Evidence map›Paper›PMID 38990220›Full record

ArticleOral radiology2024

Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.

Yukiko Iwase, Tomoya Sugiki, Yoshitaka Kise, Masako Nishiyama, Michihito Nozawa, Motoki Fukuda, Yoshiko Ariji, Eiichiro Ariji

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Article in Oral radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yukiko Iwase *Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan.
Tomoya Sugiki *Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan.
Yoshitaka KiseDepartment of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan. kise@dpc.agu.ac.jp.ORCID 0000-0002-5890-2950
Masako NishiyamaDepartment of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan.
Michihito NozawaDepartment of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
Motoki FukudaDepartment of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
Yoshiko ArijiDepartment of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
Eiichiro ArijiDepartment of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images.

methodsA total of 68 TMJs with or without condylar OA in dentofacial deformity patients were tested to verify the consistencies and performances of DL models created using 252 TMJs with or without OA in TMJ disorder and dentofacial deformity patients; these models were used to diagnose OA on conventional panoramic (Con-Pa) images and open (Open-TMJ) and closed (Closed-TMJ) mouth TMJ projection images. The GoogLeNet and VGG-16 networks were used to create the DL models. For comparison, two dental residents with < 1 year of experience interpreting radiographs evaluated the same condyle data that had been used to test the DL models.

resultsOn Open-TMJ images, the DL models showed moderate to very good consistency, whereas the residents' demonstrated fair consistency on all images. The areas under the curve (AUCs) of both DL models on Con-Pa (0.84 for GoogLeNet and 0.75 for VGG-16) and Open-TMJ images (0.89 for both models) were significantly higher than the residents' AUCs (p < 0.01). The AUCs of the DL models on Open-TMJ images (0.89 for both models) were higher than the AUCs on Closed-TMJ images (0.72 for both models).

conclusionsThe DL models created in this study could help residents to interpret Con-Pa and Open-TMJ images in the diagnosis of condylar OA.

Indexed as

Deep LearningDentofacial DeformitiesMandibular CondyleOsteoarthritisRadiography, PanoramicAdolescentAdultAgedFemaleHumansMaleMiddle AgedTemporomandibular JointTemporomandibular Joint DisordersYoung AdultDisorder, temporomandibular jointLearning, deepLearning, machinePanoramic radiography

Identifiers

PMID38990220

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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.