Project Title:

AI-Driven Image Segmentation and Analysis of Alcohol's Effects on Women’s Tissue In Vitro

Project Overview:

This project combines biological research and machine learning to study the effects of alcohol on women's muscle tissue using in vitro models. The primary focus is to develop an AI-driven image segmentation tool capable of identifying and analyzing mature and immature miotubes (muscle fibers) within tissue samples exposed to different concentrations of alcohol. By leveraging deep learning models, the project aims to automate the recognition and classification of tissue damage, providing insights into how alcohol impacts cellular structures at varying doses.

Key Objectives:

1. Biological Investigation:
   • Study the impact of alcohol on muscle tissue by exposing in vitro samples to 0 mM, 25 mM, and 100 mM alcohol concentrations.
   • Analyze the difference in cellular response between mature and immature miotubes.
   • Investigate potential markers of tissue damage, such as cellular morphology changes, and differences in structural integrity between various alcohol treatments.
2. AI/ML Development:
   • Develop and train a machine learning model for image segmentation that can identify and differentiate between mature and immature miotubes in tissue samples.
   • Create a model recognition system that classifies different regions (tiles) of tissue based on alcohol exposure, using these segmented images for further analysis.
   • Manage and process large datasets (TBs of data), ensuring that the model is scalable and capable of handling high volumes of high-resolution imagery.

Approach:

• Data Preparation:
  • Large datasets of tissue images will be gathered, annotated, and preprocessed, with distinct tiles representing different alcohol concentrations.
  • The AI will be trained to segment miotubes and to recognize different degrees of maturity in the muscle fibers.
• Machine Learning:
  • Image segmentation algorithms will be developed to differentiate between mature and immature miotubes, essential for analyzing the effect of alcohol at the cellular level.
  • The model will also analyze tissue damage based on the different alcohol concentrations, enabling automatic classification.
• Validation:
  • The model's performance will be validated by comparing its output with manually annotated data, ensuring accurate segmentation and classification.

Expected Outcomes:

• An advanced AI system that can accurately identify and segment muscle fibers in tissue samples, providing automated insights into how alcohol affects different types of miotubes.
• A better understanding of how alcohol concentrations impact tissue structure, contributing valuable data to the field of alcohol-related tissue damage studies.