Evidence map›Paper›PMID 36528684›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

Computer-based airway stenosis quantification from bronchoscopic images: preliminary results from a feasibility trial.

Artur Banach, Masahito Naito, Franklin King, Fumitaro Masaki, Hisashi Tsukada, Nobuhiko Hata

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Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Automated vision-based assistance tools in bronchoscopy: stenosis severity estimation.International journal of computer assisted radiology and surgery · 2025
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Artur BanachNational Center for Image-guided Therapy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. abanach@bwh.harvard.edu.ORCID http://orcid.org/0000-0003-2622-3301
Masahito NaitoNational Center for Image-guided Therapy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Franklin KingNational Center for Image-guided Therapy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Fumitaro MasakiNational Center for Image-guided Therapy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Hisashi TsukadaDivision of Thoracic Surgery, Department of Surgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Nobuhiko HataNational Center for Image-guided Therapy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAirway Stenosis (AS) is a condition of airway narrowing in the expiration phase. Bronchoscopy is a minimally invasive pulmonary procedure used to diagnose and/or treat AS. The AS quantification in a form of the Stenosis Index (SI), whether subjective or digital, is necessary for the physician to decide on the most appropriate form of treatment. The literature reports that the subjective SI estimation is inaccurate. In this paper, we propose an approach to quantify the SI defining the level of airway narrowing, using depth estimation from a bronchoscopic image.

methodsIn this approach we combined a generative depth estimation technique combined with depth thresholding to provide Computer-based AS quantification. We performed an interim clinical analysis by comparing AS quantification performance of three expert bronchoscopists against the proposed Computer-based method on seven patient datasets.

resultsThe Mean Absolute Error of the subjective Human-based and the proposed Computer-based SI estimation was [Formula: see text] [%] and [Formula: see text] [%], respectively. The correlation coefficients between the CT measurements were used as the gold standard, and the Human-based and Computer-based SI estimation were [Formula: see text] and 0.46, respectively.

conclusionsWe presented a new computer method to quantify the severity of AS in bronchoscopy using depth estimation and compared the performance of the method against a human-based approach. The obtained results suggest that the proposed Computer-based AS quantification is a feasible tool that has the potential to provide significant assistance to physicians in bronchoscopy.

Indexed as

BronchoscopyComputersConstriction, PathologicFeasibility StudiesHumansAirway stenosis quantificationBronchoscopyImage-guided surgery

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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.