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Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review - PubMed

  • ️Sun Jan 01 2023

Review

Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review

Mayank Shrivastava et al. Int J Oral Sci. 2023.

Abstract

Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.

© 2023. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1

Overview of relationship between temporomandibular disorders, neuroimaging and AI

Fig. 2
Fig. 2

Brain regions and pain network involved in chronic temporomandibular disorders. AMG amygdala, BG basal ganglia, dACC dorsal anterior cingulate cortex, dLPFC dorsolateral prefrontal cortex, M1 primary motor area, PB para brachial, PCC posterior cingulated cortex, PAG periaqueductal gray, pre-SMA part of supplemental motor area, RM raphe magnus, S1 and S2 primary and secondary cortex, SMA supplemental motor area, SPL superior parietal lobe area

Fig. 3
Fig. 3

Overview of AI on assessment of chronic painful temporomandibular disorders

Fig. 4
Fig. 4

Relationship between neuroimaging and machine learning algorithms

Fig. 5
Fig. 5

A summary on role of neuroimaging methods and AI in chronic painful TMD

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