Artificial Intelligence in Advance Angiogenesis and Inflammation Research: A Breakthrough in Disease Prediction and Therapy

P. Daniel Sundarraj; J.; R.; Arul Kumar V; N; Srithar

doi:10.25163/angiotherapy.899861

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

REVIEWS (Open Access)

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Artificial Intelligence in Advance Angiogenesis and Inflammation Research: A Breakthrough in Disease Prediction and Therapy

P. Daniel Sundarraj ^1*, J. Raja², R. Usha³, Arul Kumar V P⁴, N Sirisha⁵, Srithar S⁶

+ Author Affiliations

Journal of Angiotherapy 8 (9) 1-8 https://doi.org/10.25163/angiotherapy.899861

Submitted: 18 June 2024 Revised: 03 September 2024 Published: 11 September 2024

Abstract

Background: Angiogenesis and inflammation are fundamental biological processes, crucial to human health. Dysregulation of these processes is implicated in diseases such as cancer, cardiovascular disorders, and autoimmune diseases. Despite advances in research, the complexity of these interactions has been challenging to understand. Recent developments in artificial intelligence (AI) offer a promising approach for overcoming these challenges, especially in big data analysis. This study explores AI applications in quantifying angiogenesis and inflammatory markers and predicting disease progression. Methods: AI algorithms, including machine learning (ML) and deep learning (DL), were employed to analyze high-throughput biological data. The study applied Lasso regression for biomarker discovery, Long Short-Term Memory (LSTM) networks for predicting disease progression, and Gaussian Mixture Models (GMM) for patient subgroup identification. Image analysis using DeepLabv3+ was conducted to assess angiogenesis and inflammatory markers in histological images. Model performance was evaluated using R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. Results: The AI framework demonstrated high accuracy in predicting disease progression, with notable R² values and low MSE and RMSE values. The application of AI led to the successful identification of angiogenesis-related genes and biomarkers in various diseases, including diabetic foot ulcers and chronic obstructive pulmonary disease. AI-based image analysis also provided precise quantification of angiogenesis and inflammation, enhancing the understanding of disease mechanisms. Conclusion: AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation. The high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring. As AI continues to evolve, its integration into biomedical research will likely yield further advancements in disease prediction, diagnosis, and treatment.

Keywords: Angiogenesis, Inflammation, Artificial Intelligence, Machine Learning, Biomarkers

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