytics involving in-depth statistical analysis, machine learning approaches, and Data communication to help the decision-making support in TMJ OA diagnosis [39].
Figure 5
Figure 5
The general stages of the study [49].
See this image and copyright information in PMC

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