Evidence map›Paper›PMID 30206745›Full record

ArticleOral and maxillofacial surgery2018

A novel noise filtered and occlusion removal: navigational accuracy in augmented reality-based constructive jaw surgery.

Bijaya Raj Basnet, Abeer Alsadoon, Chandana Withana, Anand Deva, Manoranjan Paul

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Article in Oral and maxillofacial surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.1field-weighted citation impact, top 9% 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

3 citing papers in PubMed, 31 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Bijaya Raj BasnetSchool of Computing and Mathematics, Charles Sturt University, Sydney Campus, Sydney, Australia.
Abeer AlsadoonSchool of Computing and Mathematics, Charles Sturt University, Sydney Campus, Sydney, Australia.
Chandana WithanaSchool of Computing and Mathematics, Charles Sturt University, Sydney Campus, Sydney, Australia. cwithana@studygroup.com.
Anand DevaFaculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
Manoranjan PaulSchool of Computing and Mathematics, Charles Sturt University, Sydney Campus, Sydney, Australia.
Charles Sturt University · AUMacquarie University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAugmented reality-based constructive jaw surgery has been facing various limitations such as noise in real-time images, the navigational error of implants and jaw, image overlay error, and occlusion handling which have limited the implementation of augmented reality (AR) in corrective jaw surgery. This research aimed to improve the navigational accuracy, through noise and occlusion removal, during positioning of an implant in relation to the jaw bone to be cut or drilled.

methodThe proposed system consists of a weighting-based de-noising filter and depth mapping-based occlusion removal for removing any occluded object such as surgical tools, the surgeon's body parts, and blood.

resultsThe maxillary (upper jaw) and mandibular (lower jaw) jaw bone sample results show that the proposed method can achieve the image overlay error (video accuracy) of 0.23~0.35 mm and processing time of 8-12 frames per second compared to 0.35~0.45 mm and 6-11 frames per second by the existing best system.

conclusionThe proposed system concentrates on removing the noise from the real-time video frame and the occlusion. Thus, the acceptable range of accuracy and the processing time are provided by this study for surgeons for carrying out a smooth surgical flow.

Indexed as

AlgorithmsHumansImaging, Three-DimensionalMandibleMandibular ReconstructionMaxillaRadiography, InterventionalSurgery, Computer-AssistedVideo-Assisted Surgery3D-2D matchingAugmented reality navigationImage registrationNoise removalOcclusion handling

Identifiers

PMID30206745
OpenAlexW2889737481

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.