Esta página se tradujo automáticamente y no se garantiza la precisión de la traducción. por favor refiérase a versión inglesa para un texto fuente.

AI-Assisted Endoscopy for Upper Aerodigestive Tract Lesions (H&NANCE)

12 de mayo de 2026 actualizado 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.

Descripción general del estudio

Estado

Aún no reclutando

Condiciones

Descripción detallada

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 estudio

De observación

Inscripción (Estimado)

283

Contactos y Ubicaciones

Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.

Estudio Contacto

Ubicaciones de estudio

  • Bélgica
    • Flemish Brabant
      • Leuven, Flemish Brabant, Bélgica, 3000
        • UZ Leuven
        • Contacto:
        • Investigador principal:
          • Vincent Vander Poorten, MD PhD
  • España
    • Barcelona
      • Barcelona, Barcelona, España, 08036
        • Hospital Clinic de Barcelona
        • Contacto:
        • Investigador principal:
          • Claudio Sampieri, MD PhD
  • Italia
    • GE
      • Genova, GE, Italia, 16131
        • IRCCS Ospedale Policlinico San Martino
        • Contacto:
          • Francesco Mora, Otorhinolaryngologist and Prof
          • Número de teléfono: +39 010-5557479
          • Correo electrónico: Francesco.Mora@unige.it
        • Investigador principal:
          • Francesco Mora, MD PhD

Criterios de participación

Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.

Criterio de elegibilidad

Edades elegibles para estudiar

  • Adulto
  • Adulto Mayor

Acepta Voluntarios Saludables

No

Método de muestreo

Muestra no probabilística

Población de estudio

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

Descripción

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

Plan de estudios

Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.

¿Cómo está diseñado el estudio?

Detalles de diseño

¿Qué mide el estudio?

Medidas de resultado primarias

Medida de resultado
Medida Descripción
Periodo de tiempo
Negative Predictive Value of the CADx Algorithm for Malignant or Premalignant Upper Aerodigestive Tract Lesions
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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 resultado secundarias

Medida de resultado
Medida Descripción
Periodo de tiempo
WL-NPV vs. NBI-NPV of CADx classification
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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

Aquí es donde encontrará personas y organizaciones involucradas en este estudio.

Patrocinador

Istituto Italiano di Tecnologia

Colaboradores

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

Publicaciones y enlaces útiles

La persona responsable de ingresar información sobre el estudio proporciona voluntariamente estas publicaciones. Estos pueden ser sobre cualquier cosa relacionada con el estudio.

Publicaciones Generales

Fechas de registro del estudio

Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados ​​por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.

Fechas importantes del estudio

Inicio del estudio (Estimado)

1 de mayo de 2026

Finalización primaria (Estimado)

1 de diciembre de 2027

Finalización del estudio (Estimado)

1 de febrero de 2028

Fechas de registro del estudio

Enviado por primera vez

5 de mayo de 2026

Primero enviado que cumplió con los criterios de control de calidad

12 de mayo de 2026

Publicado por primera vez (Actual)

19 de mayo de 2026

Actualizaciones de registros de estudio

Última actualización publicada (Actual)

19 de mayo de 2026

Última actualización enviada que cumplió con los criterios de control de calidad

12 de mayo de 2026

Última verificación

1 de mayo de 2026

Más información

Términos relacionados con este estudio

Términos MeSH relevantes adicionales

Otros números de identificación del estudio

  • IIT_AIRCARE_H&NANCE

Plan de datos de participantes individuales (IPD)

¿Planea compartir datos de participantes individuales (IPD)?

INDECISO

Información sobre medicamentos y dispositivos, documentos del estudio

Estudia un producto farmacéutico regulado por la FDA de EE. UU.

No

Estudia un producto de dispositivo regulado por la FDA de EE. UU.

No

Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .

Buscar ensayos similares

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

CROs by country

CROs in Ireland

Condiciones

Enfermedades Raras

Intervenciones de drogas

Suplementos dietéticos

Patrocinador / Colaboradores

Localizaciones

Suscribir