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Artificial Intelligence-Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy.

Chow Wei Too1, Khi Yung Fong2, Guanqi Hang3

  • 1Department of Vascular and Interventional Radiology, Singapore General Hospital, Singapore, Singapore; Division of Radiological Sciences, Singapore General Hospital, Singapore, Singapore; Radiological Sciences Academic Clinical Program, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore.

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Summary

This study validates artificial intelligence (AI) software for detecting lung lesions and planning biopsy needle paths, showing high accuracy in both tasks. The AI demonstrated promising results for CT-guided lung biopsies, potentially improving future automated procedures.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Computational Pathology

Background:

  • Computed tomography (CT)-guided lung biopsy is crucial for diagnosing lung nodules.
  • Accurate lesion detection and precise needle path planning are essential for successful biopsy.
  • Current methods can be limited by human variability and the complexity of lung anatomy.

Purpose of the Study:

  • To validate the sensitivity and specificity of a 3D convolutional neural network (CNN) artificial intelligence (AI) software for lung lesion detection.
  • To assess the concordance between AI-generated needle paths and actual biopsy trajectories.
  • To evaluate the feasibility and accuracy of AI-guided path planning for CT-guided lung biopsy.

Main Methods:

  • Retrospective study utilizing CT scans from 3 hospitals.
  • Development and validation of a deep learning 3D-CNN for lesion detection using 2,147 nodules from 219 scans and validated on 354 lesions from 235 scans.
  • Bayesian optimization for AI-proposed needle trajectories, compared against actual biopsy paths in 150 patients based on angular deviation (<5°).

Main Results:

  • The AI model achieved high performance in lesion detection with an area under the receiver operating characteristic curve (AUC) of 97.4%, sensitivity of 93.5%, and specificity of 93.2%.
  • 85.3% of AI-proposed needle trajectories were feasible, with 82% matching actual biopsy paths (mean angular deviation 2.30°).
  • Performance was consistent across different patient orientations (supine vs. prone/oblique).

Conclusions:

  • AI-guided software demonstrates promising capabilities for segmentation, lesion detection, and path planning in CT-guided lung biopsies.
  • The AI system shows high accuracy and feasibility, supporting its potential clinical utility.
  • Future integration with robotic systems could lead to fully automated lung biopsy procedures.