Literature DB >> 29603675

Automated screening of Papanicolaou tests: A review of the literature.

Michael J Thrall1.   

Abstract

Automated Papanicolaou test screening systems have now been available for over 25 years. Currently two automated screening systems are in widespread clinical use. These are the ThinPrep Imaging System and the FocalPoint GS Imaging System. In their current configurations, both facilitate faster screening by showing a limited number of fields of view (FOV) to cytotechnologists. The FOV are based on the use of proprietary algorithms applied to computerized images of the slide that determine the cells and cell groups with the highest likelihood of abnormality. If all of the FOV are deemed to be negative, the case can be signed out with no additional review; if one or more fields appear possibly abnormal, the entire slide must be manually screened. The United States Food and Drug Administration has ruled that for workload calculation purposes, looking at only the FOV review counts as one-half slide, potentially greatly increasing the number of slides that can be screened. However, follow-up studies of this technology have shown that screening accuracy declines when very large numbers of cases are reviewed per day. Recommendations designed to limit screening volumes to levels that do not jeopardize patient care have therefore been created. The development of fully automated screening that does not rely on human judgment remains an unrealized aspiration. This review covers the history of the development and clinical implementation of automated screening technology with descriptions of the various automated screening systems and their performance as reported in published literature.
© 2018 Wiley Periodicals, Inc.

Entities:  

Keywords:  Papanicolaou test; automated cytology screening; screening workload

Mesh:

Year:  2018        PMID: 29603675     DOI: 10.1002/dc.23931

Source DB:  PubMed          Journal:  Diagn Cytopathol        ISSN: 1097-0339            Impact factor:   1.582


  8 in total

1.  Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Authors:  Y W Lui; P D Chang; G Zaharchuk; D P Barboriak; A E Flanders; M Wintermark; C P Hess; C G Filippi
Journal:  AJNR Am J Neuroradiol       Date:  2020-07-30       Impact factor: 3.825

2.  Tao brush endometrial cytology is a sensitive diagnostic tool for cancer and hyperplasia among women presenting to clinic with abnormal uterine bleeding.

Authors:  Stephanie R DeJong; Jamie N Bakkum-Gamez; Amy C Clayton; Michael R Henry; Gary L Keeney; Jun Zhang; Trynda N Kroneman; Shannon K Laughlin-Tommaso; Lisa J Ahlberg; Ann L VanOosten; Amy L Weaver; Nicolas Wentzensen; Sarah E Kerr
Journal:  Cancer Med       Date:  2021-09-16       Impact factor: 4.711

3.  Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting.

Authors:  Oscar Holmström; Nina Linder; Harrison Kaingu; Ngali Mbuuko; Jumaa Mbete; Felix Kinyua; Sara Törnquist; Martin Muinde; Leena Krogerus; Mikael Lundin; Vinod Diwan; Johan Lundin
Journal:  JAMA Netw Open       Date:  2021-03-01

Review 4.  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 5.  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

Review 6.  DNA Karyometry for Automated Detection of Cancer Cells.

Authors:  Alfred Böcking; David Friedrich; Martin Schramm; Branko Palcic; Gregor Erbeznik
Journal:  Cancers (Basel)       Date:  2022-08-30       Impact factor: 6.575

7.  Cric searchable image database as a public platform for conventional pap smear cytology data.

Authors:  Mariana T Rezende; Raniere Silva; Fagner de O Bernardo; Alessandra H G Tobias; Paulo H C Oliveira; Tales M Machado; Caio S Costa; Fatima N S Medeiros; Daniela M Ushizima; Claudia M Carneiro; Andrea G C Bianchi
Journal:  Sci Data       Date:  2021-06-10       Impact factor: 6.444

8.  The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women.

Authors:  Heling Bao; Xiaorong Sun; Yi Zhang; Baochuan Pang; Hua Li; Liang Zhou; Fengpin Wu; Dehua Cao; Jian Wang; Bojana Turic; Linhong Wang
Journal:  Cancer Med       Date:  2020-07-22       Impact factor: 4.452

  8 in total

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