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Related Concept Videos

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Endoscopic Procedures III: Video Capsule Endoscopy01:28

Endoscopic Procedures III: Video Capsule Endoscopy

Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers, unexplained...

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Related Experiment Video

Updated: Jun 27, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
10:25

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

Artificial Intelligence, Deep Learning, and Computer Vision in Hysteroscopy: A Systematic Review.

Rafał Watrowski1,2, Attilio Di Spiezio Sardo3, Peter Török4

  • 1Department of Gynecology, Helios Hospital Müllheim, Teaching Hospital of the University of Freiburg, Heliosweg 1, 79379 Müllheim, Germany.

Diagnostics (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in hysteroscopy for tasks like lesion detection and fertility prediction. However, current evidence is limited by study design, necessitating more clinical validation for widespread adoption.

Keywords:
artificial intelligencecomputer visioncomputer-aided diagnosisconvolutional neural networkdeep learninggynecologyhysteroscopichysteroscopymachine learningneural networks

Related Experiment Videos

Last Updated: Jun 27, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
10:25

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

Area of Science:

  • Gynecological Endoscopy
  • Medical Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Hysteroscopy is the standard for diagnosing and treating intrauterine conditions.
  • Interpretation of hysteroscopy is operator-dependent, leading to variability.
  • Artificial intelligence (AI), machine learning (ML), deep learning (DL), and computer-aided diagnosis (CAD) offer potential for improved consistency and decision support.

Purpose of the Study:

  • To systematically review AI, ML, DL, and CAD applications in hysteroscopy.
  • To assess the performance and limitations of these technologies in gynecological procedures.

Main Methods:

  • Systematic literature search of PubMed/MEDLINE and EBSCOhost up to March 8, 2026.
  • Inclusion of 19 primary studies across various AI applications in hysteroscopy.
  • Risk of bias and technical quality assessment using QUADAS-2, PROBAST, and RoB2.

Main Results:

  • AI demonstrated high performance in specific tasks like binary classification (AUC up to 0.979) and fertility prediction (AUC up to 0.992).
  • Applications included diagnostic classification, lesion detection, segmentation, and prognostic support.
  • Most studies were retrospective, single-center, and lacked external validation; only one randomized study linked AI to outcomes.

Conclusions:

  • AI shows technical proficiency in selected hysteroscopic tasks, including binary classification and lesion detection.
  • Current evidence is constrained by study design limitations, including retrospective data and operator-dependent image acquisition.
  • Near-term applications focus on supporting image interpretation, quality control, lesion highlighting, and data integration.