QNotes, Vol. 7, Issue 6
Qmetrics will be at NASS 2018 to represent ePoster P42, “Preliminary Validation of a Machine-Learning MRI-Based Algorithm to Identify Patients with Functional Impairment Associated with Lumbar Stenosis,” authored by Jose G Tamez-Pena, PhD1; Saara M Totterman, MD, PhD2; Maria Frazer, BS3; Joshua M Farber, MD4; Patricia C Gonzalez, BS, MBA5; Stephanie Northwood, BA3; Edward H Schreyer, BS2; Nicholas Olin, BS6; and John Markman, MD7. (1Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico; 2Qmetrics Technologies, Pittsford, NY, US; 3Rochester, NY, US; 4Pittsford, NY, US; 5IMITEK S C, San Pedro Garza Garcia, Neuvo Leon, Mexico; 6University of Rochester School of Medicine and Dentistry, Rochester, NY, US; 7University of Rochester, Rochester, NY, US)
The ePoster summarizes a study whose purpose was to identify MRI features that correlate with the evoked symptom pattern of neurogenic claudication using a novel machine learning algorithm. Qmetrics provided the MRI analysis and machine learning algorithm/data.
The North American Spine Society (NASS) is a global multidisciplinary medical society that utilizes education, research and advocacy to foster the highest quality, ethical, value- and evidence-based spine care for patients.
Anyone interested in more information about Qmetrics should contact us.