Evidence map›Paper›PMID 37509513›Full record

ArticleBiomedicines2023

An Automated Broncho-Arterial (BA) Pair Segmentation Process and Assessment of BA Ratios in Children with Bronchiectasis Using Lung HRCT Scans: A Pilot Study.

Sami Azam, Sidratul Montaha, A K M Rakibul Haque Rafid, Asif Karim, Mirjam Jonkman, Friso De Boer, Gabrielle McCallum, Ian Brent Masters, Anne Chang

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

9 authors at 3 institutions in 1 country.

Sami AzamFaculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.ORCID 0000-0001-7572-9750
Sidratul MontahaFaculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.ORCID 0000-0002-5276-3793
A K M Rakibul Haque RafidFaculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.ORCID 0000-0002-6236-2352
Asif KarimFaculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.ORCID 0000-0001-8532-6816
Mirjam JonkmanFaculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.
Friso De BoerFaculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.
Gabrielle McCallumChild Health Division, Menzies School of Health Research, Darwin, NT 0811, Australia.
Ian Brent MastersAustralian Centre for Health Services Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia.ORCID 0000-0002-3053-2281
Anne ChangChild Health Division, Menzies School of Health Research, Darwin, NT 0811, Australia.ORCID 0000-0002-1331-3706
Charles Darwin University · AUQueensland University of Technology · AUMenzies School of Health Research · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bronchiectasis in children can progress to a severe lung condition if not diagnosed and treated early. The radiological diagnostic criteria for the diagnosis of bronchiectasis is an increased broncho-arterial (BA) ratio. From high-resolution computed tomography (HRCT) scans, the BA pairs must be detected first to derive the BA ratio. This study aims to identify potential BA pairs from HRCT scans of children undertaken to evaluate suppurative lung disease through an automated approach. After segmenting the lung regions, the HRCT scans are cleaned using a histogram analysis-based approach followed by a potential arteries identification process comprising four conditions based on imaging features. Potential arteries and their connected components are extracted, and potential bronchi are identified. Finally, the coordinates of potential arteries and potential bronchi are matched as the last step of BA pairs extraction. A total of 8-50 BA pairs are detected for each patient. Additionally, the area and several diameters of the bronchi and arteries are measured, and BA ratios based on these are calculated. Through this approach, the BA pairs of a CT scan datasets are detected and utilizing a deep learning model, a high classification test accuracy of 98.53% is achieved, validating the robustness of the proposed BA detection approach. The results show that visible BA pairs can be identified and segmented automatically, and the BA ratio calculated may help diagnose bronchiectasis with less effort and time.

Indexed as

broncho-arterial pairbroncho-arterial ratioHRCT scansimage processing

Identifiers

PMID37509513
PMCPMC10376950
OpenAlexW4382753539

What OpenQuestion holds

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LicenceCC BY
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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.