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

Uzair Shah, Md Rafuil Biswas, Mahmood Saleh Alzubaidi, Hazrat Ali, Tanvir Alam, Mowafa Househ, Zubair Shah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that the implementation of AI-based tools will support clinicians to provide better diagnosis plans for prostate cancer.

Original languageEnglish
Title of host publicationInformatics and Technology in Clinical Care and Public Health
EditorsJohn Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos, Emmanouil Zoulias, Joseph Liasko
PublisherIOS Press BV
Pages268-271
Number of pages4
ISBN (Electronic)9781643682501
DOIs
Publication statusPublished - 2022

Publication series

NameStudies in Health Technology and Informatics
Volume289
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Keywords

  • Prostate cancer
  • deep learning
  • machine learning
  • medical imaging

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