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ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging.
Pranav Kulkarni1, Adway Kanhere1, Eliot L Siegel1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 100 N Greene St, Baltimore, MD, 21201, USA.
Intelligent streaming framework ISLE reduces bandwidth and computational needs for AI in radiology. This optimizes AI inference, significantly boosting processing speed without affecting diagnostic accuracy.
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Area of Science:
- Medical Imaging
- Artificial Intelligence
- Health Informatics
Background:
- Growing adoption of AI in radiology increases demand for bandwidth and computational resources.
- High infrastructural costs are a challenge for healthcare providers and AI vendors.
- Current imaging infrastructures face inefficiencies in supporting AI deployments.
Purpose of the Study:
- To develop an intelligent streaming framework, ISLE, to address inefficiencies in medical imaging infrastructures.
- To reduce bandwidth and computational requirements for AI inference in radiology.
- To increase the throughput of AI systems processing medical images.
Main Methods:
- Developed ISLE, an intelligent streaming framework inspired by video-on-demand platforms.
- Intelligently streams medical images at optimal resolution for AI inference using progressive encoding.
- Evaluated ISLE by streaming chest X-rays for classification and abdomen CT scans for segmentation.
Main Results:
- ISLE reduced data transmission and decoding time by at least 82% for all tasks.
- Throughput increased by over 2.9× for segmentation and 3.72× for classification.
- AI diagnostic performance remained unaffected (P > 0.05) across all evaluated tasks.
Conclusions:
- ISLE effectively addresses imaging infrastructure inefficiencies for AI deployments.
- The framework improves data and computational efficiency in clinical AI applications.
- ISLE enables efficient AI deployment without compromising clinical decision-making.