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Review
. 2022 Apr 21;12(5):1042.
doi: 10.3390/diagnostics12051042.

Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

Rachel N Flach  1 , Nina L Fransen  1 , Andreas F P Sonnen  1 , Tri Q Nguyen  1 , Gerben E Breimer  1 , Mitko Veta  1   2 , Nikolas Stathonikos  1 , Carmen van Dooijeweert  1 , Paul J van Diest  1
Affiliations
Review

Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

Rachel N Flach et al. Diagnostics (Basel). .

Abstract

Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.

Keywords: artificial intelligence; digital pathology; implementation; machine learning; roadmap.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
In-house developed AI algorithm for mitotic figures recognition. (A) Selecting a region of interest. (B,C) Interactive Mitosis Detector, with gallery (B) and without gallery (C). The detector highlights those areas suspicious for mitosis with orange, those negative for mitosis as green. (D) Close-up of mitotic figure (mitotic figure selected by the pointer on the right in the gallery), recognized by the algorithm.
Figure 2
Figure 2
Flowchart showing a workflow for on-demand, interactive processing.
Figure 3
Figure 3
Flowchart showing workflow for background batch analysis, a workflow driven process.
See this image and copyright information in PMC

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