Cargando…

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

OBJECTIVE: This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. STUDY DESIGN: A systema...

Descripción completa

Detalles Bibliográficos
Autores principales: Pouliakis, Abraham, Karakitsou, Efrossyni, Margari, Niki, Bountris, Panagiotis, Haritou, Maria, Panayiotides, John, Koutsouris, Dimitrios, Karakitsos, Petros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4760671/
https://www.ncbi.nlm.nih.gov/pubmed/26917984
http://dx.doi.org/10.4137/BECB.S31601
_version_ 1782416887287644160
author Pouliakis, Abraham
Karakitsou, Efrossyni
Margari, Niki
Bountris, Panagiotis
Haritou, Maria
Panayiotides, John
Koutsouris, Dimitrios
Karakitsos, Petros
author_facet Pouliakis, Abraham
Karakitsou, Efrossyni
Margari, Niki
Bountris, Panagiotis
Haritou, Maria
Panayiotides, John
Koutsouris, Dimitrios
Karakitsos, Petros
author_sort Pouliakis, Abraham
collection PubMed
description OBJECTIVE: This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. STUDY DESIGN: A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. RESULTS: The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. CONCLUSIONS: Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.
format Online
Article
Text
id pubmed-4760671
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Libertas Academica
record_format MEDLINE/PubMed
spelling pubmed-47606712016-02-25 Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future Pouliakis, Abraham Karakitsou, Efrossyni Margari, Niki Bountris, Panagiotis Haritou, Maria Panayiotides, John Koutsouris, Dimitrios Karakitsos, Petros Biomed Eng Comput Biol Review OBJECTIVE: This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. STUDY DESIGN: A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. RESULTS: The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. CONCLUSIONS: Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake. Libertas Academica 2016-02-18 /pmc/articles/PMC4760671/ /pubmed/26917984 http://dx.doi.org/10.4137/BECB.S31601 Text en © 2016 the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article published under the Creative Commons CC-BY-NC 3.0 license.
spellingShingle Review
Pouliakis, Abraham
Karakitsou, Efrossyni
Margari, Niki
Bountris, Panagiotis
Haritou, Maria
Panayiotides, John
Koutsouris, Dimitrios
Karakitsos, Petros
Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future
title Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future
title_full Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future
title_fullStr Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future
title_full_unstemmed Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future
title_short Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future
title_sort artificial neural networks as decision support tools in cytopathology: past, present, and future
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4760671/
https://www.ncbi.nlm.nih.gov/pubmed/26917984
http://dx.doi.org/10.4137/BECB.S31601
work_keys_str_mv AT pouliakisabraham artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture
AT karakitsouefrossyni artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture
AT margariniki artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture
AT bountrispanagiotis artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture
AT haritoumaria artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture
AT panayiotidesjohn artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture
AT koutsourisdimitrios artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture
AT karakitsospetros artificialneuralnetworksasdecisionsupporttoolsincytopathologypastpresentandfuture