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 t...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 3250 - 3264 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283985&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283985 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283985 182283985 182283985 10.1007/s10278-024-01173-z 182283985 ppf: 3250 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging. aug: au: Kulkarni, Pranav Kanhere, Adway Siegel, Eliot L. Yi, Paul H. Parekh, Vishwa S. affil: https://ror.org/00sde4n60 Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 100 N Greene St, 21201, Baltimore, MD, USA sug: subj: Artificial Intelligence Utilization Diagnostic Imaging Radiography, Thoracic Radiography, Abdominal Tomography, X-Ray Computed Liver Radiography Spleen Radiography Image Processing, Computer Assisted Human Retrospective Design DICOM ROC Curve Paired T-Tests Decision Making, Clinical Decision Making, Computer Assisted Deep Learning Wilcoxon Signed Rank Test Descriptive Statistics ab: 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% and 88%, respectively, while increasing throughput by more than 3.72 ×. For both segmentation tasks, ISLE reduced data transmission and decoding time by at least 82% and 88%, respectively, while increasing throughput by more than 2.9 ×. In all three tasks, the ISLE streamed data had no impact on the AI system's diagnostic performance (all P > 0.05). Therefore, our results indicate that our framework can address inefficiencies in current imaging infrastructures by improving data and computational efficiency of AI deployments in the clinical environment without impacting clinical decision-making using AI systems. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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