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Internal sensors may be able to accurately diagnose ACL deficiency
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In a recently published study, researchers found ACL deficiency was able to be reliably diagnosed by data stemming from internal sensors when attached to the lower extremity of the patient.
The researchers compared the knees of 32 patients with unilateral ACL deficiency with 29 controls who had healthy ACLs in both knees. ACL deficiency was diagnosed via both regression and support vector machine (SVM) classification methods.
Patients in both cohorts had inertial sensor modules fastened to the the tibia and femur of both legs for pivot-shift (PS) evaluation while under anesthesia preoperatively. Data from these modules, SVM methods and clinical grading shift from the study examiner were used to calculate PS grades.
Overall, 69 knees were given a PS grade of 0, 23 received a grade of +1, 27 received a grade of +2 and three were graded as +3 by the SVM analysis. These grades were found to be 77% clinically correct, whereas PS grades were within ±1 grade of the value deemed clinically correct in 98% of knees.
The regression/SVM method of diagnosing ACL deficiency was found to be 97% accurate with no false positives and 6% false negatives, according to the researchers. – by Christian Ingram
Disclosures: Borgstrom reports a current U.S. patent pending (PCT/US2013/026229) relevant to the device used in this study. Please see the full study for a list of all other authors’ relevant financial disclosures.
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