Evidence map›Paper›PMID 40448917›Full record

ArticleEuropean archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry2025

Artificial intelligence model for application in dental traumatology.

T Bani-Hani, M Wedyan, R Al-Fodeh, R Shuqeir, S Al Jundi, N Tewari

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Article in European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
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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.

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

T Bani-HaniDivision of Pediatric Dentistry, Preventive Dentistry Department, Faculty of Dentistry, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan. tgbanihani@just.edu.jo.ORCID http://orcid.org/0000-0002-0429-8786
M WedyanDepartment of Computer Sciences, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan.
R Al-FodehDepartment of Prosthodontics, Faculty of Dentistry, Jordan University of Science and Technology, Irbid, 22110, Jordan.
R ShuqeirPediatric Dentistry, Jordan University of Science and Technology, Irbid, 22110, Jordan.
S Al JundiDivision of Pediatric Dentistry, Preventive Dentistry Department, Faculty of Dentistry, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan.
N TewariPediatric and Preventive Dentistry, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, 110029, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn recent years, healthcare systems have witnessed a tremendous advancement in diagnostic tools and technologies. The advent of artificial intelligence (AI) has enabled a paradigm shift in the practice of health sciences particularly in medicine. In the dental field, AI has been scarcely used in the various disciplines with no application in dental traumatology. This study proposes a deep-learning, convolutional neural networks (CNN)-based model for detection and classification of dental fractures.

methodsPlain periapical radiographs of injured teeth were retrieved from patients' records and annotated by two dentists trained in dental traumatology. The teeth were categorised into four groups: uncomplicated crown fractures, complicated crown fractures, crown-root fractures and root fractures. Data augmentation was done to enhance the power of the current dataset. Images were divided into training (80%) and test (20%) datasets. Python programming language was used to implement the CNN-based classification model. Cross validation was applied.

resultsA total of 72 plain periapical radiographs of 108 fractured teeth were collected. The model achieved high accuracy in differentiating uncomplicated crown fractures from complicated ones (96.0%), from crown-root fractures (99.1%) and from root fractures (98.7%). Furthermore, the complicated injuries were distinguished from crown-root fractures and from root fractures with accuracy levels at 96.3% and 97.2% respectively. The model's overall accuracy in  recognising the four classes was 78.7%.

conclusionThe proposed model showed excellent performance in the classification of dental fractures. The application of AI in paediatric dentistry, particularly in the field of dental trauma, is innovative and highly relevant to current trends in healthcare technology. Expansion of the current model to a larger dataset that includes the various types of injuries is recommended in future research. Such models can be a great asset for the less-experienced dentists in making accurate diagnosis and timely decisions. Future models employing panoramic radiographs could also help the medical practitioners at emergency services.

Indexed as

Artificial IntelligenceDeep LearningNeural Networks, ComputerTooth FracturesTooth InjuriesHumansTooth CrownTooth RootArtificial intelligenceCNNsConvolutional neural networksDeep learningDental fractures

Identifiers

PMID40448917

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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.