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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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A Cloud-Based System for Automated AI Image Analysis and Reporting.

Neil Chatterjee1,2, Jeffrey Duda3, James Gee3,4

  • 1Department of Radiology, University of Pennsylvania, Philadelphia, USA. nchatter@nm.org.

Journal of Imaging Informatics in Medicine
|July 31, 2024
PubMed
Summary
This summary is machine-generated.

Implementing an AI orchestrator streamlined clinical workflow for radiologists, enabling opportunistic screening for hepatic steatosis in abdominal CT scans. This AI integration achieved rapid turnaround times and seamless data handling across multiple sites.

Keywords:
AIAI orchestratorInformaticsOpportunistic screeningSteatosis

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Healthcare Informatics

Background:

  • Clinical implementation of artificial intelligence (AI) algorithms faces challenges integrating into existing radiologist workflows and healthcare systems.
  • Seamless integration is crucial for widespread adoption of AI tools in medical imaging.
  • Opportunistic screening for conditions like hepatic steatosis using AI can improve patient outcomes.

Purpose of the Study:

  • To develop and evaluate an AI orchestrator for seamless integration of AI tools into a multi-site university healthcare system.
  • To assess the feasibility and efficiency of using the AI orchestrator for opportunistic screening of hepatic steatosis on abdominal CT scans.
  • To provide a adaptable framework for deploying various clinical AI algorithms.

Main Methods:

  • An AI orchestrator was developed to manage AI tool deployment and clinical workflow integration across multiple physical locations.
  • The system processed 991 abdominal CT scans over a 60-day period for opportunistic hepatic steatosis screening.
  • Standardized data formats were used for all server inputs and outputs, ensuring quality control and workflow integration.

Main Results:

  • The AI orchestrator facilitated the deployment of AI tools for opportunistic hepatic steatosis screening in a large healthcare system.
  • An average turnaround time of 2.8 minutes was achieved for processing abdominal CT scans.
  • Quality control images and AI results were fully integrated into the existing clinical workflow, demonstrating seamless operation.

Conclusions:

  • The developed AI orchestrator effectively integrates AI tools into clinical workflows, overcoming common implementation barriers.
  • This framework supports opportunistic screening for hepatic steatosis and is adaptable for other clinical AI applications.
  • The methodology demonstrates a viable approach for enhancing AI adoption in radiology and healthcare enterprises.