Published Paper Details:

The diagnosis of rare genetic disorders remains a significant challenge in modern medicine due to their complex nature, low incidence rates, and the scarcity of clinical data. Traditional diagnostic methods, including genetic testing and clinical evaluation, often require significant time and resources, leading to delayed diagnoses and suboptimal patient outcomes. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) offer transformative potential for overcoming these limitations by enabling more efficient, accurate, and early detection of such disorders. This paper explores the application of AI and ML algorithms in integrating multi-modal data comprising genomic sequences, medical imaging, and clinical histories to improve the early detection and diagnosis of rare genetic diseases. We present a framework that leverages the power of multi-modal data fusion, where genomic data like DNA/RNA sequencing, medical imaging, for example, X-rays, MRIs, and CT scans, and clinical data like patient history, lab results, and family background) are systematically integrated to form a comprehensive dataset for analysis. We demonstrate how deep learning techniques, particularly Convolutional Neural Networks (CNNs), can extract critical features from medical images, while advanced Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are used for analysing sequential clinical data. Ensemble learning models, including Random Forests and Gradient Boosting Machines (GBM), are employed to classify genetic mutations and predict disease outcomes based on structured clinical data. Additionally, this paper emphasizes the importance of transfer learning and multi-task learning (MTL) techniques to address the challenges posed by limited data availability, particularly for rare genetic disorders and by utilizing pre-trained models and learning from multiple related tasks, the system is capable of improving its performance even when working with relatively small datasets. Furthermore, we discuss the integration of explainable AI (XAI) techniques, such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model Agnostic Explanation) with Graph Neural Networks (GNNs), to ensure transparency and interpretability in AI-based decision-making, which is crucial for clinical adoption. Our results show that the combination of genomic, imaging, and clinical data not only enhances diagnostic accuracy but also enables earlier detection of genetic disorders that might otherwise go undiagnosed for years. We conclude that Artificial Intelligence and Machine Learning, when applied to multi-modal data integration, holds the potential to revolutionize the diagnosis of rare genetic disorders, ultimately leading to better patient outcomes, personalized treatment strategies, and more efficient healthcare practices.