이 페이지는 자동 번역되었으며 번역의 정확성을 보장하지 않습니다. 참조하십시오 영문판 원본 텍스트의 경우.

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

2026년 5월 12일 업데이트: 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.

연구 개요

상태

아직 모집하지 않음

정황

상세 설명

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.

연구 유형

관찰

등록 (추정된)

283

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

  • 벨기에
    • Flemish Brabant
      • Leuven, Flemish Brabant, 벨기에, 3000
        • UZ Leuven
        • 연락하다:
        • 수석 연구원:
          • Vincent Vander Poorten, MD PhD
  • 스페인
    • Barcelona
      • Barcelona, Barcelona, 스페인, 08036
        • Hospital Clinic de Barcelona
        • 연락하다:
        • 수석 연구원:
          • Claudio Sampieri, MD PhD
  • 이탈리아
    • GE
      • Genova, GE, 이탈리아, 16131
        • IRCCS Ospedale Policlinico San Martino
        • 연락하다:
        • 수석 연구원:
          • Francesco Mora, MD PhD

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

샘플링 방법

비확률 샘플

연구 인구

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

설명

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

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Negative Predictive Value of the CADx Algorithm for Malignant or Premalignant Upper Aerodigestive Tract Lesions
기간: 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
기간: 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
기간: 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
기간: 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.

2차 결과 측정

결과 측정
측정값 설명
기간
WL-NPV vs. NBI-NPV of CADx classification
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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.

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Istituto Italiano di Tecnologia

협력자

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

간행물 및 유용한 링크

연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.

일반 간행물

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 5월 1일

기본 완료 (추정된)

2027년 12월 1일

연구 완료 (추정된)

2028년 2월 1일

연구 등록 날짜

최초 제출

2026년 5월 5일

QC 기준을 충족하는 최초 제출

2026년 5월 12일

처음 게시됨 (실제)

2026년 5월 19일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 5월 19일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 5월 12일

마지막으로 확인됨

2026년 5월 1일

추가 정보

이 연구와 관련된 용어

추가 관련 MeSH 약관

기타 연구 ID 번호

  • IIT_AIRCARE_H&NANCE

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

미정

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

유사한 임상시험 검색

스폰서 및 공동 작업자

건강 상태

약물 개입

CROs by country

CROs in Saudi Arabia

정황

희귀 질병

약물 개입

식이 보충제

스폰서 / 협력자

위치

구독하다