An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.
Although machine learning (ML) has made significant improvements in radiology, few algorithms have been integrated into clinical radiology workflow. Complex radiology IT environments and Picture Archiving and Communication System (PACS) pose unique challenges in creating a practical ML schema. Howev...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 4; pp. 1041 - 1047 |
|---|---|
| Autores principales: | , , , , , , , , |
| Formato: | algorithm diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2020
|
| 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=146122227&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146122227 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2020 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146122227 144164158 146122227 146122227 10.1007/s10278-020-00348-8 146122227 ppf: 1041 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow. aug: au: Sohn, Jae Ho Chillakuru, Yeshwant Reddy Lee, Stanley Lee, Amie Y Kelil, Tatiana Hess, Christopher Paul Seo, Youngho Vu, Thienkhai Joe, Bonnie N affil: Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, 94143, San Francisco, CA, USA sug: subj: Radiology Service Workflow Machine Learning Algorithms Software Design Computer Hardware Radiology Information Systems Systems Integration Human Artificial Intelligence Breast Tissue Density Mammography Hospitals, Community ab: Although machine learning (ML) has made significant improvements in radiology, few algorithms have been integrated into clinical radiology workflow. Complex radiology IT environments and Picture Archiving and Communication System (PACS) pose unique challenges in creating a practical ML schema. However, clinical integration and testing are critical to ensuring the safety and accuracy of ML algorithms. This study aims to propose, develop, and demonstrate a simple, efficient, and understandable hardware and software system for integrating ML models into the standard radiology workflow and PACS that can serve as a framework for testing ML algorithms. A Digital Imaging and Communications in Medicine/Graphics Processing Unit (DICOM/GPU) server and software pipeline was established at a metropolitan county hospital intranet to demonstrate clinical integration of ML algorithms in radiology. A clinical ML integration schema, agnostic to the hospital IT system and specific ML models/frameworks, was implemented and tested with a breast density classification algorithm and prospectively evaluated for time delays using 100 digital 2D mammograms. An open-source clinical ML integration schema was successfully implemented and demonstrated. This schema allows for simple uploading of custom ML models. With the proposed setup, the ML pipeline took an average of 26.52 s per second to process a batch of 100 studies. The most significant processing time delays were noted in model load and study stability times. The code is made available at "http://bit.ly/2Z121hX". We demonstrated the feasibility to deploy and utilize ML models in radiology without disrupting existing radiology workflow. pubtype: Academic Journal doctype: algorithm diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|