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Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection.

ABSTRACT: BACKGROUND:Objective assessments of movement impairment are needed to support clinical trials and facilitate diagnosis. The objective of the current study was to determine if a rapid web-based computer mouse test (Hevelius) could detect and accurately measure ataxia and parkinsonism. METHODS:Ninety-five ataxia, 46 parkinsonism, and 29 control participants and 229,017 online participants completed Hevelius. We trained machine-learning models on age-normalized Hevelius features to (1) measure severity and disease progression and (2) distinguish phenotypes from controls and from each other. RESULTS:Regression model estimates correlated strongly with clinical scores (from r =?0.66 for UPDRS dominant arm total to r =?0.83 for the Brief Ataxia Rating Scale). A disease change model identified ataxia progression with high sensitivity. Classification models distinguished ataxia or parkinsonism from healthy controls with high sensitivity (?0.91) and specificity (?0.90). CONCLUSIONS:Hevelius produces a granular and accurate motor assessment in a few minutes of mouse use and may be useful as an outcome measure and screening tool. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

SUBMITTER: Gajos KZ

PROVIDER: S-EPMC7028247 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection.

Gajos Krzysztof Z KZ Reinecke Katharina K Donovan Mary M Stephen Christopher D CD Hung Albert Y AY Schmahmann Jeremy D JD Gupta Anoopum S AS

Movement disorders : official journal of the Movement Disorder Society 20191107 2

<h4>Background</h4>Objective assessments of movement impairment are needed to support clinical trials and facilitate diagnosis. The objective of the current study was to determine if a rapid web-based computer mouse test (Hevelius) could detect and accurately measure ataxia and parkinsonism.<h4>Methods</h4>Ninety-five ataxia, 46 parkinsonism, and 29 control participants and 229,017 online participants completed Hevelius. We trained machine-learning models on age-normalized Hevelius features to ( ...[more]

PMID: 31769069

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Project description:Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34-82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F[Formula: see text] score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of [Formula: see text], 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

Dataset Information

Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection.

Publications

Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection.

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