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.
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)
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
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
Cohort
Prospective
Not listed
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%.
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).
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.
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.
WL-NPV vs. NBI-NPV of CADx classification
Negative Predictive Value (NPV) of CADx classification calculated using only the three selected white-light frames, compared with definitive histopathology, vs. NPV of CADx classification calculated using only the three selected narrow-band imaging frames, compared with definitive histopathology. The final AI-result will be calculated based on the majority rule of the 3 WL and 3 NBI frames computed separately.
Clinician-Reported Usability Score for the AI Endoscopy System
Usability of the AI endoscopy system assessed using standardized usability questionnaires administered to clinicians after use of the AI system. Questionnaire scoring will be interpreted according to the selected questionnaire manual, with higher scores indicating greater usability.
Sensitivity, Specificity and Accuracy of CADx histology prediction
Sensitivity, Specificity and Accuracy of the CADx algorithm for histology prediction, compared with definitive histopathology.
F1 Score of CADx Classification
F1 score of the CADx classification output compared with definitive histopathology.
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Istituto Italiano di Tecnologia
Lead sponsor
Hospital Clinic of Barcelona
Collaborator
CRO "Centro Clinical Trials" IRCCS Ospedale Policlinico San Martino
Collaborator
Universitaire Ziekenhuizen KU Leuven
Collaborator
Università degli Studi 'G. d'Annunzio' Chieti e Pescara
Collaborator
Universita degli Studi di Genova
Collaborator
Scuola Superiore di Studi Universitari e di Perfezionamento Sant'Anna
Collaborator
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