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Related Concept Videos

Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due to...

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Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
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Deep Learning-Based Videomics for Automatic Segmentation in Endoscopic Endonasal Surgery.

Edoardo Agosti1, Andrea Pagnoni1, Cesare Zoia2

  • 1Neurosurgery Division, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy.

Annali Italiani Di Chirurgia
|March 16, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) videomics shows high accuracy for segmenting tissues in endoscopic endonasal surgery. While models like Swin Transformer and YOLO offer real-time surgical support, clinical adoption is hindered by data inconsistencies.

Keywords:
automatic segmentationdeep learningendoscopic endonasal surgeryvideomics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Surgery
  • Endoscopic Surgery

Background:

  • Videomics, using deep learning (DL) on endoscopic video, enhances real-time tissue segmentation and anatomical recognition.
  • Potential applications in endoscopic endonasal approaches include improved intraoperative visualization, tumor delineation, and surgical precision.
  • Clinical translation of these advanced methods remains limited and requires further characterization.

Purpose of the Study:

  • To systematically review current evidence on DL-based segmentation in endoscopic endonasal surgery.
  • To analyze model architectures, segmentation targets, and reported outcomes.
  • To identify barriers to clinical implementation.

Main Methods:

  • Systematic literature search following PRISMA 2020 guidelines (PubMed, Scopus, Web of Science).
  • Inclusion of studies from 2018-2025 involving human endoscopic endonasal procedures and DL segmentation.
  • Data extraction on sample size, image resolution, datasets, DL architectures, targets, and performance metrics; quality assessment using Newcastle-Ottawa Scale.

Main Results:

  • 28 studies (154,989 patients, 1,028,440 images) met inclusion criteria.
  • Common targets: nasal polyps (25%), nasopharyngeal carcinoma (21.4%), pituitary adenomas (7.14%).
  • High performance metrics (AUC-ROC: 87.4%-99.2%, mIoU: 61.2%-81.7%, mAP: 53.4%-94.9%), with increasing use of transformer models; dataset heterogeneity noted.

Conclusions:

  • Deep learning videomics achieves high segmentation accuracy for diverse pathologies in endoscopic endonasal surgery.
  • Swin Transformer and YOLO models show promise for real-time surgical assistance.
  • Clinical integration is challenged by dataset heterogeneity and inconsistent reporting standards.