ISLE: an Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging

As the adoption of artificial intelligence (AI) systems in radiology grows, the increase in demand for greater bandwidth and computational resources can lead to greater infrastructural costs for healthcare providers and AI vendors. To that end, we developed ISLE, an intelligent streaming framework to address inefficiencies in current imaging infrastructures. Our framework draws inspiration from video-on-demand platforms to intelligently stream medical images to AI vendors at an optimal resolution for inference from a single high-resolution copy using progressive encoding. We hypothesize that ISLE can dramatically reduce the bandwidth and computational requirements for AI inference, while increasing throughput (i.e., the number of scans processed by the AI system per second). We evaluate our framework by streaming chest X-rays for classification and abdomen CT scans for liver and spleen segmentation and comparing them with the original versions of each dataset. For classification, our results show that ISLE reduced data transmission and decoding time by at least 92