Deep learning based Wireless Capsule Endoscopy for Small Intestinal Lesions Detection and personalized treatment pathways

Artificial intelligence (AI) revolutionizes disease diagnosis by analyzing medical data and improving clinical tasks. This paper proposes an integrated approach that combines deep learning algorithms with Wireless Capsule Endoscopy (WCE) for enhanced small intestinal lesion detection. Our methodology integrates WCE images, electronic health records (EHR), and genomic data using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for feature extraction. By analyzing these data sources, we aim to uncover correlations between clinical factors, genetic markers, and small intestinal lesions. This approach enables customized treatment plans based on individual patient characteristics and genetic profiles, contributing to improved patient outcomes in gastroenterology. Our study highlights the importance of incorporating AI technologies in medical practice, promoting precision medicine and enhancing patient care. The integration of deep learning with WCE offers a promising avenue for personalized treatment strategies, optimizing the management of small intestinal lesions.