Literature DB >> 31406351

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis.

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


  36 in total

Review 1.  Deep learning in histopathology: the path to the clinic.

Authors:  Jeroen van der Laak; Geert Litjens; Francesco Ciompi
Journal:  Nat Med       Date:  2021-05-14       Impact factor: 53.440

Review 2.  Artificial Intelligence in Pathology.

Authors:  Sebastian Försch; Frederick Klauschen; Peter Hufnagl; Wilfried Roth
Journal:  Dtsch Arztebl Int       Date:  2021-03-26       Impact factor: 5.594

Review 3.  Designing deep learning studies in cancer diagnostics.

Authors:  Andreas Kleppe; Ole-Johan Skrede; Sepp De Raedt; Knut Liestøl; David J Kerr; Håvard E Danielsen
Journal:  Nat Rev Cancer       Date:  2021-01-29       Impact factor: 60.716

4.  Deep learning-based single-shot autofocus method for digital microscopy.

Authors:  Jun Liao; Xu Chen; Ge Ding; Pei Dong; Hu Ye; Han Wang; Yongbing Zhang; Jianhua Yao
Journal:  Biomed Opt Express       Date:  2021-12-14       Impact factor: 3.732

Review 5.  Computational analysis of cancer genome sequencing data.

Authors:  Isidro Cortés-Ciriano; Doga C Gulhan; Jake June-Koo Lee; Giorgio E M Melloni; Peter J Park
Journal:  Nat Rev Genet       Date:  2021-12-08       Impact factor: 53.242

6.  Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study.

Authors:  Ohad Kott; Drew Linsley; Ali Amin; Andreas Karagounis; Carleen Jeffers; Dragan Golijanin; Thomas Serre; Boris Gershman
Journal:  Eur Urol Focus       Date:  2019-11-22

7.  Pan-cancer image-based detection of clinically actionable genetic alterations.

Authors:  Alexander T Pearson; Tom Luedde; Jakob Nikolas Kather; Lara R Heij; Heike I Grabsch; Chiara Loeffler; Amelie Echle; Hannah Sophie Muti; Jeremias Krause; Jan M Niehues; Kai A J Sommer; Peter Bankhead; Loes F S Kooreman; Jefree J Schulte; Nicole A Cipriani; Roman D Buelow; Peter Boor; Nadi-Na Ortiz-Brüchle; Andrew M Hanby; Valerie Speirs; Sara Kochanny; Akash Patnaik; Andrew Srisuwananukorn; Hermann Brenner; Michael Hoffmeister; Piet A van den Brandt; Dirk Jäger; Christian Trautwein
Journal:  Nat Cancer       Date:  2020-07-27

8.  Data-efficient and weakly supervised computational pathology on whole-slide images.

Authors:  Drew F K Williamson; Tiffany Y Chen; Ming Y Lu; Richard J Chen; Matteo Barbieri; Faisal Mahmood
Journal:  Nat Biomed Eng       Date:  2021-03-01       Impact factor: 25.671

9.  Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning.

Authors:  Bin Li; Yin Li; Kevin W Eliceiri
Journal:  Conf Comput Vis Pattern Recognit Workshops       Date:  2021-11-13

10.  The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography.

Authors:  Weimin Tan; Pengfei Guan; Lingjie Wu; Hedan Chen; Jichun Li; Yu Ling; Ting Fan; Yunfeng Wang; Jian Li; Bo Yan
Journal:  Ann Transl Med       Date:  2021-06
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