Evidence map›Paper›PMID 40632440›Full record

ArticleRadiological physics and technology2025

Detection and dual-label classification of tooth number and condition in dental panoramic radiographs including deciduous teeth.

Koki Sakai, Chisako Muramatsu, Yuta Seino, Ryo Takahashi, Tatsuro Hayashi, Wataru Nishiyama, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, Hiroshi Fujita

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Article in Radiological physics and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Koki SakaiGraduate School of Natural Science and Technology, Gifu University, Gifu, Japan.
Chisako MuramatsuFaculty of Data Science, Shiga University, Hikone, Japan. chisako-muramatsu@biwako.shiga-u.ac.jp.ORCID http://orcid.org/0000-0002-9251-2833
Yuta SeinoDivision for Oral Dental Informatics, The University of Osaka Dental Hospital, Osaka, Japan.
Ryo TakahashiEyeTech Co., Ltd, Tokyo, Japan.
Tatsuro HayashiEyeTech Co., Ltd, Tokyo, Japan.
Wataru NishiyamaSchool of Dentistry, Asahi University, Mizuho, Japan.
Xiangrong ZhouFaculty of Engineering, Gifu University, Gifu, Japan.
Takeshi HaraFaculty of Engineering, Gifu University, Gifu, Japan.
Akitoshi KatsumataSchool of Dentistry, Asahi University, Mizuho, Japan.
Hiroshi FujitaFaculty of Engineering, Gifu University, Gifu, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe purpose of this study is to automatically extract the information necessary for chart recording from panoramic radiographs, to reduce the workload for dentists. STUDY

designUsing 1,085 dental panoramic radiographs (994 of permanent dentition and 91 of mixed dentition) taken at 10 facilities, we conducted tooth detection, numbering, and condition classification. Tooth condition was defined into five classes: natural, partial restoration, prosthetic crown, implant, and pontic. First, the YOLOv7 model was used to simultaneously detect 10 classes of deciduous teeth, 16 classes of permanent teeth, and four classes of tooth condition (excluding natural). We applied rule-based post-processing to the detected objects. Precision, Recall, and F1-score were used to evaluate our method, with an IoU (Intersection over Union) threshold set at 0.5.

resultsWe achieved Precision, Recall, and F1-score of 98.51%, 98.38%, and 98.45%, respectively, in tooth numbering. In tooth condition classification, the average F1-score across the 5 classes was 95.47%.

conclusionsOur method, which detects and classifies the tooth numbers of permanent and deciduous teeth and their tooth condition simultaneously, is expected to contribute to reducing the workload of dentists and improving accuracy.

Indexed as

Radiography, PanoramicToothTooth, DeciduousHumansImage Processing, Computer-AssistedComputer-aided diagnosisDental panoramic radiographsTooth condition classificationTooth numbering

Identifiers

PMID40632440

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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.