000160989 001__ 160989
000160989 005__ 20251017144615.0
000160989 0247_ $$2doi$$a10.1007/s11548-025-03398-x
000160989 0248_ $$2sideral$$a144166
000160989 037__ $$aART-2025-144166
000160989 041__ $$aeng
000160989 100__ $$0(orcid)0009-0001-2112-0939$$aTomasini, Clara$$uUniversidad de Zaragoza
000160989 245__ $$aAutomated vision-based assistance tools in bronchoscopy: stenosis severity estimation
000160989 260__ $$c2025
000160989 5060_ $$aAccess copy available to the general public$$fUnrestricted
000160989 5203_ $$aPurpose
Subglottic stenosis refers to the narrowing of the subglottis, the airway between the vocal cords and the trachea. Its severity is typically evaluated by estimating the percentage of obstructed airway. This estimation can be obtained from CT data or through visual inspection by experts exploring the region. However, visual inspections are inherently subjective, leading to less consistent and robust diagnoses. No public methods or datasets are currently available for automated evaluation of this condition from bronchoscopy video.
Methods
We propose a pipeline for automated subglottic stenosis severity estimation during the bronchoscopy exploration, without requiring the physician to traverse the stenosed region. Our approach exploits the physical effect of illumination decline in endoscopy to segment and track the lumen and obtain a 3D model of the airway. This 3D model is obtained from a single frame and is used to measure the airway narrowing.
Results
Our pipeline is the first to enable automated and robust subglottic stenosis severity measurement using bronchoscopy images. The results show consistency with ground-truth estimations from CT scans and expert estimations and reliable repeatability across multiple estimations on the same patient. Our evaluation is performed on our new Subglottic Stenosis Dataset of real bronchoscopy procedures data.
Conclusion
We demonstrate how to automate evaluation of subglottic stenosis severity using only bronchoscopy. Our approach can assist with and shorten diagnosis and monitoring procedures, with automated and repeatable estimations and less exploration time, and save radiation exposure to patients as no CT is required. Additionally, we release the first public benchmark for subglottic stenosis severity assessment.
000160989 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/EC/H2020/863146/EU/EndoMapper: Real-time mapping from endoscopic video/EndoMapper$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 863146-EndoMapper
000160989 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000160989 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000160989 700__ $$aRodriguez-Puigvert, Javier$$uUniversidad de Zaragoza
000160989 700__ $$aPolanco, Dinora
000160989 700__ $$aViñuales, Manuel$$uUniversidad de Zaragoza
000160989 700__ $$0(orcid)0000-0002-6722-5541$$aRiazuelo, Luis$$uUniversidad de Zaragoza
000160989 700__ $$0(orcid)0000-0002-7580-9037$$aMurillo, Ana C.$$uUniversidad de Zaragoza
000160989 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000160989 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000160989 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000160989 773__ $$g(2025), [8 p.]$$tInternational Journal of Computer Assisted Radiology and Surgery$$x1861-6429
000160989 8564_ $$s3042805$$uhttps://zaguan.unizar.es/record/160989/files/texto_completo.pdf$$yVersión publicada
000160989 8564_ $$s2336093$$uhttps://zaguan.unizar.es/record/160989/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000160989 909CO $$ooai:zaguan.unizar.es:160989$$particulos$$pdriver
000160989 951__ $$a2025-10-17-14:19:00
000160989 980__ $$aARTICLE