首页|University Magna Graecia Reports Findings in Machine Learning (Multimodal imagin g and electrophysiological study in the differential diagnosis of rest tremor)
University Magna Graecia Reports Findings in Machine Learning (Multimodal imagin g and electrophysiological study in the differential diagnosis of rest tremor)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Catanzaro, Italy, by New sRx journalists, research stated, "Distinguishing tremor-dominant Parkinson's di sease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data." The news correspondents obtained a quote from the research from University Magna Graecia, "We enrolled 72 patients including 40 tPD patients and 32 rET patients , and 45 control subjects (HC). RT electrophysiological features (frequency, amp litude, and phase) were calculated using surface electromyography (sEMG). Severa l MRI morphometric variables (cortical thickness, surface area, cortical/subcort ical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a treebased classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET pati ents. Both structural MRI and sEMG data showed acceptable performance in disting uishing the two patient groups. Models based on electrophysiological data perfor med slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; = 0.0071). The top-performing model used a combination of sEMG fea tures (amplitude and phase) and MRI data (cortical volumes, surface area, and me an curvature), reaching AUC: 0.97 ? 0.03 and outperforming models using separate ly either MRI ( = 0.0001) or EMG data ( = 0.0231). In the best model, the most i mportant feature was the RT phase."
CatanzaroItalyEuropeCyborgsDiagn ostics and ScreeningEmerging TechnologiesHealth and MedicineMachine Learni ng
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