An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

Talaat, Wael M.; Shetty, Shishir; Al Bayatti, Saad; Talaat, Sameh; Mourad, Louloua; Shetty, Sunaina; Kaboudan, Ahmed

doi:10.1038/s41598-023-43277-6

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…In a preliminary study, Reda et al 13 demonstrated that AI can effectively identify TMDs early by accessing records of other clinical cases reported in the literature, thus providing a possible diagnosis based on symptoms. Likewise, Talaat et al 14 investigated the capacity of AI models to identify potential risks of osteoarthritis in the temporomandibular joint (TMJ) using computed tomography (CT)-considered the gold standard for investigation. Interestingly, these authors found that AI exhibited a greater degree of diagnostic accuracy compared with professional radiologists, in terms of both symptoms and the presence of morphostructural alterations (p = 0.0079 and p = 0.0214, respectively).…”

Section: E T T E R T O T H E E D I T O Rmentioning

confidence: 99%

ChatGPT‐4 as auxiliary tool in the temporomandibular disorders diagnostic: An opinion

Freitas,

da Mota Santana,

Floresta

et al. 2023

Oral Surgery

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Section: E T T E R T O T H E E D I T O Rmentioning

confidence: 99%

ChatGPT‐4 as auxiliary tool in the temporomandibular disorders diagnostic: An opinion

Freitas,

da Mota Santana,

Floresta

et al. 2023

Oral Surgery

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A Visualization System for Interactive Diagnosis and Intelligent Statistical Analysis of Oral Diseases based on Panoramic Radiography: Method Study (Preprint)

Xu,

Hong,

Pan

et al. 2024

Preprint

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BACKGROUND Panoramic radiography is an essential auxiliary diagnostic tool for oral diseases. It is a difficult and time-consuming task to generate electronic medical records and explore oral diseases based on collective analysis after conducting extensive panoramic radiography interpretation. OBJECTIVE The objective of our work is to develop a visualization system for the interactive diagnosis and intelligent statistical analysis of oral diseases based on panoramic radiographs. METHODS Firstly, we employ a deep learning model, Mask Region-based Convolutional Neural Network (Mask-RCNN), to conduct tooth segmentation in the panoramic radiographs. Then, we provide a rich set of user interfaces for doctors to revise the tooth segmentations and identify the lesion location for the analysis of oral diseases. After that, we provide a rule-based Natural Language Processing (NLP) method to generate electronic medical records. We further develop statistical correlation analysis to visually evaluate and interactively explore the statistical data from oral health surveys. RESULTS We conduct tooth segmentation and manual diagnosis, obtain their electronic medical records and do collective analysis based on a group of real-world panoramic radiographs. The results are reported from a comprehensive case study showing that our system is capable of improving the efficiency in disease detection and data mining, which also can fuel research studies in the field of public oral health. CONCLUSIONS This paper provides an efficient, automatic, visualizing, and user-friendly system, which helps dentists easily conduct personal oral diagnoses and access collective oral health status. Moreover, extensive population-level oral health surveys and visual analyses provide robust guidance and support for oral healthcare strategies.

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An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings

Almășan,

Mureșanu,

Hedeșiu

et al. 2024

Medicina

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Background and Objectives: This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magnetic resonance imaging disc displacement diagnosis on oblique sagittal and oblique coronal images. Materials and Methods: The study included fifty subjects with magnetic resonance imaging scans of the temporomandibular joint. Oblique sagittal and coronal sections of the magnetic resonance imaging scans were analyzed. Investigations were performed on the right and left coronal images with a closed mouth, as well as right and left sagittal images with closed and open mouths. Three hundred sagittal and coronal images were employed to train the artificial intelligence algorithm. Results: The accuracy ratio of the completely computerized articular disc identification method was 81%. Conclusions: An automated and accurate evaluation of temporomandibular joint disc position was developed by using both oblique sagittal and oblique coronal magnetic resonance imaging images.

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An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

Cited by 3 publications

References 37 publications

ChatGPT‐4 as auxiliary tool in the temporomandibular disorders diagnostic: An opinion

ChatGPT‐4 as auxiliary tool in the temporomandibular disorders diagnostic: An opinion

A Visualization System for Interactive Diagnosis and Intelligent Statistical Analysis of Oral Diseases based on Panoramic Radiography: Method Study (Preprint)

An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings

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