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Area of Science:

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

Background:

  • Inferior Vena Cava (IVC) filters are crucial for preventing pulmonary embolism but can lead to complications if not managed properly.
  • Accurate detection and characterization of IVC filters on abdominal computed tomography (CT) scans are essential for clinical decision-making.
  • Existing methods for IVC filter assessment can be time-consuming and prone to human error.

Purpose of the Study:

  • To develop and evaluate data-driven algorithms for automated detection and characterization of IVC filters on abdominal CT scans.
  • To assist healthcare providers in managing IVC filter placement and removal, thereby reducing associated complications.
  • To establish a computational framework for identifying patients with retained IVC filters for individualized treatment decisions.

Main Methods:

  • Development of two algorithms: a 2D + transfer learning (2D + TL) model and a 3D + recurrent convolutional neural network (3D + RCNN) model.
  • Training computational models using Tensorflow Keras API on a dataset of 2048 abdominal CT studies from 439 patients.
  • Utilizing a fine-tuning supervised training strategy with reference annotations for filter location and type.

Main Results:

  • The 3D + RCNN model achieved higher sensitivity (0.923) and precision (0.853) for filter detection compared to the 2D + TL model (0.911 sensitivity, 0.804 precision).
  • Both models demonstrated high confidence in predicting IVC filter locations (0.993 for 2D + TL, 0.996 for 3D + RCNN).
  • The filter type prediction component achieved high performance, with the 3D + RCNN model showing 0.940 sensitivity, 0.927 specificity, and 0.975 AUC.

Conclusions:

  • The developed AI framework shows significant promise in accurately detecting and characterizing IVC filters from abdominal CT scans.
  • These algorithms can serve as valuable tools for healthcare providers, aiding in the timely and appropriate management of IVC filters.
  • This study represents a novel contribution to automated IVC filter analysis, paving the way for improved patient outcomes and reduced medical complications.