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...
Autores principales: | , , , , , , , |
---|---|
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 |