Robot-Assisted US-Based Vertebral Segmentation for Pedicle Screw Trajectory Identification

Healthy Clinical Trial

Official title: Robot-Assisted US-Based Vertebral Segmentation for Pedicle Screw Trajectory Identification: A Comparison With Handheld US-Reconstructions and Ground Truth CT-Data

Status Completed

Clinical Trial Summary

This other clinical trial compares robot-assisted US scanning with handheld US scanning and ground-truth CT data of the lumbar spine in healthy, young volunteers. The main questions it aims to answer are:

• Is a 3D reconstruction of a lumbar spine from robot-assisted US scanning equivalent to or better quality than a 3D reconstruction from handheld US scanning?

• Can a machine learning algorithm automatically segment the bone anatomy from robot-assisted and handheld US scanning to generate 3D lumbar spine reconstructions?

• Can pedicle screw trajectories be identified based on posterior vertebral landmarks of 3D reconstructions of lumbar spines from both robot-assisted and handheld US scanning?

Participants will:

• fill out a medical history questionnaire

• get clinically examined

• have an ultra-low-dose (ULD) CT Scan of the vertebra L1 to S1

• have a handheld US scan of the vertebra L1 to S1

• have a robot-assisted US Scan of the vertebra L1 to S1

• fill out a post-study questionnaire

Clinical Trial Description

The following hypotheses are tested:

1. A 3D reconstruction of a lumbar spine from robot-assisted US scanning is equivalent to or of better quality than a 3D reconstruction handheld US scanning.

2. A machine learning algorithm can automatically segment the bone anatomy from robot-assisted and handheld US scanning to generate said 3D lumbar spine reconstructions.

3. Pedicle screw trajectories can be identified based on posterior vertebral landmarks of 3D reconstructions of lumbar spines from robot-assisted and handheld US scanning.

The project consists of three pillars as objectives to help solidify the US reconstruction of the lumbar spine as a novel navigational method in interventional spine applications.

• 1st Pillar: A first-of-a-kind in-vivo robot-assisted and handheld US reconstruction dataset of the lumbar spine in healthy subjects is acquired. The collected dataset is compared to ground truth CT data to assess quality.

• 2nd Pillar: A novel machine learning algorithm is trained to segment the US reconstructions of all the collected lumbar spine data into each identified vertebra.

• 3rd Pillar: A novel measurement method to identify pedicle screw trajectories based on posterior vertebral landmarks is applied to the segmented US reconstructions. This research further promotes US for future use in robot-assisted interventions.

This project consists of two phases. First, a preliminary pilot study is planned to assess the project's feasibility and improve the planned workflow and safety measures. For this pilot, the investigators will mouth-to-mouth recruit two volunteers. After completing and thoroughly evaluating the pilot, the investigators will conduct the actual study.

The volunteers for the actual study are selected through public calls for participation. Possible volunteers are young, healthy, and not affected by illness or deformation of the lumbar spine. The selected volunteers are screened by asking about their medical history. If included and willing to participate, the volunteers are invited to the study at Balgrist Campus and will be clinically examined regarding the lumbar spine. Furthermore, a low-dose CT scan, a handheld US scan, and a robot-assisted US scan are held.

The CT scans are manually segmented into 3D surface models to obtain a "segmentation ground truth". A novel machine learning algorithm automatically performs 3D reconstruction and segments the robot-assisted and handheld US scans.

The 3D US reconstructions are then utilized to identify pedicle screw trajectories through a novel method based on the posterior anatomical landmarks of lumbar vertebrae.

This single-center study combines the clinical and computer-science knowledge from the Research in Orthopedic Computer Science (ROCS) team of the University of Zurich, Switzerland, with the robotics and US application knowledge from the Faculty of Engineering of the University of Leuven, Belgium. The data collection is performed at Balgrist Campus. ;

Related Conditions & MeSH terms

• Healthy

• NCT number: NCT05904418
• Study type: Interventional
• Source: Balgrist University Hospital
• Status: Completed
• Phase: N/A
• Start date: May 15, 2023
• Completion date: June 9, 2023