Evidence map›Paper›PMID 37553193›Full record

ArticleBMJ open2023

Artificial intelligence in detecting dentomaxillofacial fractures in diagnostic imaging: a scoping review protocol.

Silviana Farrah Diba, Dwi Cahyani Ratna Sari, Yana Supriatna, Igi Ardiyanto, Bagas Suryo Bintoro

Abstract read
In one paragraph

Article in BMJ open, 2023. 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. Review
  2. Article
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

5 authors.

Silviana Farrah DibaDoctorate Program of Medical and Health Science, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia.ORCID 0000-0002-8374-7622
Dwi Cahyani Ratna SariDepartment of Anatomy, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia dwi.cahyani@ugm.ac.id.ORCID 0000-0002-1126-4939
Yana SupriatnaDepartment of Radiology, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia.ORCID 0000-0003-3664-5495
Igi ArdiyantoDepartment of Electrical Engineering and Information Technology, Gadjah Mada University Faculty of Engineering, Yogyakarta, Indonesia.ORCID 0000-0002-0006-1458
Bagas Suryo BintoroDepartment of Health Behaviour, Environment, and Social Medicine, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia.ORCID 0000-0001-8419-8429

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe dentomaxillofacial (DMF) area, which includes the teeth, maxilla, mandible, zygomaticum, orbits and midface, plays a crucial role in the maintenance of the physiological functions despite its susceptibility to fractures, which are mostly caused by mechanical trauma. As a diagnostic tool, radiographic imaging helps clinicians establish a diagnosis and determine a treatment plan; however, the presence of human factors in image interpretation can result in missed detection of fractures. Therefore, an artificial intelligence (AI) computing system with the potential to help detect abnormalities on radiographic images is currently being developed. This scoping review summarises the literature and assesses the current status of AI in DMF fracture detection in diagnostic imaging. METHODS AND ANALYSIS: This proposed scoping review will be conducted using the framework of Arksey and O'Malley, with each step incorporating the recommendations of Levac ETHICS AND DISSEMINATION: This study does not require ethical approval because it analyses primary research articles. The research findings will be distributed through international conferences and peer-reviewed publications.

Indexed as

Artificial IntelligenceFractures, BoneHumansPeer ReviewResearch DesignScoping Reviews as TopicDiagnostic radiologyHead & neck imagingInformation technology

Identifiers

PMID37553193
PMCPMC10414106

What OpenQuestion holds

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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.