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| ID | Type | Description | Link |
|---|---|---|---|
| CDMRP-OR210069 | Other Grant/Funding Number | U.S. Army Medical Research Acquisition Activity |
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| Name | Class |
|---|---|
| United States Department of Defense | FED |
| Lebanon Valley College | UNKNOWN |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| high risk/ACL repair cohort | The high risk cohort will be subjects who have had an ACL reconstruction procedure 9-24 months prior to enrollment. | ||
| control group | The control group will be individuals who have not had an ACL reconstruction procedure. |
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
High risk cohort
Control cohort
Exclusion Criteria:
High risk cohort
Control cohort
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ACL repair cohort: Penn State Health Bone and Joint clinic
control cohort: Penn State University/College of Medicine students and employees, Lebanon Valley College students, community sample
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| Name | Affiliation | Role |
|---|---|---|
| Cayce A Onks, DO, MS | Milton S. Hershey Medical Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Lebanon Valley College | Annville | Pennsylvania | 17003 | United States | ||
| Pennsylvania State University College of Medicine |
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| ID | Term |
|---|---|
| D000070598 | Anterior Cruciate Ligament Injuries |
| ID | Term |
|---|---|
| D007718 | Knee Injuries |
| D007869 | Leg Injuries |
| D014947 | Wounds and Injuries |
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| Day 1 |
| 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 |
| Hershey |
| Pennsylvania |
| 17033 |
| United States |