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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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The colon, or large intestine, is the final segment of the digestive system. Its primary functions include absorbing water and vitamins produced by gut bacteria and transforming waste from liquid to solid to form stool. In adults, the large intestine is approximately 5 feet long and consists of four main sections:
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Related Experiment Video

Updated: Nov 7, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network.

Seung-Jun Hwang1, Sung-Jun Park1, Gyu-Min Kim1

  • 1School of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Korea.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

Improving colonoscopy accuracy, this study introduces a novel AI approach for polyp detection. The depth feedback network enhances depth estimation, aiming to reduce missed adenomas and prevent colorectal cancer.

Keywords:
colonoscopymonocular depth estimationunsupervised deep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Robotics

Background:

  • Colonoscopies are crucial for detecting colorectal cancer by identifying polyps and adenomas.
  • Current colonoscopy polyp detection rates vary significantly due to endoscopist skill, with a notable miss rate for adenomas.
  • Artificial intelligence and robotic technologies offer potential solutions to enhance colonoscopy accuracy.

Purpose of the Study:

  • To develop a self-supervised monocular depth estimation method for colonoscopy environments.
  • To improve polyp and adenoma detection rates by enhancing visual data interpretation during colonoscopies.
  • To introduce novel AI-driven techniques to compensate for human error in colonoscopy.

Main Methods:

  • Proposed a self-supervised monocular depth estimation technique leveraging spatiotemporal consistency.
  • Introduced a novel loss function to minimize reconstruction errors between adjacent predicted depths.
  • Developed a depth feedback network (FBNet) utilizing previous frame depth information for enhanced current frame prediction.

Main Results:

  • The proposed FBNet demonstrated superior performance in unsupervised depth estimation compared to state-of-the-art methods.
  • Quantitative and qualitative evaluations confirmed the effectiveness of the developed approach.
  • The FBNet achieved state-of-the-art results on the UCL datasets for depth estimation.

Conclusions:

  • The developed depth estimation technique shows promise for improving colonoscopy accuracy.
  • AI-driven depth feedback networks can significantly enhance the detection of abnormalities like polyps and adenomas.
  • This approach has the potential to reduce missed diagnoses and improve colorectal cancer prevention.