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This summary is machine-generated.

Artificial intelligence (AI) shows promise in improving prostate cancer detection and segmentation using high-frequency micro-ultrasound (micro-US). While early results are encouraging, further research is needed for widespread clinical adoption of AI-enhanced micro-US.

Keywords:
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • High-frequency micro-ultrasound (micro-US) provides real-time, high-resolution imaging for prostate cancer.
  • Artificial intelligence (AI) has the potential to enhance micro-US interpretation for prostate cancer.
  • A comprehensive review of AI applications in micro-US for prostate cancer is currently lacking.

Purpose of the Study:

  • To synthesize current evidence on AI applied to ExactVu 29 MHz micro-US for prostate cancer detection, segmentation, and registration.
  • To evaluate the performance of AI models in interpreting micro-US data for prostate cancer.
  • To identify the current state and future directions of AI in micro-US for prostate cancer.

Main Methods:

  • A systematic literature search was conducted across major databases (PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane Library) up to December 2025.
  • Studies were included if they utilized machine learning or deep learning on 29 MHz micro-US data and reported quantitative performance metrics.
  • Ten studies met the inclusion criteria, focusing on prostate cancer detection, segmentation, and micro-US-histopathology registration.

Main Results:

  • AI models for prostate cancer detection achieved area under the receiver operating characteristic curve (AUROC) values around 0.76-0.81 for core-level detection and up to 0.92 for lesion-level detection.
  • AI-driven prostate segmentation demonstrated high accuracy with a Dice similarity coefficient of approximately 0.94.
  • A single study showed precise 3D registration of micro-US to whole-mount histopathology with a Dice similarity coefficient of 0.97 and landmark error < 3 mm.

Conclusions:

  • AI applied to micro-US demonstrates promising and reproducible early results in prostate cancer detection, segmentation, and registration.
  • Current evidence, while limited, suggests AI can optimize micro-US utilization for prostate cancer.
  • Further research and development are warranted to facilitate the clinical adoption of AI-enhanced micro-US.