Evidence map›Paper›PMID 30881937›Full record

ArticleJournal of biomedical physics & engineering2019

Mandibular Trabecular Bone Analysis Using Local Binary Pattern for Osteoporosis Diagnosis.

L Khojastepour, M Hasani, M Ghasemi, A R Mehdizadeh, F Tajeripour

Open access · greenAbstract read
In one paragraph

Article in Journal of biomedical physics & engineering, 2019. 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
0.7field-weighted citation impact, top 30% 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

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

L KhojastepourDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
M HasaniDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
M GhasemiDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
A R MehdizadehDepartment of Medical Physics, School of Medicine, Shiraz University of Medical Sciences.
F TajeripourDepartment of Computer Engineering, Science and IT, Shiraz University, Shiraz, Iran.
Shiraz University of Medical Sciences · IRShiraz University · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOsteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and micro-architectural deterioration of bone tissue, leading to bone fragility and increased fracture risk. Since Panoramic image is a feasible and relatively routine imaging technique in dentistry; it could provide an opportunistic chance for screening osteoporosis. In this regard, numerous panoramic derived indices have been developed and suggested for osteoporosis screening. Jaw trabecular pattern is one of the main bone strength factors and trabecular bone pattern assessment is important factor in bone quality analysis. Texture analysis applied to trabecular bone images offers an ability to exploit the information present on conventional radiographs.

objectiveThe purpose of this study was to evaluate the relationship between Jaw trabecular pattern in panoramic image and osteoporosis based on image texture analyzing using local binary pattern. MATERIAL AND

methodsAn experiment is evaluated in this paper based on a real hand-captured database of panoramic radiograph images from osteoporosis and non-osteoporosis person in Namazi Hospital, Shiraz, Iran .An approach is proposed for osteoporosis diagnosis consisting of two steps. First, modified version of local binary patterns is used to extract discriminative features from jaw panoramic radiograph images. Then, classification is done using different classifiers.

resultsComparative results show that the proposed approach provides classification accuracy about 99.6%, which is higher than many state-of-the-art methods.

conclusionHigh classification accuracy, low computational complexity, multi-resolution and rotation invariant are among advantages of our proposed approach.

Indexed as

Local Binary PatternOsteoporosisPanoramicTexture Analysis

Identifiers

PMID30881937
PMCPMC6409375
OpenAlexW2836789552

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.