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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Deep learning for gastroscopic images: computer-aided techniques for clinicians.

Ziyi Jin¹, Tianyuan Gan¹, Peng Wang¹

¹Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China.

Biomedical Engineering Online

|February 12, 2022

Summary

This summary is machine-generated.

Deep learning aids real-time gastroscopy by helping doctors detect and characterize gastric lesions. This technology also improves overall gastroscopy quality by preventing missed diagnoses due to technical limitations.

Keywords:

Computer-aided Deep learning Gastroscopy Stomach

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Published on: March 13, 2021

Area of Science:

Medical technology
Artificial intelligence
Gastroenterology

Background:

Gastric diseases pose a significant global health burden.
Gastroscopy is the gold standard for diagnosing gastric conditions but is limited by endoscopist factors.
Advancements in deep learning offer potential solutions to enhance gastroscopy performance.

Purpose of the Study:

To review deep learning applications in real-time gastroscopy.
To address challenges related to disease detection and gastroscopy quality.
To provide technical guidance for physicians on deep learning in gastroscopy.

Main Methods:

Systematic review of recent publications on deep learning in gastroscopy.
Analysis of deep learning applications for lesion identification and characterization.
Evaluation of deep learning for improving non-disease-related aspects of gastroscopy.

Main Results:

Deep learning assists in identifying and characterizing gastric lesions during endoscopy.
AI tools help mitigate missed diagnoses caused by poor image quality or incomplete examination.
Applications are categorized into disease-related (lesion detection) and non-disease-related (quality improvement) aspects.

Conclusions:

Deep learning shows promise in enhancing the accuracy and quality of gastroscopy.
Further research is needed to address clinical application challenges and explore future directions.
AI integration can significantly support endoscopists in managing gastric diseases.