Next generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis

Affiliations

Editorial

Anil V Parwani. Diagn Pathol. 2019.

. 2019 Dec 27;14(1):138.

doi: 10.1186/s13000-019-0921-2.

¹ Division of Digital and Computational Pathology, Department of Pathology, The Ohio State University, Columbus, USA. anil.parwani@osumc.edu.

No abstract available

Conflict of interest statement

The author declares that he has no competing interests.

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.
Nagpal K, Foote D, Liu Y, Chen PC, Wulczyn E, Tan F, Olson N, Smith JL, Mohtashamian A, Wren JH, Corrado GS, MacDonald R, Peng LH, Amin MB, Evans AJ, Sangoi AR, Mermel CH, Hipp JD, Stumpe MC. Nagpal K, et al. NPJ Digit Med. 2019 Jun 7;2:48. doi: 10.1038/s41746-019-0112-2. eCollection 2019. NPJ Digit Med. 2019. PMID: 31304394 Free PMC article.

1. Mandong BM. Diagnostic oncology: role of the pathologist in surgical oncology--a review article. Afr J Med Med Sci. 2009;38(Suppl 2):81–88. - PubMed
1. Amin W, Srintrapun SJ, Parwani AV. Automated whole slide imaging. Expert Opin Med Diagn. 2008;2(10):1173–1181. doi: 10.1517/17530059.2.10.1173. - DOI - PubMed
1. Abels E, et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the digital pathology association. J Pathol. 2019;249(3):286–294. doi: 10.1002/path.5331. - DOI - PMC - PubMed
1. Aeffner F, et al. Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association. J Pathol Inform. 2019;10:9. doi: 10.4103/jpi.jpi_82_18. - DOI - PMC - PubMed
1. Lucas M, et al. Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Arch. 2019;475(1):77–83. doi: 10.1007/s00428-019-02577-x. - DOI - PMC - PubMed