Evidence map›Paper›PMID 39125563›Full record

ArticleDiagnostics (Basel, Switzerland)2024

Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs.

Tai-Jung Lin, Yi-Cheng Mao, Yuan-Jin Lin, Chin-Hao Liang, Yi-Qing He, Yun-Chen Hsu, Shih-Lun Chen, Tsung-Yi Chen, Chiung-An Chen, Kuo-Chen Li and 1 more

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In one paragraph

Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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.

Tai-Jung LinDepartment of Periodontics, Division of Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan.
Yi-Cheng MaoDepartment of Operative Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan.
Yuan-Jin LinDepartment of Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan.
Chin-Hao LiangDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan.
Yi-Qing HeDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan.
Yun-Chen HsuDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan.
Shih-Lun ChenDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan.ORCID 0000-0002-4079-9350
Tsung-Yi ChenDepartment of Electronic Engineering, Feng Chia University, Taichung City 407301, Taiwan.
Chiung-An ChenDepartment of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan.ORCID 0000-0002-7605-5214
Kuo-Chen LiDepartment of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan.ORCID 0000-0002-0110-5491
Patricia Angela R AbuAteneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines.ORCID 0000-0002-8848-6644

Funding

Ministry of Science and Technology (MOST), Taiwan MOST-106-2119-M-033-001Ministry of Science and Technology (MOST), Taiwan MOST-106-2221-E-033-072Ministry of Science and Technology (MOST), Taiwan MOST-106-2622-E-033-014-CC2Ministry of Science and Technology (MOST), Taiwan MOST 107-2112-M-131-001Ministry of Science and Technology (MOST), Taiwan MOST-107-2218-E-131-002Ministry of Science and Technology (MOST), Taiwan MOST-107-2221-E-033-057Ministry of Science and Technology (MOST), Taiwan MOST-107-2622-E-131-007-CC3Ministry of Science and Technology (MOST), Taiwan MOST-109-2410-H-197-002-MY3Ministry of Science and Technology (MOST), Taiwan MOST-112-2410-H-033-014
6 · The paper itself

Abstract

The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth's bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research.

Indexed as

alveolar crestapical periodontitiscemento-enamel junctionMask R-CNNobject detection

Identifiers

PMID39125563
PMCPMC11312231

What OpenQuestion holds

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LicenceCC BY
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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.