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Advancing personalized diagnosis and treatment using deep learning architecture.

Rahat Ullah¹, Nadeem Sarwar², Mohammed Naif Alatawi³

¹School of Physics and Optoelectronics, Nanjing University of Information Science and Technology, Nanjing, China.

Frontiers in Medicine

|April 11, 2025

Summary

This study introduces ImmunoNet, a deep learning AI tool that accurately diagnoses autoimmune disorders (AID) by analyzing integrated patient data. ImmunoNet offers personalized treatment recommendations, improving precision medicine in immunology.

Keywords:

CNN MLP autoimmune disorder deep learning ensemble learning

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Area of Science:

Immunology
Artificial Intelligence
Computational Biology

Background:

Autoimmune disorders (AID) pose diagnostic challenges due to complex causes and varied symptoms.
Current diagnostic methods lack specificity and personalized treatment capabilities.
Need for advanced tools to improve autoimmune disease diagnosis and management.

Purpose of the Study:

To develop ImmunoNet, a deep learning framework for enhanced autoimmune disease diagnosis and treatment.
To integrate genetic, molecular, and clinical data for improved accuracy.
To provide personalized therapeutic recommendations using AI.

Main Methods:

Utilized deep learning, including convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs).
Integrated diverse datasets: genetic, molecular, and clinical information.
Employed explainable AI for interpretability and federated learning for privacy.

Main Results:

ImmunoNet achieved 98% accuracy in predicting autoimmune disorders.
Demonstrated superior performance compared to traditional machine learning models.
Enabled precise disease classification and personalized treatment suggestions.

Conclusions:

ImmunoNet advances precision medicine in immunology.
Offers a powerful tool for personalized diagnosis and optimized therapeutic strategies.
Highlights the potential of AI in improving autoimmune disease care.