AI-Assisted Endoscopy for Upper Aerodigestive Tract Lesions

NCT07596355 | Not Yet Recruiting

• 1 other identifier: IIT_AIRCARE_H&NANCE
• Study Type: observational
• Target: 283
• Locations: 3 countries
• Sites: 3

Brief Summary

This is a prospective observational clinical study designed to evaluate the performance of artificial intelligence (AI) algorithms applied to upper aerodigestive tract (UADT) video-endoscopy. The study assesses three main tasks: lesion detection (localization), classification (benign vs malignant), and segmentation of tumor margins. AI algorithms will be applied to endoscopic video data acquired during routine clinical practice without influencing clinical decision-making. The system will process images in real time and store data for subsequent analysis. AI outputs will be compared with physician assessment and reference standard histopathology to evaluate diagnostic performance.

Conditions

Upper AerodigestiveTract Cancer; Upper Aero-digestive Tract (UADT) Neoplasm; Squamous Cell Carcinoma; Leukoplakia; Head and Neck Cancer (H&N); Vocal Cord Disease; Vocal Fold Polyp; Vocal Fold Nodules; Upper Aerodigestive Tract Neoplasms

Outcome Measures

Primary Outcomes (4)

• Negative Predictive Value of the CADx Algorithm for Malignant or Premalignant Upper Aerodigestive Tract Lesions

  Negative Predictive Value (NPV) of the computer-aided diagnosis (CADx) algorithm for classifying UADT lesions as malignant/premalignant versus benign/non-neoplastic, using definitive histopathology as the reference standard. The CADx final classification will be based on the majority rule across selected white-light and narrow-band imaging frames. NPV = true negatives / (true negatives + false negatives). The pre-specified performance target is NPV ≥ 90%.

  From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.

• Sensitivity of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions

  Sensitivity of the computer-aided detection (CADe) algorithm for localizing UADT lesions with a bounding box. A true positive is defined as localization of the lesion area by a bounding box in the majority of physician-labeled lesion-positive captured frames. Sensitivity = true positives / (true positives + false negatives).

  At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.

• Median Intersection Over Union Between CASe Segmentation and Surgeon-Drawn Lesion Margins

  Median overlap between the AI-generated segmentation mask and the lesion margin area drawn by the surgeon on intraoperative endoscopic images. Intersection over Union (IoU) = area of overlap / area of union. Values range from 0 to 1; higher values indicate greater agreement.

  At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.

• Median Dice Similarity Coefficient Between CASe Segmentation and Surgeon-Drawn Lesion Margins

  Median Dice Similarity Coefficient (DSC) between the AI-generated segmentation mask and the lesion margin area drawn by the surgeon on intraoperative endoscopic images. Dice Similarity Coefficient = 2 × area of overlap / (AI segmented area + surgeon-drawn area). Values range from 0 to 1; higher values indicate greater agreement.

  At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.

Secondary Outcomes (11)

• WL-NPV vs. NBI-NPV of CADx classification

  From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.

• Clinician-Reported Usability Score for the AI Endoscopy System

  Assessed after clinician use of the AI system during study procedures, up to 20 months after study initiation.

• Sensitivity, Specificity and Accuracy of CADx histology prediction

  From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.

• F1 Score of CADx Classification

  From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.

• Area Under the Receiver Operating Characteristic Curve of CADx Classification

  From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The sample size for a single-arm, prospective cohort study, where the same group of patients undergoes testing with an AI model and a human physician and then they are compared with the gold standard reference test (biopsy) was calculated based on information from previous studies

You may qualify if:

• Age \> 18 years
• Injury originating from the upper aero-digestive tract
• Recording of the video-endoscopic examination
• Patient known to undergo a biopsy of the lesion or clinical follow-up for lesion with known biopsy (e.g. laryngeal papillomatosis) or suffering from Reinke's edema (in this pathology, in fact, biopsy is not necessary since the diagnosis is clinical)
• Or patients undergoing transoral lesion excision

You may not qualify if:

• Submucosal lesion
• Patients with previous operations on the upper aero-digestive tract
• Patients with previous radiotherapy of the head and neck district
• Poor compliance on endoscopic examination
• Unavailability of CADe/CADx or CASe data logging note

