Cargando…
Prostate cancer detection using e-nose and AI for high probability assessment
This research aims to develop a diagnostic tool that can quickly and accurately detect prostate cancer using electronic nose technology and a neural network trained on a dataset of urine samples from patients diagnosed with both prostate cancer and benign prostatic hyperplasia, which incorporates a...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559535/ https://www.ncbi.nlm.nih.gov/pubmed/37803440 http://dx.doi.org/10.1186/s12911-023-02312-2 |
_version_ | 1785117521833099264 |
---|---|
author | Talens, J. B. Pelegri-Sebastia, J. Sogorb, T. Ruiz, J. L. |
author_facet | Talens, J. B. Pelegri-Sebastia, J. Sogorb, T. Ruiz, J. L. |
author_sort | Talens, J. B. |
collection | PubMed |
description | This research aims to develop a diagnostic tool that can quickly and accurately detect prostate cancer using electronic nose technology and a neural network trained on a dataset of urine samples from patients diagnosed with both prostate cancer and benign prostatic hyperplasia, which incorporates a unique data redundancy method. By analyzing signals from these samples, we were able to significantly reduce the number of unnecessary biopsies and improve the classification method, resulting in a recall rate of 91% for detecting prostate cancer. The goal is to make this technology widely available for use in primary care centers, to allow for rapid and non-invasive diagnoses. |
format | Online Article Text |
id | pubmed-10559535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105595352023-10-08 Prostate cancer detection using e-nose and AI for high probability assessment Talens, J. B. Pelegri-Sebastia, J. Sogorb, T. Ruiz, J. L. BMC Med Inform Decis Mak Research This research aims to develop a diagnostic tool that can quickly and accurately detect prostate cancer using electronic nose technology and a neural network trained on a dataset of urine samples from patients diagnosed with both prostate cancer and benign prostatic hyperplasia, which incorporates a unique data redundancy method. By analyzing signals from these samples, we were able to significantly reduce the number of unnecessary biopsies and improve the classification method, resulting in a recall rate of 91% for detecting prostate cancer. The goal is to make this technology widely available for use in primary care centers, to allow for rapid and non-invasive diagnoses. BioMed Central 2023-10-06 /pmc/articles/PMC10559535/ /pubmed/37803440 http://dx.doi.org/10.1186/s12911-023-02312-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Talens, J. B. Pelegri-Sebastia, J. Sogorb, T. Ruiz, J. L. Prostate cancer detection using e-nose and AI for high probability assessment |
title | Prostate cancer detection using e-nose and AI for high probability assessment |
title_full | Prostate cancer detection using e-nose and AI for high probability assessment |
title_fullStr | Prostate cancer detection using e-nose and AI for high probability assessment |
title_full_unstemmed | Prostate cancer detection using e-nose and AI for high probability assessment |
title_short | Prostate cancer detection using e-nose and AI for high probability assessment |
title_sort | prostate cancer detection using e-nose and ai for high probability assessment |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559535/ https://www.ncbi.nlm.nih.gov/pubmed/37803440 http://dx.doi.org/10.1186/s12911-023-02312-2 |
work_keys_str_mv | AT talensjb prostatecancerdetectionusingenoseandaiforhighprobabilityassessment AT pelegrisebastiaj prostatecancerdetectionusingenoseandaiforhighprobabilityassessment AT sogorbt prostatecancerdetectionusingenoseandaiforhighprobabilityassessment AT ruizjl prostatecancerdetectionusingenoseandaiforhighprobabilityassessment |