Evidence map›Paper›PMID 36036857›Full record

ArticleAnnals of biomedical engineering2023

Classifying Temporomandibular Disorder with Artificial Intelligent Architecture Using Magnetic Resonance Imaging.

Zih-Kai Kao, Neng-Tai Chiu, Hung-Ta Hondar Wu, Wan-Chen Chang, Ding-Han Wang, Yen-Ying Kung, Pei-Chi Tu, Wen-Liang Lo, Yu-Te Wu

Abstract read
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In one paragraph

Article in Annals of biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  15. Evaluation of mandibular condylar morphology using orthopantomogram in South Indian population.Journal of advanced pharmaceutical technology & research · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zih-Kai KaoInstitute of Biophotonics, National Yang Ming Chiao Tung University, No.155, Section 2, Li-Nong Street, Beitou District, 11221, Taipei, Taiwan.
Neng-Tai ChiuInstitute of Biophotonics, National Yang Ming Chiao Tung University, No.155, Section 2, Li-Nong Street, Beitou District, 11221, Taipei, Taiwan.
Hung-Ta Hondar WuSchool of Medicine, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Wan-Chen ChangDepartment of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Ding-Han WangDepartment of Dentistry, College of Dentistry, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Yen-Ying KungSchool of Medicine, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Pei-Chi TuDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
Wen-Liang LoDepartment of Dentistry, College of Dentistry, National Yang Ming Chiao Tung University, Taipei, Taiwan. wllo@vghtpe.gov.tw.
Yu-Te WuInstitute of Biophotonics, National Yang Ming Chiao Tung University, No.155, Section 2, Li-Nong Street, Beitou District, 11221, Taipei, Taiwan. ytwu@ym.edu.tw.ORCID http://orcid.org/0000-0002-6942-0340

Funding

Taipei Veterans General Hospital CIC008
6 · The paper itself

Abstract

This study proposes a new diagnostic tool for automatically extracting discriminative features and detecting temporomandibular joint disc displacement (TMJDD) accurately with artificial intelligence. We analyzed the structural magnetic resonance imaging (MRI) images of 52 patients with TMJDD and 32 healthy controls. The data were split into training and test sets, and only the training sets were used for model construction. U-net was trained with 100 sagittal MRI images of the TMJ to detect the joint cavity between the temporal bone and the mandibular condyle, which was used as the region of interest, and classify the images into binary categories using four convolutional neural networks: InceptionResNetV2, InceptionV3, DenseNet169, and VGG16. The best models were InceptionV3 and DenseNet169; the results of InceptionV3 for recall, precision, accuracy, and F1 score were 1, 0.81, 0.85, and 0.9, respectively, and the corresponding results of DenseNet169 were 0.92, 0.86, 0.85, and 0.89, respectively. Automated detection of TMJDD from sagittal MRI images is a promising technique that involves using deep learning neural networks. It can be used to support clinicians in diagnosing patients as having TMJDD.

Indexed as

Artificial IntelligenceTemporomandibular Joint DisordersHumansMagnetic Resonance ImagingMandibular CondyleTemporomandibular JointDeep learningDiagnosis, computer-assistedImage interpretation, computer-assistedPattern recognition, automatedSpatial analysisTemporomandibular joint disc

Identifiers

PMID36036857

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.