Sponsors & Collaborators

• Istituto Italiano di Tecnologia: lead
• Hospital Clinic of Barcelona: collaborator
• Universitaire Ziekenhuizen KU Leuven: collaborator
• Università degli Studi 'G. d'Annunzio' Chieti e Pescara: collaborator
• Universita degli Studi di Genova: collaborator
• Scuola Superiore Sant'Anna di Pisa: collaborator
• CRO "Centro Clinical Trials" IRCCS Ospedale Policlinico San Martino: collaborator

Study Sites (3)

UZ Leuven

Leuven, Flemish Brabant, 3000, Belgium

Location

IRCCS Ospedale Policlinico San Martino

Genova, GE, 16131, Italy

Location

Hospital Clínic de Barcelona

Barcelona, Barcelona, 08036, Spain

Location

Related Publications (7)

• Dunham ME, Kong KA, McWhorter AJ, Adkins LK. Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network. Laryngoscope. 2022 Feb;132 Suppl 4:S1-S8. doi: 10.1002/lary.28708. Epub 2020 Apr 28.

  PMID: 32343434 BACKGROUND

• Piazza C, Peretti G, Vander Poorten V. Editorial: Advances in Transoral Approaches for Laryngeal Cancer. Front Oncol. 2018 Oct 17;8:455. doi: 10.3389/fonc.2018.00455. eCollection 2018. No abstract available.

  PMID: 30386742 BACKGROUND

• Paderno A, Piazza C, Del Bon F, Lancini D, Tanagli S, Deganello A, Peretti G, De Momi E, Patrini I, Ruperti M, Mattos LS, Moccia S. Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective. Front Oncol. 2021 Mar 24;11:626602. doi: 10.3389/fonc.2021.626602. eCollection 2021.

  PMID: 33842330 BACKGROUND

• Azam MA, Sampieri C, Ioppi A, Africano S, Vallin A, Mocellin D, Fragale M, Guastini L, Moccia S, Piazza C, Mattos LS, Peretti G. Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection. Laryngoscope. 2022 Sep;132(9):1798-1806. doi: 10.1002/lary.29960. Epub 2021 Nov 25.

  PMID: 34821396 BACKGROUND

• Ren J, Jing X, Wang J, Ren X, Xu Y, Yang Q, Ma L, Sun Y, Xu W, Yang N, Zou J, Zheng Y, Chen M, Gan W, Xiang T, An J, Liu R, Lv C, Lin K, Zheng X, Lou F, Rao Y, Yang H, Liu K, Liu G, Lu T, Zheng X, Zhao Y. Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique. Laryngoscope. 2020 Nov;130(11):E686-E693. doi: 10.1002/lary.28539. Epub 2020 Feb 18.

  PMID: 32068890 BACKGROUND

• Kim DH, Kim Y, Kim SW, Hwang SH. Use of narrowband imaging for the diagnosis and screening of laryngeal cancer: A systematic review and meta-analysis. Head Neck. 2020 Sep;42(9):2635-2643. doi: 10.1002/hed.26186. Epub 2020 May 4.

  PMID: 32364313 BACKGROUND

• Rex DK, Kahi C, O'Brien M, Levin TR, Pohl H, Rastogi A, Burgart L, Imperiale T, Ladabaum U, Cohen J, Lieberman DA. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2011 Mar;73(3):419-22. doi: 10.1016/j.gie.2011.01.023.

  PMID: 21353837 BACKGROUND

MeSH Terms

Conditions

Head and Neck Neoplasms; Neoplasms; Carcinoma, Squamous Cell; Leukoplakia

Condition Hierarchy (Ancestors)

Neoplasms by Site; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms, Squamous Cell; Precancerous Conditions; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Leonardo De Mattos, PhD

CONTACT

+39 010 2898 270; leonardo.demattos@iit.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 5, 2026
• First Posted: May 19, 2026
• Study Start: May 1, 2026
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): February 1, 2028
• Last Updated: May 19, 2026

Record last verified: 2026-05

Locations

IT (1); BE (1); ES (1)