Published online Jun 28, 2020. doi: 10.35713/aic.v1.i1.31
Peer-review started: March 21, 2020
First decision: April 22, 2020
Revised: May 2, 2020
Accepted: June 7, 2020
Article in press: June 7, 2020
Published online: June 28, 2020
Core tip: Little attention has been paid to the reproducibility of machine learning (ML)-based histological classification in heterochronously obtained Digital pathology images (DPIs) of the same hematoxylin and eosin slide. This study elucidated the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs. We observed discordant classification results in 23.1% of the paired DPIs obtained by two independent scans of the same microscope slide. The group with discordant classification results showed a significantly higher blur index than the other group. Our results suggest that differences in the blur of the paired DPIs may cause discordant classification results.