| Literature DB >> 31406351 |
Po-Hsuan Cameron Chen1, Krishna Gadepalli1, Robert MacDonald1, Yun Liu1, Shiro Kadowaki1, Kunal Nagpal1, Timo Kohlberger1, Jeffrey Dean1, Greg S Corrado1, Jason D Hipp1,2, Craig H Mermel3, Martin C Stumpe3,4.
Abstract
The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.Entities:
Mesh:
Year: 2019 PMID: 31406351 DOI: 10.1038/s41591-019-0539-7
Source DB: PubMed Journal: Nat Med ISSN: 1078-8956 Impact factor: 53.440