Evidence map›Paper›PMID 35438326›Full record

ReviewClinical oral investigations2022

Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology.

Kuo Feng Hung, Qi Yong H Ai, Yiu Yan Leung, Andy Wai Kan Yeung

Abstract readReview
PubMed Publisher
In one paragraph

Review in Clinical oral investigations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
10.5field-weighted citation impact, top 1% of its field
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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.

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  9. Detection of maxillary sinus pathologies using deep learning algorithms.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
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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

4 authors at 2 institutions in 1 country.

Kuo Feng HungDivision of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-3971-3484
Qi Yong H AiDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.ORCID http://orcid.org/0000-0002-1155-4389
Yiu Yan LeungDivision of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-6670-6570
Andy Wai Kan YeungDivision of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China. ndyeung@hku.hk.ORCID http://orcid.org/0000-0003-3672-357X
University of Hong Kong · HKHong Kong Polytechnic University · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesNovel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR. MATERIALS AND

methodsA narrative review was conducted on the literature on AI algorithms in DMFR.

resultsIn the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping.

conclusionsFurther integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future. CLINICAL RELEVANCE: This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.

Indexed as

Dental CariesRadiologyAlgorithmsArtificial IntelligenceDeep LearningDentistsHumansNeural Networks, ComputerProfessional RoleArtificial intelligenceConvolutional neural networkDeep learningDento-maxillofacial radiology

Identifiers

PMID35438326
OpenAlexW4224233567

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.