Evidence map›Paper›PMID 40996510›Full record

ArticleEuropean radiology2026

Deep learning reconstruction for temporomandibular joint MRI: diagnostic interchangeability, image quality, and scan time reduction.

Gyu-Dong Jo, Kug Jin Jeon, Yoon Joo Choi, Chena Lee, Sang-Sun Han

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Gyu-Dong Jo *Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, Seoul, Republic of Korea.
Kug Jin Jeon *Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, Seoul, Republic of Korea.
Yoon Joo ChoiDepartment of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, Seoul, Republic of Korea.
Chena LeeDepartment of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, Seoul, Republic of Korea.
Sang-Sun HanDepartment of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, Seoul, Republic of Korea. SSHAN@yuhs.ac.ORCID http://orcid.org/0000-0003-1775-7862

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the diagnostic interchangeability, image quality, and scan time of deep learning (DL)-reconstructed magnetic resonance imaging (MRI) compared with conventional MRI for the temporomandibular joint (TMJ). MATERIALS AND

methodsPatients with suspected TMJ disorder underwent sagittal proton density-weighted (PDW) and T2-weighted fat-suppressed (T2W FS) MRI using both conventional and DL reconstruction protocols in a single session. Three oral radiologists independently assessed disc shape, disc position, and joint effusion. Diagnostic interchangeability for these findings was evaluated by comparing interobserver agreement, with equivalence defined as a 95% confidence interval (CI) within ±5%. Qualitative image quality (sharpness, noise, artifacts, overall) was rated on a 5-point scale. Quantitative image quality was assessed by measuring the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the condyle, disc, and background air. Image quality scores were compared using the Wilcoxon signed-rank test, and SNR/CNR using paired t-tests. Scan times were directly compared.

resultsA total of 176 TMJs from 88 patients (mean age, 37 ± 16 years; 43 men) were analyzed. DL-reconstructed MRI demonstrated diagnostic equivalence to conventional MRI for disc shape, position, and effusion (equivalence indices < 3%; 95% CIs within ±5%). DL reconstruction significantly reduced noise in PDW and T2W FS sequences (p < 0.05) while maintaining sharpness and artifact levels. SNR and CNR were significantly improved (p < 0.05), except for disc SNR in PDW (p = 0.189). Scan time was reduced by 49.2%.

conclusionDL-reconstructed TMJ MRI is diagnostically interchangeable with conventional MRI, offering improved image quality with a shorter scan time. KEY POINTS: Question Long MRI scan times in patients with temporomandibular disorders can increase pain and motion-related artifacts, often compromising image quality in diagnostic settings. Findings DL reconstruction is diagnostically interchangeable with conventional MRI for assessing disc shape, disc position, and effusion, while improving image quality and reducing scan time. Clinical relevance DL reconstruction enables faster and more tolerable TMJ MRI workflows without compromising diagnostic accuracy, facilitating broader adoption in clinical settings where long scan times and motion artifacts often limit diagnostic efficiency.

Indexed as

Deep LearningMagnetic Resonance ImagingTemporomandibular JointTemporomandibular Joint DisordersAdolescentAdultAgedFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedSignal-To-Noise RatioTime FactorsYoung AdultDeep learningImage enhancementMagnetic resonance imagingTemporomandibular JointTemporomandibular joint disorders

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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.