| Autor: |
Sohn JH; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA. sohn87@gmail.com., Chillakuru YR; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.; School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA., Lee S; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA., Lee AY; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA., Kelil T; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA., Hess CP; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA., Seo Y; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA., Vu T; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA., Joe BN; Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA. |
| Abstrakt: |
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. |
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