Evidence map›Paper›PMID 40421828›Full record

ArticleThe Laryngoscope2025

Development of an Open-Source Algorithm for Automated Segmentation in Clinician-Led Paranasal Sinus Radiologic Research.

Rhea Darbari Kaul, Wenjin Zhong, Sidong Liu, Ghasem Azemi, Kate Liang, Emma Zou, Peta-Lee Sacks, Cedric Thiel, Raewyn Gay Campbell, Larry Kalish and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Laryngoscope, 2025. 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. Article
  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

13 authors.

Rhea Darbari KaulRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0002-2945-6090
Wenjin ZhongCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.
Sidong LiuCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.ORCID 0000-0002-2371-0713
Ghasem AzemiComputational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.ORCID 0000-0002-8675-6858
Kate LiangRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.
Emma ZouRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.
Peta-Lee SacksRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0001-7828-8305
Cedric ThielRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0002-6557-908X
Raewyn Gay CampbellRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0002-6512-3613
Larry KalishRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0003-0227-8295
Raymond SacksRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0002-9260-5175
Antonio Di IevaComputational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.ORCID 0000-0002-5341-5416
Richard John HarveyRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0002-6942-8975

Funding

WOA Institution: Macquarie University
6 · The paper itself

Abstract

objectiveArtificial Intelligence (AI) research needs to be clinician led; however, expertise typically lies outside their skill set. Collaborations exist but are often commercially driven. Free and open-source computational algorithms and software expertise are required for meaningful clinically driven AI medical research. Deep learning algorithms automate segmenting regions of interest for analysis and clinical translation. Numerous studies have automatically segmented paranasal sinus computed tomography (CT) scans; however, openly accessible algorithms capturing the sinonasal cavity remain scarce. The purpose of this study was to validate and provide an open-source segmentation algorithm for paranasal sinus CTs for the otolaryngology research community.

methodsA cross-sectional comparative study was conducted with a deep learning algorithm, UNet++, modified for automatic segmentation of paranasal sinuses CTs and "ground-truth" manual segmentations. A dataset of 100 paranasal sinuses scans was manually segmented, with an 80/20 training/testing split. The algorithm is available at https://github.com/rheadkaul/SinusSegment. Primary outcomes included the Dice similarity coefficient (DSC) score, Intersection over Union (IoU), Hausdorff distance (HD), sensitivity, specificity, and visual similarity grading.

resultsTwenty scans representing 7300 slices were assessed. The mean DSC was 0.87 and IoU 0.80, with HD 33.61 mm. The mean sensitivity was 83.98% and specificity 99.81%. The median visual similarity grading score was 3 (good). There were no statistically significant differences in outcomes with normal or diseased paranasal sinus CTs.

conclusionAutomatic segmentation of CT paranasal sinuses yields good results when compared with manual segmentation. This study provides an open-source segmentation algorithm as a foundation and gateway for more complex AI-based analysis of large datasets. LEVEL OF EVIDENCE: 3:

Indexed as

AlgorithmsDeep LearningParanasal SinusesTomography, X-Ray ComputedArtificial IntelligenceCross-Sectional StudiesHumansMaleartificial intelligenceautomated segmentationCTradiomicsrhinology

Identifiers

PMID40421828
PMCPMC12645370

What OpenQuestion holds

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LicenceCC BY-NC
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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.