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Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review.

Uzair Shah1, Md Rafuil Biswas1, Mahmood Saleh Alzubaidi1

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.

Studies in Health Technology and Informatics
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Summary

Artificial intelligence (AI) aids early prostate cancer diagnosis. This review summarizes AI methods in medical imaging for prostate cancer detection, grading, and segmentation, highlighting convolutional neural networks (CNNs).

Keywords:
Prostate cancerdeep learningmachine learningmedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate cancer diagnosis relies heavily on medical imaging.
  • Early detection significantly improves patient outcomes.
  • A growing body of research explores artificial intelligence (AI) for enhancing diagnostic accuracy.

Purpose of the Study:

  • To systematically review and summarize AI methods applied to prostate cancer diagnosis using medical imaging.
  • To categorize AI applications in prostate cancer detection, grading, and segmentation.
  • To identify prevalent AI techniques and their performance in recent literature.

Main Methods:

  • Systematic literature review following the PRISMA-ScR principle.
  • Selection of 69 studies from 1441 papers published within the last three years.
  • Analysis of AI methods including deep learning (CNNs) and traditional machine learning (SVM, decision trees, LASSO, Ridge regression).

Main Results:

  • AI methods are applied to prostate cancer diagnosis, grading, and tissue segmentation.
  • Convolutional Neural Networks (CNNs) are the most frequently utilized AI technique.
  • Traditional machine learning methods like Support Vector Machines (SVM) are also employed.

Conclusions:

  • AI-based tools show significant potential to support clinicians in early and accurate prostate cancer diagnosis.
  • The integration of AI in medical imaging can lead to improved diagnostic plans.
  • Further research and implementation of AI are crucial for advancing prostate cancer care.