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Quantitative Evaluation of Artificial Intelligence-Based Organ Segmentation Across Multiple Anatomic Sites Using 8

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Commercial AI software shows significant variability in segmenting organs-at-risk (OARs), impacting clinical practice. Thorough testing and quality assurance are crucial for AI segmentation tools to ensure reliable patient care.

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Radiotherapy Planning

Background:

  • Automated segmentation of organs-at-risk (OARs) using artificial intelligence (AI) offers potential efficiency gains in radiotherapy planning.
  • However, variability in AI software performance necessitates careful evaluation before clinical implementation.

Purpose of the Study:

  • To assess the segmentation accuracy and variability of eight commercial AI software platforms across diverse anatomical sites.
  • To compare AI-generated contours against clinical standards using multiple metrics.
  • To provide recommendations for the clinical adoption of AI-based OAR segmentation.

Main Methods:

  • Retrospective analysis of 160 planning CT datasets from head-and-neck, thorax, abdomen, and pelvis regions.
  • Evaluation of 31 OARs segmented by AI software against clinical contours using Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95), and relative added path length (RAPL).
  • Statistical analysis using two-factor ANOVA to quantify inter-software and inter-patient variability.

Main Results:

  • Significant inter-software and inter-patient variability in OAR segmentation accuracy was observed (p<0.05).
  • Largest inter-software variations in DSC were noted for cervical esophagus (0.41), trachea (0.10), spinal cord (0.13), and prostate (0.17).
  • Segmentation accuracy varied, with 7 OARs achieving mean DSC >0.9, 15 between 0.7-0.89, and others below 0.7. Over half (52%) of OARs showed RAPL < 0.1.

Conclusions:

  • AI-based OAR segmentation software exhibits significant variability in performance across different platforms and patient anatomies.
  • These findings underscore the critical need for rigorous software commissioning, validation, and ongoing quality assurance.
  • Implementing standardized testing protocols is essential for safe and effective clinical integration of AI segmentation tools.