Artificial intelligence for medical imaging: U-Net technology for anatomical feature analysis

The successful utilization of artificial intelligence (AI) systems in medical and healthcare systems has substantially advanced research and publication in scientific journals. This field encompasses a wide range of studies, including image processing, natural language processing, medical physics, patient data analysis, and clinical assistance tools. The current progress in AI methods can be attributed to substantial improvements in computational capacity and data processing capabilities. Notably, computer vision and image processing have emerged as highly successful AI applications. The U-Net convolutional neural network has emerged as a powerful and efficient tool for medical image segmentation and processing. This model features an encoder-decoder configuration interconnected by a bridging element, with skip connections between layers that enhance the value of the original training data. Its impressive efficiency in image processing stems from its rapid processing capability, ability to extract relationships from data, and high training velocity. Medical imagery often comprises multiple cross-sectional slices, providing a volumetric perspective of the observed region. Analyzing such imaging data requires substantial computational power and storage capacity, especially for 3-dimensional (3D) analysis. In this regard, 3D U-Net networks excel by concurrently processing numerous slices in voxel space. This attribute considerably reduces computational expenses while simultaneously improving precision. Recent advancements have brought AI technology developers to a level of stability and positive predictive values that make routine use of these systems in medical devices feasible. Currently, the implementation of AI systems appears more realistic than in previous decades. This review article focuses on state-of-the-art technologies in medical imaging for monitoring and diagnostic purposes, specifically using U-Net. We have reviewed the quality measurement of AI imaging systems using gold standards and explored novel technologies that have not been discussed in previous U-Net review papers. Additionally, we discuss the promising future development of AI systems for medical imaging purposes.