Prostate cancer detection using e-nose and AI for high probability assessment

BMC Medical Informatics and Decision Making

Research

J. Pelegri-Sebastia¹  T. Sogorb¹  J. B. Talens²  J. L. Ruiz³ 

[1] Sensor and Magnetism Group, Institut de Recerca Per a La Gestió Integrada de Zones Costaneres (IGIC), Campus de Gandia, Universitat Politecnica de Valencia, Paranimf 1, Grao de Gandia, 46000, Valencia, Spain;Sensor and Magnetism Group, Institut de Recerca Per a La Gestió Integrada de Zones Costaneres (IGIC), Campus de Gandia, Universitat Politecnica de Valencia, Paranimf 1, Grao de Gandia, 46000, Valencia, Spain;Educacion, Conselleria de Educacion, Cultura y Deporte, Av. de Campanar, 32, 46015, Valencia, Spain;Surgery Department, Universitat de Valencia, Av Fernando Abril, Martorell, 106., 46026, Valencia, Spain;

关键词: Deep learning;    Neural networks;    Machine intelligence;    e-Nose;    MOOSY-32;    Prostate cancer;   

DOI  :  10.1186/s12911-023-02312-2

 received in 2023-05-12, accepted in 2023-09-28,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

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