Iridology as a Predictive Tool for Human Health: Innovating Non-Invasive Diagnostics

Abstract

Iridology, the study of the iris to reveal systemic health, has faced challenges related to data privacy, clinical validity, and diagnostic limitations. This paper introduces a novel AI-driven framework that integrates federated learning (FL), convolutional neural networks (CNNs), genetic algorithms (GA), and edge computing to enhance iridology’s reliability and practicality. The proposed system preserves data privacy while improving predictive accuracy through decentralized learning and genomic correlation analysis. Experimental results demonstrate strong clinical performance, achieving a diabetes prediction accuracy of 90% and cardiovascular risk prediction of 86% [1], with real-time deployment on NVIDIA Jetson Nano ensuring an average inference time of 45ms per image [2]. Furthermore, genetic feature selection identified iris biomarkers linked to disease-associated genes, improving diagnostic precision [3]. Clinical validation across diverse demographics confirmed robustness, with no significant variation across age groups (p = 0.23), genders (p = 0.45), or ethnicities (p = 0.12) [4]. This hybrid approach represents a significant advancement in non-invasive diagnostics, bridging AI-driven iris analysis with genetic predisposition insights. Future work will focus on integrating multi-modal imaging techniques and expanding federated learning across global datasets to enhance scalability and applicability in clinical settings [5].

Introduction

Overview
Iridology—assessing health through iris patterns—has historically lacked clinical validation due to subjective interpretations. Recent integration of AI and machine learning (ML) offers new promise by improving diagnostic accuracy, scalability, and objectivity. However, current models face limitations such as:

• Small, non-diverse datasets reducing generalizability.

• Centralized models risking privacy and bias.

• Focus on symptom detection without accounting for genetic predispositions.

Proposed Solution
This study introduces a hybrid AI framework combining:

• Federated Learning (FL) for decentralized, privacy-preserving training.

• Convolutional Neural Networks (CNNs) for iris/fundus image analysis.

• Genetic Algorithms (GAs) to correlate image features with genetic risk.

• Edge Computing (e.g., NVIDIA Jetson Nano) for real-time disease detection.

Dataset & Tools
The model uses the ODIR dataset (5,000 patients) with labeled fundus images for 8 ocular diseases. It employs:

• Python, TensorFlow Federated, PyTorch for FL and CNN implementation.

• PLINK for processing genomic data.

Methodology Highlights

• Image Preprocessing: ResNet-50 used for deep feature extraction.

• Federated Learning: CNN trained across institutions using FedAvg.

• GA-Based Feature Selection: Optimizes prediction accuracy and stability.

• Edge Deployment: Achieves <50 ms inference latency per image.

Results

• FL-based models performed nearly as well as centralized CNNs (e.g., 90% vs. 92% accuracy for diabetic retinopathy).

• GA identified biomarkers like MYOC (glaucoma) and VEGFA (diabetic retinopathy).

• Clinical tests showed strong generalizability across demographics.

• No significant performance difference across age, gender, or ethnicity.

Conclusion

This study introduces a privacy-preserving, AI-driven framework for ocular disease prediction, leveraging Federated Learning (FL), genetic algorithms (GA), and edge computing. The proposed approach achieves high disease prediction accuracy, reaching 90% for diabetic retinopathy and 86% for glaucoma, with minimal performance degradation of approximately 2% in FL models. Genetic feature selection via GA successfully identifies key ocular biomarkers linked to disease-associated genes, such as MYOC for glaucoma and VEGFA for diabetic retinopathy. Additionally, the framework ensures efficient edge deployment on an NVIDIA Jetson Nano, enabling real-time inference at 45ms per image while maintaining low power consumption, making it highly suitable for telemedicine applications. With robust generalizability validated across diverse age groups, genders, and ethnicities, the model demonstrates strong clinical applicability. By addressing the limitations of traditional CNN-based ocular disease detection, this integrated framework offers a scalable, privacy-aware, and clinically relevant AI solution for early diagnosis and disease monitoring.Despite its success, there are several avenues for future enhancements. Expanding the scope of Federated Learning to include cross-institutional training across global ophthalmology datasets could improve model robustness and ensure broader applicability. Strengthening differential privacy mechanisms would further enhance data security in decentralized learning environments. Another promising direction is multi-modal disease diagnosis, integrating imaging techniques such as optical coherence tomography (OCT) and fundus fluorescein angiography (FFA) to enable a more comprehensive assessment of ocular diseases. Additionally, extending FL to multi-organ disease prediction could help explore correlations between ocular conditions and systemic diseases like diabetes and cardiovascular disorders.Optimization of Edge AI is another critical aspect of future work, including the development of lightweight CNN architectures tailored for ultra-low-power IoT devices. Implementing on-device learning would allow personalized ocular health monitoring, reducing reliance on centralized processing while maintaining efficiency. Furthermore, clinical integration and real-world testing are essential to validate the framework\'s practical effectiveness. Large-scale clinical trials will be conducted to evaluate its real-world efficacy, while collaborations with ophthalmology clinics will facilitate real-time deployment and validation within telemedicine workflows. By integrating advanced AI, privacy-preserving learning, and real-time edge inference, this framework bridges the gap between AI research and clinical application, paving the way for scalable, global eye care solutions.

References

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Copyright

Copyright © 2025 V T Ram Pavan Kumar M, P. Subrahmanyam, P. Vishnu Vardhan, T Ravi Teja, T. Ganga Raju, V. Nithin Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.