Algorithm-assisted diagnosis of Hirschsprung’s disease – evaluation of robustness and comparative image analysis on data from various labs and slide scanners

Algorithm-assisted diagnosis of Hirschsprung’s disease – evaluation of robustness and comparative image analysis on data from various labs and slide scanners

Abstract Background Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision su...

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Bibliographic Details
Main Authors: Ariel Greenberg, Benzion Samueli, Shai Farkash, Yaniv Zohar, Shahar Ish-Shalom, Rami R. Hagege, Dov Hershkovitz
Format: Article
Language:English
Published: BMC 2024-02-01
Series:Diagnostic Pathology
Subjects:
Hirschsprung's disease
Algorithm
Robustness
Machine learning
Digital pathology
Online Access:https://doi.org/10.1186/s13000-024-01452-x

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