AI-Assisted Endoscopy for Upper Aerodigestive Tract Lesions

Condition(s) studied

Study 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.

Eligibility

Primary outcome measure(s)

• Negative Predictive Value of the CADx Algorithm for Malignant or Premalignant Upper Aerodigestive Tract Lesions — 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%.
• Sensitivity of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions — 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).
• Median Intersection Over Union Between CASe Segmentation and Surgeon-Drawn Lesion Margins — 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.
• Median Dice Similarity Coefficient Between CASe Segmentation and Surgeon-Drawn Lesion Margins — 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.

Trial sites (3)

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