AI-Assisted Endoscopy for Upper Aerodigestive Tract Lesions (H&NANCE)
12 de maio de 2026 atualizado por: Istituto Italiano di Tecnologia
Head&Neck Application of Novel Computer-assisted Endoscopy
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.
Visão geral do estudo
Status
Ainda não está recrutando
Descrição detalhada
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.
Tipo de estudo
Observacional
Inscrição (Estimado)
283
Contactos e Locais
Esta seção fornece os detalhes de contato para aqueles que conduzem o estudo e informações sobre onde este estudo está sendo realizado.
Contato de estudo
- Nome: Leonardo De Mattos, PhD
- Número de telefone: +39 010 2898 270
- E-mail: leonardo.demattos@iit.it
Locais de estudo
-
-
Flemish Brabant
-
Leuven, Flemish Brabant, Bélgica, 3000
- UZ Leuven
-
Contato:
- Vincent Vander Poorten, Prof. and ENT Surgeon
- Número de telefone: +32 16 33 23 42
- E-mail: vincent.vanderpoorten@uzleuven.be
-
Investigador principal:
- Vincent Vander Poorten, MD PhD
-
-
-
-
Barcelona
-
Barcelona, Barcelona, Espanha, 08036
- Hospital Clinic de Barcelona
-
Contato:
- Claudio Sampieri, Otorhinolaryngologist
- Número de telefone: +34 93 227 57 66
- E-mail: claudio.sampieri@outlook.com
-
Investigador principal:
- Claudio Sampieri, MD PhD
-
-
-
-
GE
-
Genova, GE, Itália, 16131
- IRCCS Ospedale Policlinico San Martino
-
Contato:
- Francesco Mora, Otorhinolaryngologist and Prof
- Número de telefone: +39 010-5557479
- E-mail: Francesco.Mora@unige.it
-
Investigador principal:
- Francesco Mora, MD PhD
-
-
Critérios de participação
Os pesquisadores procuram pessoas que se encaixem em uma determinada descrição, chamada de critérios de elegibilidade. Alguns exemplos desses critérios são a condição geral de saúde de uma pessoa ou tratamentos anteriores.
Critérios de elegibilidade
Idades elegíveis para estudo
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Não
Método de amostragem
Amostra Não Probabilística
População do estudo
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
Descrição
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
Plano de estudo
Esta seção fornece detalhes do plano de estudo, incluindo como o estudo é projetado e o que o estudo está medindo.
Como o estudo é projetado?
Detalhes do projeto
O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
Negative Predictive Value of the CADx Algorithm for Malignant or Premalignant Upper Aerodigestive Tract Lesions
Prazo: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
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
Prazo: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
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
Prazo: At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.
|
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
Prazo: At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.
|
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.
|
Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
WL-NPV vs. NBI-NPV of CADx classification
Prazo: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
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. |
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
Prazo: Assessed after clinician use of the AI system during study procedures, up to 20 months after study initiation.
|
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.
|
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
Prazo: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
Sensitivity, Specificity and Accuracy of the CADx algorithm for histology prediction, compared with definitive histopathology.
|
From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
|
F1 Score of CADx Classification
Prazo: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
F1 score of the CADx classification output compared with definitive histopathology.
|
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
Prazo: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
AUC of the ROC curve for CADx classification of UADT lesions compared with definitive histopathology.
|
From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
|
Sensitivity, Specificity and Accuracy of human physician histology prediction
Prazo: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
Sensitivity, Specificity an Accuracy of the treating physician's suspected diagnosis compared with definitive histopathology.
|
From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.
|
|
Specificity of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions
Prazo: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
Proportion of physician-labeled lesion-negative frames/cases in which the CADe algorithm does not output a bounding box in the majority of negative-control frames.
|
At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
|
Accuracy of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions
Prazo: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
Overall proportion of correctly classified lesion-positive and lesion-negative cases/frames by the CADe algorithm.
|
At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
|
Positive Predictive Value of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions
Prazo: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
Positive predictive value of CADe bounding-box output for lesion localization.
PPV = true positives / (true positives + false positives).
|
At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
|
Negative Predictive Value of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions
Prazo: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
Negative predictive value of CADe absence of bounding-box output for lesion localization.
NPV = true negatives / (true negatives + false negatives).
|
At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.
|
|
Percentage of Positive Superficial Margin Cases in Which the AI-Predicted Tumor Area Is Wider Than the Surgeon-Drawn Area
Prazo: At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.
|
Among cases with positive superficial margins on final histopathology, percentage of cases in which the AI-predicted tumor area extends beyond the surgeon-drawn margin at the affected margin.
|
At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.
|
Colaboradores e Investigadores
É aqui que você encontrará pessoas e organizações envolvidas com este estudo.
Patrocinador
Publicações e links úteis
A pessoa responsável por inserir informações sobre o estudo fornece voluntariamente essas publicações. Estes podem ser sobre qualquer coisa relacionada ao estudo.
Publicações Gerais
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Datas de registro do estudo
Essas datas acompanham o progresso do registro do estudo e os envios de resumo dos resultados para ClinicalTrials.gov. Os registros do estudo e os resultados relatados são revisados pela National Library of Medicine (NLM) para garantir que atendam aos padrões específicos de controle de qualidade antes de serem publicados no site público.
Datas Principais do Estudo
Início do estudo (Estimado)
1 de maio de 2026
Conclusão Primária (Estimado)
1 de dezembro de 2027
Conclusão do estudo (Estimado)
1 de fevereiro de 2028
Datas de inscrição no estudo
Enviado pela primeira vez
5 de maio de 2026
Enviado pela primeira vez que atendeu aos critérios de CQ
12 de maio de 2026
Primeira postagem (Real)
19 de maio de 2026
Atualizações de registro de estudo
Última Atualização Postada (Real)
19 de maio de 2026
Última atualização enviada que atendeu aos critérios de controle de qualidade
12 de maio de 2026
Última verificação
1 de maio de 2026
Mais Informações
Termos relacionados a este estudo
Termos MeSH relevantes adicionais
- Neoplasias por local
- Condições Patológicas, Anatômicas
- Neoplasias por Tipo Histológico
- Neoplasias Glandulares e Epiteliais
- Carcinoma
- Neoplasias de Células Escamosas
- Condições pré-cancerosas
- Condições Patológicas, Sinais e Sintomas
- Neoplasias
- Carcinoma de Células Escamosas
- Neoplasias de Cabeça e Pescoço
- Leucoplasia
Outros números de identificação do estudo
- IIT_AIRCARE_H&NANCE
Plano para dados de participantes individuais (IPD)
Planeja compartilhar dados de participantes individuais (IPD)?
INDECISO
Informações sobre medicamentos e dispositivos, documentos de estudo
Estuda um medicamento regulamentado pela FDA dos EUA
Não
Estuda um produto de dispositivo regulamentado pela FDA dos EUA
Não
Essas informações foram obtidas diretamente do site clinicaltrials.gov sem nenhuma alteração. Se você tiver alguma solicitação para alterar, remover ou atualizar os detalhes do seu estudo, entre em contato com register@clinicaltrials.gov. Assim que uma alteração for implementada em clinicaltrials.gov, ela também será atualizada automaticamente em nosso site .