Impact of blurs on machine-learning aided digital pathology image analysis

BACKGROUND Digital pathology image(DPI)analysis has been developed by machine learning(ML)techniques.However,little attention has been paid to the reproducibility of ML-based histological classification in heterochronously obtained DPIs of the same hematoxylin and eosin(HE)slide.AIM To elucidate the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs.METHODS We created paired DPIs by scanning 298 HE stained slides containing 584 tissues twice with a virtual slide scanner.The paired DPIs were analyzed by our MLaided classification model.We defined non-flipped and flipped groups as the paired DPIs with concordant and discordant classification results,respectively.We compared differences in color and blur between the non-flipped and flipped groups by L1-norm and a blur index,respectively.RESULTS We observed discordant classification results in 23.1%of the paired DPIs obtained by two independent scans of the same microscope slide.We detected no significant difference in the L1-norm of each color channel between the two groups;however,the flipped group showed a significantly higher blur index than the non-flipped group.CONCLUSION Our results suggest that differences in the blur-not the color-of the paired DPIs may cause discordant classification results.An ML-aided classification model for DPI should be tested for this potential cause of the reduced reproducibility of the model.In a future study,a slide scanner and/or a preprocessing method of minimizing DPI blur should be developed.

Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Tremendous advances in artificial intelligence(AI)in medical image analysis have been achieved in recent years.The integration of AI is expected to cause a revolution in various areas of medicine,including gastrointestinal(GI)pathology.Currently,deep learning algorithms have shown promising benefits in areas of diagnostic histopathology,such as tumor identification,classification,prognosis prediction,and biomarker/genetic alteration prediction.While AI cannot substitute pathologists,carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice.Regardless of these promising advances,unlike the areas of radiology or cardiology imaging,no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement.Thus,implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice.The challenges have been identified at different stages of the development process,such as needs identification,data curation,model development,validation,regulation,modification of daily workflow,and cost-effectiveness balance.The aim of this review is to present challenges in the process of AI development,validation,and regulation that should be overcome for its implementation in real-life GI pathology practice.