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Gastritis-II: Pathophysiology

Gastritis-II: Pathophysiology

Gastritis is marked by disruption of the mucosal barrier that usually protects the stomach tissue from digestive juices and manifests in acute and chronic forms.
In acute gastritis, the gastric mucosa becomes swollen and red and undergoes superficial erosion. Superficial ulceration may lead to bleeding.
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Gastritis-I: Introduction and Types

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Gastritis III: Clinical Manifestations and Management

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Related Experiment Video

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Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

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Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation.

Hamideh Kerdegari¹, Kyle Higgins^1,2, Dennis Veselkov¹

¹Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.

Diagnostics (Basel, Switzerland)

|September 14, 2024

Summary

Foundation models (FMs) enhance artificial intelligence (AI) for early detection of upper gastrointestinal cancers. These advanced AI models improve endoscopic and pathology image analysis, aiding in better patient outcomes.

Keywords:

endoscopy foundation models gastric cancer pathology

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Area of Science:

Medical Diagnostics
Artificial Intelligence
Gastroenterology

Background:

Upper gastrointestinal (GI) cancer, particularly gastric cancer (GC), is a leading cause of global cancer mortality.
Chronic inflammation drives mucosal changes, progressing from atrophy and intestinal metaplasia (IM) to dysplasia and cancer.
Early detection via endoscopic surveillance is critical for improving patient survival rates.

Purpose of the Study:

To review recent advancements, applications, and challenges of foundation models (FMs) in endoscopy and pathology imaging for upper GI cancer.
To elucidate the principles, architectures, and training methodologies of FMs in medical diagnostics.
To provide a roadmap for integrating FMs into clinical practice for GC prevention and management.

Main Methods:

Exploration of core principles and architectures of foundation models (FMs).
Analysis of training methodologies and the role of large-scale data in FM development.
Review of current applications in endoscopy and pathology image analysis.

Main Results:

Foundation models show promise in enhancing the accuracy of endoscopic and pathology image analysis for upper GI cancer.
FMs leverage large-scale, diverse data for robust predictive capabilities.
The review highlights the potential of FMs to improve early detection and management strategies.

Conclusions:

Foundation models represent a significant advancement in AI-driven medical diagnostics for upper GI cancers.
Integrating FMs into clinical workflows can improve the accuracy and efficiency of cancer detection.
Future research should focus on multimodal data integration and developing equitable, real-time diagnostic tools.