Authors :
Vijaya Kumar K; Santhi Baskaran
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3wbmTXh
DOI :
https://doi.org/10.5281/zenodo.7010190
Abstract :
Temporomandibular Joint (TMJ) disorder
is a set of orofacial ache syndromes that are the most
frequent non-dental ache issue in the maxillofacial area.
It mostly refers to a group of musculoskeletal problems
that can impact the masticatory system. It is believed
that 60-70% of the population suffers from the
minimum any signs. This condition is quite common in
the wide-ranging community, yet women are afflicted at
a 4:1 fraction. Over the past decades, advanced
Artificial Intelligence (AI) methods including machine
and deep learning algorithms have been developed to
recognize and categorize the TMJ disorder early from
different imaging modalities like panoramic images, Xray images, etc. Amongst, panoramic radiograph is
utilized as a preliminary forecasting technique in
association with a complete medicinal evaluation to
diagnose TMJ disorder. The findings observed from
such methods can help the physicians in decisionmaking and early diagnosis of TMJ disorder. This
paper presents a detailed review of different machine
and deep learning algorithms developed to recognize
and categorize TMJ disorder from panoramic images.
First, different TMJ disorder recognition and
categorization models designed by many researchers
based on machine and deep learning algorithms are
studied in brief. Then, a comparative study is conducted
to understand the drawbacks of those algorithms and
suggest a new solution to classify the TMJ disorder
accurately.
Keywords :
Temporomandibular disorder, Temporomandibular joint, Artificial intelligence, Machine learning, Deep learning, Panoramic imaging
Temporomandibular Joint (TMJ) disorder
is a set of orofacial ache syndromes that are the most
frequent non-dental ache issue in the maxillofacial area.
It mostly refers to a group of musculoskeletal problems
that can impact the masticatory system. It is believed
that 60-70% of the population suffers from the
minimum any signs. This condition is quite common in
the wide-ranging community, yet women are afflicted at
a 4:1 fraction. Over the past decades, advanced
Artificial Intelligence (AI) methods including machine
and deep learning algorithms have been developed to
recognize and categorize the TMJ disorder early from
different imaging modalities like panoramic images, Xray images, etc. Amongst, panoramic radiograph is
utilized as a preliminary forecasting technique in
association with a complete medicinal evaluation to
diagnose TMJ disorder. The findings observed from
such methods can help the physicians in decisionmaking and early diagnosis of TMJ disorder. This
paper presents a detailed review of different machine
and deep learning algorithms developed to recognize
and categorize TMJ disorder from panoramic images.
First, different TMJ disorder recognition and
categorization models designed by many researchers
based on machine and deep learning algorithms are
studied in brief. Then, a comparative study is conducted
to understand the drawbacks of those algorithms and
suggest a new solution to classify the TMJ disorder
accurately.
Keywords :
Temporomandibular disorder, Temporomandibular joint, Artificial intelligence, Machine learning, Deep learning, Panoramic imaging
