AI-Assisted Endoscopy for Upper Aerodigestive Tract Lesions

ClinicalTrials.gov ID

NCT07596355

Lead Sponsor

Istituto Italiano di TecnologiaOTHER

Overall Status

Not yet recruiting

Study Type

Observational

Enrollment

283Estimated

Last Update Posted

May 19, 2026Actual

Start Date

May 2026Estimated

Primary Completion Date

Dec 2027Estimated

Completion Date

Feb 2028Estimated

Official Title

Head&Neck Application of Novel Computer-assisted Endoscopy

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.

Detailed Description

The artificial intelligence algorithms developed will be employed in the analysis of laryngeal lesions for 3 tasks:

• Task 1: Computer aided diagnosis (CADx): the algorithm provides a differential diagnosis between benign and malignant neoplasms (binary classification) and the exact histology (multiclass classification). During the UADT video-endoscopy in the outpatient clinic, the physician performs the video-endoscopy and selects and captures n.3 WL and n.3 NBI significant frames of the lesion. The AI model records the classification output of the algorithm that the physician cannot access. The predicted pathologic results will be finally displayed as two different classifications along with the probability of each prediction (0% to 100%) as estimated by the AI algorithm: a first binary classification "neoplastic" or "non-neoplastic," and a second multiclass classification with the exact histology. The physician subsequently, based on the endoscopic examination, will write the suspected diagnosis (benign vs. malignant lesion and the actual histology) in the appropriate patient chart. Next, the physician reviews the screenshot taken and makes sure the lesion is visible in every one of them. Retrospectively, an investigator (blinded to the physician's assessment) will review the AI processed frames with the resulting CADx classifications and mark the AI-processed diagnosis in the patient chart. Once biopsied, the final histology of the lesion analyzed by definitive histopathological examination is recorded in the patient chart by the investigator. The investigators will finally compare the two recorded diagnoses (CADx and physician) with the definitive histology.
• Task 2: Computer aided detection (CADe): the algorithm, through the representation of a rectangle (bounding box), localizes the lesion during the video-endoscopy in the outpatient clinic in real-time. During the UADT video-endoscopy, the physician performs the video-endoscopy as for standard-of-care procedure. In parallel, the AI model processes in real-time the endoscopic video and records the output of the algorithm (which the physician cannot access). The physician captures n.3 WL and n.3 NBI significant frames of the lesion. Moreover, n.3 frames where no lesions are visible are captured as negative controls. Later, the physician reviews the screenshot taken and makes sure to label the frames where the lesion is visible as "positive cases" and the frame where the lesion is not visible as "negative cases". The investigators will finally assess if the lesion was detected by the CADe system to define a "true positive". Similarly, to define a true negative, the CADe system should have not output a bounding box in the majority of the "negative cases" frames.
• Task 3: Computer aided segmentation (CASe): the algorithm analyzes the neoplasm margins and provides a delineation mask. In the operating room setting, once the lesion to be resected is identified with a 0° telescope, the surgeon captures n.1 WL and n.1 NBI close-up photographs that exemplify the superficial lesion margins. The same procedure is repeated with a 70° optics and other two photographs are acquired. The frames taken are then saved and analyzed by the AI algorithm, which will perform the segmentation task. The surgeon will be blinded to the AI prediction. Later, the surgeon will draw the margins of the lesion according to her/his evaluation of each captured frame. The annotated frame will be saved so that it can be analyzed at a later time. Afterwards, in cases where positive superficial margins are identified by histopathologic examination, the surgeon-designed margins and the AI model ones will be compared to see if there was any difference in the suggested margin.

• Upper Aerodigestive Tract Neoplasms
• 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

Ages Eligible for Study

Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

No

Eligibility Criteria

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

Observational Model

Cohort

Time Perspective

Prospective

Lead Sponsor

Istituto Italiano di TecnologiaOTHER

Collaborators

Hospital Clinic of BarcelonaOTHER

CRO "Centro Clinical Trials" IRCCS Ospedale Policlinico San MartinoUNKNOWN

Universitaire Ziekenhuizen KU LeuvenOTHER

Università degli Studi 'G. d'Annunzio' Chieti e PescaraOTHER

Universita degli Studi di GenovaOTHER

Scuola Superiore di Studi Universitari e di Perfezionamento Sant'AnnaOTHER

Francesco.Mora@unige.it+39 010-5557479

claudio.sampieri@outlook.com+34 93 227 57 66

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