Literature DB >> 31272605

Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape.

Michael S Landau1, Liron Pantanowitz2.   

Abstract

Artificial intelligence (AI) has made impressive strides recently in interpreting complex images, thanks to improvements in deep learning techniques and increasing computational power. Researchers have started applying these advanced techniques to pathology images, although most efforts have been focused on histopathology. Cytopathology, however, remains the original field of pathology for which AI models for clinical use were successfully commercialized, to assist with automating Papanicolaou test screening. Recent AI efforts have focused on whole slide images of both gynecologic and non-gynecologic cytopathology. This review summarizes the literature and commercial landscape of AI as applied to cytopathology.
Copyright © 2019 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.

Keywords:  Artificial intelligence; Cytology; Deep learning; Image analysis; Informatics; Machine learning

Mesh:

Year:  2019        PMID: 31272605     DOI: 10.1016/j.jasc.2019.03.003

Source DB:  PubMed          Journal:  J Am Soc Cytopathol        ISSN: 2213-2953


  10 in total

1.  Promises of artificial intelligence in neuroradiology: a systematic technographic review.

Authors:  Allard W Olthof; Peter M A van Ooijen; Mohammad H Rezazade Mehrizi
Journal:  Neuroradiology       Date:  2020-04-22       Impact factor: 2.804

2.  Rapid on-site evaluation (ROSE) with EUS-FNA: The ROSE looks beautiful.

Authors:  Fei Yang; Enshuo Liu; Siyu Sun
Journal:  Endosc Ultrasound       Date:  2019 Sep-Oct       Impact factor: 5.628

Review 3.  Artificial intelligence and computational pathology.

Authors:  Miao Cui; David Y Zhang
Journal:  Lab Invest       Date:  2021-01-16       Impact factor: 5.662

4.  Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning.

Authors:  Bum-Joo Cho; Jeong-Won Kim; Jungkap Park; Gui-Young Kwon; Mineui Hong; Si-Hyong Jang; Heejin Bang; Gilhyang Kim; Sung-Taek Park
Journal:  Diagnostics (Basel)       Date:  2022-02-21

Review 5.  Artificial Intelligence in Cervical Cancer Screening and Diagnosis.

Authors:  Xin Hou; Guangyang Shen; Liqiang Zhou; Yinuo Li; Tian Wang; Xiangyi Ma
Journal:  Front Oncol       Date:  2022-03-11       Impact factor: 6.244

Review 6.  Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review.

Authors:  Nishant Thakur; Mohammad Rizwan Alam; Jamshid Abdul-Ghafar; Yosep Chong
Journal:  Cancers (Basel)       Date:  2022-07-20       Impact factor: 6.575

7.  Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.

Authors:  Xiaohui Zhu; Xiaoming Li; Kokhaur Ong; Wenli Zhang; Wencai Li; Longjie Li; David Young; Yongjian Su; Bin Shang; Linggan Peng; Wei Xiong; Yunke Liu; Wenting Liao; Jingjing Xu; Feifei Wang; Qing Liao; Shengnan Li; Minmin Liao; Yu Li; Linshang Rao; Jinquan Lin; Jianyuan Shi; Zejun You; Wenlong Zhong; Xinrong Liang; Hao Han; Yan Zhang; Na Tang; Aixia Hu; Hongyi Gao; Zhiqiang Cheng; Li Liang; Weimiao Yu; Yanqing Ding
Journal:  Nat Commun       Date:  2021-06-10       Impact factor: 14.919

Review 8.  Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology.

Authors:  Ryan P Lau; Teresa H Kim; Jianyu Rao
Journal:  Front Med (Lausanne)       Date:  2021-07-02

9.  A cross-sectional study exploring triage of human papillomavirus (HPV)-positive women by visual assessment, manual and computer-interpreted cytology, and HPV-16/18-45 genotyping in Cameroon.

Authors:  Pierre Vassilakos; Ania Wisniak; Rosa Catarino; Eveline Tincho Foguem; Christine Balli; Essia Saiji; Jean-Christophe Tille; Bruno Kenfack; Patrick Petignat
Journal:  Int J Gynecol Cancer       Date:  2021-04-08       Impact factor: 3.437

10.  Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells.

Authors:  Shir Ying Lee; Crystal M E Chen; Elaine Y P Lim; Liang Shen; Aneesh Sathe; Aahan Singh; Jan Sauer; Kaveh Taghipour; Christina Y C Yip
Journal:  J Pathol Inform       Date:  2021-04-07
  10 in total

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