TY - JOUR
T1 - Deep learning assistance for the histopathologic diagnosis of Helicobacter pylori
AU - Zhou, Sharon
AU - Marklund, Henrik
AU - Blaha, Ondrej
AU - Desai, Manisha
AU - Martin, Brock
AU - Bingham, David
AU - Berry, Gerald J.
AU - Gomulia, Ellen
AU - Ng, Andrew Y.
AU - Shen, Jeanne
N1 - Publisher Copyright:
© 2020 The Authors
PY - 2020/11
Y1 - 2020/11
N2 - Aims: Deep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on H&E stain is a tedious, imprecise task which might benefit from DL assistance. In this study, a DL assistant was developed to diagnose H. pylori in gastric biopsies, and its impact on pathologist diagnostic accuracy and turnaround time was tested. Methods and results: H&E-stained whole-slide images (WSI) of 303 gastric biopsies with ground truth confirmation by immunohistochemistry formed the study dataset; 47 and 126 WSI were respectively used to train and optimize the DL assistant to detect H. pylori, and 130 were used in a clinical experiment in which 3 experienced GI pathologists reviewed the same test set with and without assistance. On the test set, the assistant achieved high performance, with a WSI-level area under the receiver-operating-characteristic curve (AUROC) of 0.965 (95% CI 0.934–0.987). On H. pylori-positive cases, assisted diagnoses were faster (βˆ, the fixed effect size for assistance = −0.557, p = 0.003) and much more accurate (OR = 13.37, p < 0.001) than unassisted diagnoses. However, assistance increased diagnostic uncertainty on H. pylori-negative cases, resulting in an overall decrease in assisted accuracy (OR = 0.435, p = 0.016) and negligible impact on overall turnaround time (βˆ for assistance = 0.010, p = 0.860). Conclusions: DL can assist pathologists with H. pylori diagnosis, but its integration into clinical workflows requires optimization to mitigate diagnostic uncertainty as a potential consequence of assistance.
AB - Aims: Deep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on H&E stain is a tedious, imprecise task which might benefit from DL assistance. In this study, a DL assistant was developed to diagnose H. pylori in gastric biopsies, and its impact on pathologist diagnostic accuracy and turnaround time was tested. Methods and results: H&E-stained whole-slide images (WSI) of 303 gastric biopsies with ground truth confirmation by immunohistochemistry formed the study dataset; 47 and 126 WSI were respectively used to train and optimize the DL assistant to detect H. pylori, and 130 were used in a clinical experiment in which 3 experienced GI pathologists reviewed the same test set with and without assistance. On the test set, the assistant achieved high performance, with a WSI-level area under the receiver-operating-characteristic curve (AUROC) of 0.965 (95% CI 0.934–0.987). On H. pylori-positive cases, assisted diagnoses were faster (βˆ, the fixed effect size for assistance = −0.557, p = 0.003) and much more accurate (OR = 13.37, p < 0.001) than unassisted diagnoses. However, assistance increased diagnostic uncertainty on H. pylori-negative cases, resulting in an overall decrease in assisted accuracy (OR = 0.435, p = 0.016) and negligible impact on overall turnaround time (βˆ for assistance = 0.010, p = 0.860). Conclusions: DL can assist pathologists with H. pylori diagnosis, but its integration into clinical workflows requires optimization to mitigate diagnostic uncertainty as a potential consequence of assistance.
KW - Artificial intelligence
KW - Deep learning
KW - Helicobacter
KW - Imaging
KW - Machine learning
KW - Pathology
KW - Stomach
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U2 - 10.1016/j.ibmed.2020.100004
DO - 10.1016/j.ibmed.2020.100004
M3 - Article
AN - SCOPUS:85110935223
SN - 2666-5212
VL - 1-2
JO - Intelligence-Based Medicine
JF - Intelligence-Based Medicine
M1 - 100004
ER -