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Related Experiment Video

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep

Akarsh Singh1, Shruti Randive1, Anne Breggia2

  • 1College of Engineering, Northeastern University, Boston, MA 02115, USA.

Cancers
|December 9, 2023
PubMed
Summary

This study introduces a web platform integrating human expertise with AI for prostate cancer grading, improving clinical practice. User feedback indicates high usability and acceptance, suggesting potential for reduced workload in pathology.

Keywords:
artificial intelligencebiopsy gradingclinical validationdigital pathologyhuman computer interactionmultimodal dataprostate cancerusability test

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Prostate cancer is a leading cause of male cancer mortality.
  • Accurate grading of prostate cancer biopsies is essential for treatment planning.
  • Integrating advanced deep learning for biopsy grading into clinical workflows remains a challenge.

Purpose of the Study:

  • To develop and evaluate a web platform that combines human expertise with AI for prostate cancer grading.
  • To assess the usability and clinical relevance of the AI-assisted grading tool through user feedback.

Main Methods:

  • A web platform was developed integrating AI-driven grading with human expertise and diverse data sources.
  • Usability and clinical alignment were assessed via surveys and the NASA TLX Usability Test with pathologists and a medical practitioner.
  • User feedback was collected on navigation, ease of understanding, data requirements, and overall workload.

Main Results:

  • 60% of users found the platform easy to navigate (5.5/7 ease of understanding).
  • All users preferred the detailed summary tab for ease of use (6.5/7).
  • High-resolution biopsy images were considered vital, while extensive patient demographics were deemed unnecessary by 80% of users.

Conclusions:

  • The AI-assisted prostate cancer grading platform demonstrates high usability and user acceptance.
  • The tool shows potential for reducing pathologist workload and improving clinical integration of AI in oncology.
  • Further improvements in data visualization and explanations are suggested based on workload assessment.