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ArticleDento maxillo facial radiology2025

Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.

Tabea Flügge, Shankeeth Vinayahalingam, Niels van Nistelrooij, Stefanie Kellner, Tong Xi, Bram van Ginneken, Stefaan Bergé, Max Heiland, Florian Kernen, Ute Ludwig and 1 more

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In one paragraph

Article in Dento maxillo facial radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

2 citing papers in PubMed.

  1. Article
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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

11 authors.

Tabea FlüggeCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203 Berlin, Germany.ORCID 0000-0002-1288-8452
Shankeeth VinayahalingamDepartment of Oral and Maxillofacial Surgery, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, 6525 EX, the Netherlands.
Niels van NistelrooijCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203 Berlin, Germany.
Stefanie KellnerCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203 Berlin, Germany.
Tong XiDepartment of Oral and Maxillofacial Surgery, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, 6525 EX, the Netherlands.
Bram van GinnekenDepartment of Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, the Netherlands.
Stefaan BergéDepartment of Oral and Maxillofacial Surgery, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, 6525 EX, the Netherlands.
Max HeilandCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203 Berlin, Germany.ORCID 0000-0002-4987-2913
Florian KernenDepartment of Oral and Maxillofacial Surgery, Translational Implantology, Medical Center , Faculty of Medicine, University of Freiburg, Hugstetter Straße 55, 79106 Freiburg, Germany.
Ute LudwigDivision of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University Medical Center Freiburg, University of Freiburg, Kilianstraße 5a, 79106 Freiburg im Breisgau, Germany.
Kento OdakaCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203 Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.

methodsMR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation. Two clinicians segmented and annotated the teeth in each dataset. A segmentation model was trained using the nnU-Net framework. The manual reference tooth segmentation and the inferred tooth segmentation were superimposed and compared by computing precision, sensitivity, and Dice-Sørensen coefficient. Surface meshes were extracted from the segmentations, and the distances between points on each mesh and their closest counterparts on the other mesh were computed, of which the mean (average symmetric surface distance) and 95th percentile (Hausdorff distance 95%, HD95) were reported.

resultsThe model achieved an overall precision of 0.867, a sensitivity of 0.926, a Dice-Sørensen coefficient of 0.895, and a 95% Hausdorff distance of 0.91 mm. The model predictions were less accurate for datasets containing dental restorations due to image artefacts.

conclusionsThe current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.

Indexed as

Deep LearningMagnetic Resonance ImagingToothAdultFemaleHumansImaging, Three-DimensionalMalePilot Projectsartificial intelligencejawmagnetic resonance imagingmedical image processingtooth

Identifiers

PMID39589897
PMCPMC11664100

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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.