Prediction of Obliteration After the Gamma Knife Radiosurgery of Arteriovenous Malformations Using Hand-Crafted Radiomics and Deep-Learning Methods.

Introduction: Brain arteriovenous malformations (bAVMs) are vascular abnormalities that can be treated with embolization or radiotherapy to prevent the risk of future rupture. In this study, we use hand-crafted radiomics and deep learning techniques to predict favorable vs. unfavorable outcomes following Gamma Knife radiosurgery (GKRS) of bAVMs and compare their prediction performances.

Utilizing kinematic analysis of postural instability as an objective measure to aid in distinguishing between normal pressure hydrocephalus and Parkinson's disease.

Objective: Patients with normal pressure hydrocephalus (NPH) and Parkinson's Disease (PD) can clinically appear quite similar at baseline evaluation. We sought to investigate the use of kinematic assessment of postural instability (PI) using inertial measurement units (IMUs) as a mechanism of differentiation between the two disease processes.

Methods: 20 patients with NPH, 55 patients with PD, and 56 age-matched, healthy controls underwent quantitative pull test examinations while wearing IMUs at baseline.

Percutaneous n-butyl cyanoacrylate embolization of cervical metastatic disease via an anterolateral approach.

Symptomatic vascular spinal metastases will benefit from pre-operative tumor embolization - percutaneous with or without adjunct endovascular embolization. However, when a transpedicular approach is not feasible, an anterolateral approach may be a viable alternative. The authors report a 57-year-old woman with prior C3-T1 instrumentation who presented with acute cord compression from a pathologic C5 vertebral body fracture related to metastatic renal cell carcinoma.

Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients.

The use of wearable sensors in movement disorder patients such as Parkinson's disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects.