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Current Architectural and Developmental Approaches in Artificial Intelligence Models for Prostate Cancer Detection

Kian A Huang1, Haris K Choudhary1, Kyoung A V Lee1

  • 1General Surgery, University of South Florida Health Morsani College of Medicine, Tampa, USA.

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|May 7, 2025
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Summary

Artificial intelligence (AI) enhances prostate cancer diagnostics by improving whole-slide image analysis and Gleason grading. AI models integrate data for better risk stratification, aiming to reduce unnecessary biopsies and increase diagnostic accuracy.

Keywords:
computer visionconvolutional neural networkprostate cancerprostate cancer detectionprostate cancer treatment

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Area of Science:

  • Oncology
  • Medical Informatics
  • Computational Pathology

Background:

  • Prostate cancer is a leading cause of male cancer mortality, necessitating improved diagnostic tools.
  • Current methods like Gleason grading and PSA testing have limitations in accuracy and efficiency.
  • Artificial intelligence (AI) offers potential solutions for enhancing prostate cancer diagnosis.

Purpose of the Study:

  • To explore the role of AI in improving prostate cancer diagnostics.
  • To assess AI's impact on histopathological analysis and Gleason grading.
  • To evaluate AI's potential in risk stratification and reducing unnecessary biopsies.

Main Methods:

  • Utilized AI models, including convolutional neural networks and deep learning systems.
  • Employed advanced techniques like ensemble learning and semi-supervised learning for feature extraction.
  • Integrated AI with prostate-specific antigen (PSA) data for risk stratification.

Main Results:

  • AI models show promise in enhancing accuracy for tumor detection and Gleason grading.
  • AI integration with PSA data improved risk stratification accuracy.
  • AI demonstrated potential to reduce reliance on traditional PSA thresholds and minimize biopsies.

Conclusions:

  • AI has the potential to revolutionize prostate cancer diagnostics, improving workflow efficiency and precision.
  • Addressing challenges like data variability and standardization is crucial for clinical adoption.
  • AI-driven approaches can significantly enhance diagnostic accuracy in clinical practice.