Micro-Doppler Radar: A Gold Standard Comparison

Anterior Cruciate Ligament Injuries Clinical Trial

Official title:

The Use of Micro-Doppler Radar to Identify Service Members at Risk for Musculoskeletal Injury: A Gold Standard Comparison

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05521126
• Other study ID #: STUDY00020118
• Secondary ID: CDMRP-OR210069
• Status: Recruiting
• Start date: February 21, 2023
• Est. completion date: February 2025

Study information

• Verified date: March 2024
• Source: Milton S. Hershey Medical Center
• Contact: Leonard A Kishel, MA
• Phone: 717-531-0003
• Email: lkishel[@]pennstatehealth.psu.edu
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The purpose of this study is to see if the study team can use micro-Doppler signal (MDS) technology to determine if someone has had an anterior cruciate ligament (ACL) reconstruction. The investigators will do this by comparing the movement data from a group of people who have had the surgery with a group who has not had the surgery to see if the micro-Doppler radar technology can accurately and predictably tell the difference.

Description:

The objective of this research is to validate that radar MDS can accurately and predictably differentiate individuals at high-risk for MSKI from those who are low risk. The investigators hypothesize that MDS will identify individuals at a high-risk for MSKI more accurately than the gold-standard MC technologies. To test this hypothesis, the investigators propose a case control study that will compare adults who have undergone ACL reconstruction to a control group of healthy adults that has not. Patients who have undergone ACL reconstruction have a 6-24% chance of either re-tearing their ACL or having a subsequent knee surgery on either side within two years of successful completion of surgery and post-surgical rehabilitation. Despite being released for full activities, little is known about what makes this group at high-risk for re-tear. As such, the investigators will use this patient population as a model for identifying an at-risk population for musculoskeletal injury (MSKI). The researchers will simultaneously collect radar micro-Doppler signals and biomechanical motion capture (MC) data in a state-of-the-art human movement lab. Participants will be asked to perform a series of functional activities that will be captured by both the MDS radar and MC systems. The data sets will then be analyzed independently.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 250
• Est. completion date: February 2025
• Est. primary completion date: February 2025
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 40 Years

Eligibility

Inclusion Criteria:

High risk cohort

• age 18-40

• history of ACL reconstruction

• no current musculoskeletal injuries

• ACL repair between 9 and 24 months prior to recruitment

Control cohort

• age 18-40

• never had lower extremity surgery

Exclusion Criteria:

High risk cohort

• age <18 or >40

• pregnancy

• institutionalization

• history of cerebral vascular accident

• unable to provide informed consent

• inability to perform study activities

• history of hip or knee replacement

• inability to walk or jump without a limp

• current neuromuscular disease

• any surgery in the last 6 months

Control cohort

• age < 18 or > 40

• pregnancy

• institutionalization

• history of Cerebral Vascular Accident

• unable to provide informed consent

• inability to perform study activities

• history of knee or hip replacement

• inability to walk or jump without a limp

• current neuromuscular disease

• history of lower extremity surgery

• any surgery in the last 6 months

Related Conditions & MeSH terms

• Anterior Cruciate Ligament Injuries
• Musculoskeletal Injury
• Wounds and Injuries

Locations

Country	Name	City	State
United States	Lebanon Valley College	Annville	Pennsylvania
United States	Pennsylvania State University College of Medicine	Hershey	Pennsylvania

Sponsors (3)

Lead Sponsor	Collaborator
Milton S. Hershey Medical Center	Lebanon Valley College, United States Department of Defense

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	accuracy and predictability of MDS differentiation between ACL repair and control group	The investigators hypothesize that MDS will differentiate subjects who have recovered from an ACL reconstruction from a control cohort with accuracy and predictability. Participants will perform drop jumps, sit to stand, and walk on a treadmill in the presence of the micro-Doppler radar. MDS will be obtained for the ACL group as well as control. The micro-Doppler signature projection algorithm (mD-SPA) will then be applied to the data sets showing what percentage of the MDS are successfully classified to the ACL group versus control.	Day 1
Secondary	accuracy of MDS differentiation between ACL repair and control groups versus the motion capture system	The investigators hypothesize that MDS will differentiate between the ACL group versus the control group with greater accuracy compared with the MC system. MDS will show greater sensitivity and specificity for correct classification compared with the gold standard MC. Participants will perform drop jumps, sit to stand, and walk on a treadmill while collecting simultaneously the motion capture data and MDS. The data sets will then be analyzed separately and the sensitivity and specificity of each system will be compared.	Day 1
Secondary	ability of micro-Doppler radar and deep learning algorithms to automatically produce predictive data	The investigators hypothesize that by incorporating several deep learning algorithms that can extract high-level deep features automatically through hierarchical architectures, the system will be able to automatically produce predictive data that will not require specialized knowledge to operate. This will allow the system to be used by medical assistants or medical technicians who have no expertise in interpretation of the radar MDS. The investigators will accomplish this by applying numerous deep learning algorithms to the MDS to determine which algorithms most accurately and automatically classify the ACL group from control.	Day 1