Clinical trial · Observational
AI-Assisted Endoscopy for Upper Aerodigestive Tract Lesions
Head&Neck Application of Novel Computer-assisted Endoscopy
NCT07596355CI-TRIAL-00112033H&NANCEnot yet recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (9)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Head and Neck Cancer (H&N) | Malignant Head and Neck Neoplasm | ALIAS | 0.85 |
| Leukoplakia | — | UNRESOLVED | — |
| Squamous Cell Carcinoma | Squamous Cell Carcinoma | ONTOLOGY_EXACT | 0.98 |
| Upper AerodigestiveTract Cancer | — | UNRESOLVED | — |
| Upper Aerodigestive Tract Neoplasms | — | UNRESOLVED | — |
| Upper Aero-digestive Tract (UADT) Neoplasm | — | UNRESOLVED | — |
| Vocal Cord Disease | — | UNRESOLVED | — |
| Vocal Fold Nodules | — | UNRESOLVED | — |
| Vocal Fold Polyp |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (4)
- measure
- Negative Predictive Value of the CADx Algorithm for Malignant or Premalignant Upper Aerodigestive Tract Lesions
- timeFrame
- From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
- description
- 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%.
- measure
- Sensitivity of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions
- timeFrame
- At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
- description
- 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).
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * 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
References
Publications (7)
- BACKGROUNDDunham 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
- BACKGROUNDPiazza 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
- BACKGROUNDPaderno 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
- BACKGROUNDAzam 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
- BACKGROUNDRen 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
- BACKGROUNDKim 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