Building a Machine-learning Framework to Remotely Assess Parkinson's Disease Using Smartphones.

Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency. Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD

Structure-Based Prediction of Asparagine and Aspartate Degradation Sites in Antibody Variable Regions

Monoclonal antibodies (mAbs) and proteins containing antibody domains are the most prevalent class of biotherapeutics in diverse indication areas. Today, established techniques such as immunization or phage display allow for an efficient generation of new mAbs. Besides functional properties, the stability of future therapeutic mAbs is a key selection criterion which is essential for the development of a drug candidate into a marketed product. Therapeutic proteins may degrade via asparagine (Asn) deamidation and aspartate (Asp) isomerization, but the factors responsible for such degradation remain poorly understood. We studied the structural properties of a large, uniform dataset of Asn and Asp residues in the variable domains of antibodies. Their structural parameters were correlated with the degradation propensities measured by mass spectrometry. We show that degradation hotspots can be characterized by their conformational flexibility, the size of the C-terminally flanking amino acid residue, and secondary structural parameters. From these results we derive an accurate in silico prediction method for the degradation propensity of both Asn and Asp residues in the complementarity-determining regions (CDRs) of mAbs

Predicting motor, cognitive and functional impairment in Parkinson’s

Objective We recently demonstrated that 998 features derived from a simple 7‐minute smartphone test could distinguish between controls, people with Parkinson's and people with idiopathic Rapid Eye Movement sleep behavior disorder, with mean sensitivity/specificity values of 84.6‐91.9%. Here, we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson's. Methods A total of 237 participants with Parkinson's (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest, and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18‐month follow‐up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10‐fold and subject‐wise cross validation schemes. Results Baseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment, and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10‐fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained. Interpretation We demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualized predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome

Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8-15%. A lack of transparency of "black-box" deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions

Smartphone-and smartwatch-based remote characterisation of ambulation in multiple sclerosis during the two-minute walk test

status: accepte

Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test

status: publishe

Adherence and satisfaction of smartphone- And smartwatch-based remote active testing and passive monitoring in people with multiple sclerosis : Nonrandomized interventional feasibility study

Càrrega feta de Scopus d'articles UAB 2019 (Gold, hybrid o Bronze) procedents de l'Observatori d'Accés Obert (càrrega maig 2020). Compte! Cal comprovar la versió permesa per l'editor en els bronze.We would like to thank all patients, their families, and the investigators who participated in this trial. This research was funded by F Hoffmann-La Roche Ltd, Basel, Switzerland. We would like to thank the following employees from F Hoffmann-La Roche Ltd who supported and contributed to the study: Atieh Bamdadian, Alessandro Barbato, Jan Beckmann, Sandro Fritz, Nicholas Pierce Heinemeier, Timothy Kilchenmann, Lito Kriara, Bernd Laub, Grégoire Pointeau, Caroline Polakowska, Marcin Puhacz, Cedric Simillion, Jens Schjodt-Eriksen, Jöerg Sprengel, Ralf Stubner, and Krzysztof Trybus. Writing and editorial assistance for this manuscript was provided by Heather Latimer from Articulate Science, United Kingdom, and funded by F Hoffmann-La Roche Ltd.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: F Hoffmann-La Roche Ltd, Basel, Switzerland, provided financial support for the study and publication of this manuscript.XM has received speaker honoraria and travel expense reimbursement for participation in scientific meetings, been a steering committee member of clinical trials, or served on advisory boards of clinical trials for Actelion, Biogen, Celgene, Merck, Novartis, Oryzon, Roche, Sanofi Genzyme, and Teva Pharmaceutical. JG has received grants or research support from Biogen, Genentech Inc, and S3 Group and has received compensation for a nonbranded resident and fellow education seminar supported by Biogen. SLH serves on the scientific advisory boards for Annexon, Symbiotix, Bionure, and Molecular Stethoscope, is on the board of trustees for Neurona Therapeutics, and has received travel reimbursement and writing assistance from F Hoffmann-La Roche Ltd for CD20-related meetings and presentations. LJ is an employee of Genentech Inc and a shareholder of F Hoffmann-La Roche Ltd. MB, JS, and CG are employees and shareholders of F Hoffmann-La Roche Ltd. AS, FL, and JvB are employees of F Hoffmann-La Roche Ltd. CB and ML are contractors for F Hoffmann-La Roche Ltd. SB was an employee of F Hoffmann-La Roche Ltd during the completion of the work related to this manuscript. SB is now an employee of Biogen (Cambridge, MA), which was not in any way associated with this study. LM and PM have nothing to disclose.Background: Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits. Objective: The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery. Methods: People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed. Results: People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis. Conclusions: People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings