AI-based models for geometric modeling of kidney function over optical networks
Abstract
The fusion of AI and emerging communication technologies introduces new challenges and opportunities in medical imaging and diagnostics. To address this issue, this study presents a new theoretical framework that utilizes AI-based geometry modeling for realistic descriptions of kidney function and anatomy based on the optical networks to transmission with high resolution in medical imaging. The AI models are powered by deep learning architectures such as convolutional neural networks (CNNs) and physics-based neural networks (PINNs) to recover fine-grained 3D geometric representations of the kidney from multi-modality imaging inputs. At the same time, optical networking technology enables large scale medical imaging files to be transmitted quickly and securely among medical imaging systems, processing servers, and remote clinical specialists. This compatibility renders both real-time analysis and telemedicine beneficial improving diagnostic accuracy and planning of therapy. The Physics-Informed Neural Networks (PINNs) technique has been met with a great deal of interest in many scientific disciplines since its introduction. Importantly, this procedure is simpler compared to other classical approaches. While PINNs are not emphasized to be competitive in terms of accuracy and computational efficiency of training, they provide a potent alternative for solving problems including parametric partial differential equations (PDE) and inverse problems which are challenging for traditional methods.