AI-Driven Tongue and Eye Diagnostics scan: Integrating Microsoft Custom Vision and AI Foundry in a Holistic Health Monitoring System

Table of Contents

AI tongue eye diagnostics myzencheck customvision foundry

This study introduces an AI-powered health diagnostics solution developed by MyZenCheck that integrates tongue diagnostics scan and eye analysis scan based on Traditional Chinese Medicine (TCM) principles. Using Microsoft’s Custom Vision for image segmentation and AI Foundry agents for intelligent processing and diagnosis, the platform demonstrates how modern AI tools can enhance personal health awareness. The system offers users real-time feedback via mobile apps, creating a bridge between ancient diagnostics and modern machine learning.

1. Introduction

Background on TCM diagnostics
Limitations of traditional methods (subjectivity, lack of scalability)
Rise of AI in health monitoring
Purpose: To develop and test an AI-based diagnostic platform using Microsoft tools and TCM methodology.

2. System Architecture

2.1. Data Collection via MyZenCheck App

Tongue and eye images collected through mobile applications
Secure cloud storage with anonymization
Optional health survey data included for better contextual accuracy

2.2. Image Segmentation Using Microsoft Custom Vision

Custom Vision model trained to identify key visual features (e.g., cracks, color, coating, pupil size)
Project classification: Object Detection for segmentation and labeling
Dataset: 500images

2.3. Analysis via AI Foundry Agents

Agents built using Azure AI Foundry logic
Tasks: Feature extraction → Pattern matching → TCM-based diagnosis logic
Multi-step reasoning and personalized advice generation

3. Methodology

Image pre-processing steps
Training strategy and performance metrics in Custom Vision
Rule-based and probabilistic models within AI Foundry agents
Cross-validation and testing of diagnostic accuracy

4. Results

4.1. Segmentation Accuracy

Precision/Recall for tongue features, eye markers
Confusion matrix for common diagnostic outcomes
Comparison with manual annotation by TCM experts

4.2. Diagnostic Relevance

Agreement score with expert TCM practitioners
Case studies: Hormonal imbalance, digestion issues, stress detection

5. Discussion

Advantages: Scalability, cost-efficiency, objectivity
Challenges: Lighting variability, user compliance, interpretability
Limitations: Not a replacement for medical consultation

6. Conclusion and Future Work

Summary of diagnostic performance
Integration roadmap: Real-time recommendations, multilingual expansion
Research direction: Combine tongue and eye data for higher accuracy

References

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Tagged ai diagnostics, AI Foundry, eye health scanner, Holistic Health, MyZenCheck, tongue analysis, Traditional Chinese Medicine