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

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Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry

Stefan Lucian Popa¹, Teodora Surdea-Blaga¹, Dan Lucian Dumitrascu¹

¹Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania.

Medicina (Kaunas, Lithuania)

|September 28, 2024

Summary

This summary is machine-generated.

A new deep learning model aids in diagnosing esophageal motility disorders using high-resolution manometry images. This artificial intelligence approach shows high precision and accuracy, even for complex multi-class classifications.

Keywords:

Gemini HREM artificial intelligence deep learning esophageal motility disorder diagnosis image classification

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Esophageal motility disorders require accurate diagnosis for effective treatment.
High-resolution manometry (HRM) provides detailed functional data.
Developing automated diagnostic tools can improve efficiency and accuracy.

Purpose of the Study:

To develop a deep learning model for diagnosing esophageal motility disorders.
To utilize high-resolution manometry images for AI-driven analysis.
To leverage the Gemini AI model in the development process.

Main Methods:

A deep learning model was developed with assistance from the Gemini AI.
Gemini aided in code development, data preprocessing, and model optimization.
The model was trained and tested on high-resolution manometry image data.

Main Results:

The model achieved an overall precision of 0.89 and accuracy of 0.88.
High performance was noted in specific categories, such as panesophageal pressurization (precision=0.99, recall=0.99).
The F1-scores indicated robust diagnostic capabilities across multiple classes.

Conclusions:

Artificial intelligence, specifically Gemini, can facilitate the creation of effective deep learning models for medical image analysis.
The developed model demonstrates proficiency in complex, multi-class classification tasks for esophageal motility disorders.
This approach shows promise for advancing diagnostic capabilities in gastroenterology.