Real-Time Biomedical Imaging for Surgical Guidance: A Review of Challenges and Solutions

Abstract

The domain of real-time biomedical imaging has seen remarkable technological advances, enhancing the efficacy of surgical interventions. This paper addresses the critical challenges associated with the implementation of real-time biomedical imaging systems for surgical guidance and discusses comprehensive solutions to mitigate these issues. It outlines the substantial computational demands for real-time processing and the necessity for high-fidelity image synthesis. The intricacies of integrating multimodal imaging data, ensuring minimal latency, and maintaining spatial accuracy for augmented reality applications are also examined. Solutions leveraging cutting-edge machine learning algorithms for image segmentation and enhancement, as well as the application of parallel processing architectures for expediting computational tasks, are presented. This manuscript also explores the potential of quantum computing paradigms in transcending conventional processing limitations. Also, the paper addresses the importance of interoperability standards for seamless integration of imaging systems in diverse surgical environments. It concludes with a discussion on the ethical implications and privacy considerations in deploying artificial intelligence in surgical settings. This paper highlights the importance of interdisciplinary innovations necessary for the advancement of real-time biomedical imaging for surgical guidance. The machine learning techniques such as CNNs, helps the trade-off with accuracy and computational speed. Whereas transfer learning procedures take 20 seconds and Federated Learning in 15 seconds represents the better performance.