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Related Experiment Video

Updated: Jul 14, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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

J B Talens1,2, J Pelegri-Sebastia3, T Sogorb1

  • 1Sensor 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.

BMC Medical Informatics and Decision Making
|October 6, 2023
PubMed
Summary

This study introduces an electronic nose and neural network tool for rapid prostate cancer detection using urine samples. The technology significantly reduces unnecessary biopsies, achieving a 91% cancer detection recall rate.

Keywords:
Deep learningMOOSY-32Machine intelligenceNeural networksProstate cancere-Nose

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Area of Science:

  • Biotechnology and Medical Diagnostics
  • Artificial Intelligence in Healthcare
  • Oncology and Urology

Background:

  • Prostate cancer diagnosis often relies on invasive procedures like biopsies.
  • Current diagnostic methods can lead to overdiagnosis and overtreatment.
  • There is a need for rapid, non-invasive, and accurate diagnostic tools.

Purpose of the Study:

  • To develop an electronic nose-based diagnostic tool for prostate cancer.
  • To utilize artificial intelligence, specifically neural networks, for sample analysis.
  • To improve diagnostic accuracy and reduce unnecessary invasive procedures.

Main Methods:

  • Trained a neural network on urine sample data from prostate cancer and benign prostatic hyperplasia patients.
  • Employed a unique data redundancy method for enhanced signal analysis.
  • Utilized electronic nose technology to detect volatile organic compounds in urine.

Main Results:

  • Achieved a 91% recall rate for prostate cancer detection.
  • Demonstrated significant reduction in the number of unnecessary biopsies.
  • Improved the overall classification accuracy of the diagnostic method.

Conclusions:

  • The developed electronic nose and neural network tool offers a promising non-invasive method for prostate cancer detection.
  • This technology has the potential to be implemented in primary care settings for early and rapid diagnosis.
  • The approach effectively differentiates between prostate cancer and benign prostatic hyperplasia, aiding clinical decision-making.