Digital Biomarkers最新文献

{"title":"Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology.","authors":"Anzar Abbas,&nbsp;Vijay Yadav,&nbsp;Emma Smith,&nbsp;Elizabeth Ramjas,&nbsp;Sarah B Rutter,&nbsp;Caridad Benavidez,&nbsp;Vidya Koesmahargyo,&nbsp;Li Zhang,&nbsp;Lei Guan,&nbsp;Paul Rosenfield,&nbsp;Mercedes Perez-Rodriguez,&nbsp;Isaac R Galatzer-Levy","doi":"10.1159/000512383","DOIUrl":"https://doi.org/10.1159/000512383","url":null,"abstract":"<p><strong>Introduction: </strong>Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote \"digital phenotyping\" of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones.</p><p><strong>Methods: </strong>Eighteen patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify the Euclidean distance of head movement between frames through a pretrained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed.</p><p><strong>Results: </strong>The rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame; <i>p</i> = 0.01), and a logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (<i>p</i> = 0.02). Linear regression between head movement and clinical scores of schizophrenia showed that head movement has a negative relationship with schizophrenia symptom severity (<i>p</i> = 0.04), primarily with negative symptoms of schizophrenia.</p><p><strong>Conclusions: </strong>Remote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"29-36"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25390996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Detection of Vancomycin in Sweat: A Next-Generation Digital Surrogate Marker for Antibiotic Tissue Penetration: A Pilot Study.","authors":"Noé Brasier, Andreas Widmer, Michael Osthoff, Markus Mutke, Fiorangelo De Ieso, Pascale Brasier-Lutz, Kitty Brown, Linxing Yao, Corey D Broeckling, Jessica Prenni, Jens Eckstein","doi":"10.1159/000512947","DOIUrl":"10.1159/000512947","url":null,"abstract":"<p><strong>Background: </strong>Assuring adequate antibiotic tissue concentrations at the point of infection, especially in skin and soft tissue infections, is pivotal for an effective treatment and cure. Despite the global issue, a reliable AB monitoring test is missing. Inadequate antibiotic treatment leads to the development of antimicrobial resistances and toxic side effects. β-lactam antibiotics were already detected in sweat of patients treated with the respective antibiotics intravenously before. With the emergence of smartphone-based biosensors to analyse sweat on the spot of need, next-generation molecular digital biomarkers will be increasingly available for a non-invasive pharmacotherapy monitoring.</p><p><strong>Objective: </strong>Here, we investigated if the glycopeptide antibiotic vancomycin is detectable in sweat samples of in-patients treated with intravenous vancomycin.</p><p><strong>Methods: </strong>Eccrine sweat samples were collected using the Macroduct Sweat Collector®. Along every sweat sample, a blood sample was taken. Bio-fluid analysis was performed by Ultra-high Pressure Liquid Chromatograph-Tandem Quadrupole Mass Spectrometry coupled with tandem mass spectrometry.</p><p><strong>Results: </strong>A total of 5 patients were included. Results demonstrate that vancomycin was detected in 5 out of 5 sweat samples. Specifically, vancomycin concentrations ranged from 0.011 to 0.118 mg/L in sweat and from 4.7 to 8.5 mg/L in blood.</p><p><strong>Conclusion: </strong>Our results serve as proof-of-concept that vancomycin is detectable in eccrine sweat and may serve as a surrogate marker for antibiotic tissue penetration. A targeted vancomycin treatment is crucial in patients with repetitive need for antibiotics and a variable antibiotic distribution such as in peripheral artery disease to optimize treatment effectiveness. If combined with on-skin smartphone-based biosensors and smartphone applications, the detection of antibiotic concentrations in sweat might enable a first digital, on-spot, lab-independent and non-invasive therapeutic drug monitoring in skin and soft tissue infections.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"24-28"},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879282/pdf/dib-0005-0024.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25390999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of Machine Learning-Based Computer Vision and Voice Analysis to Derive Digital Biomarkers of Cognitive Functioning in Trauma Survivors.","authors":"Katharina Schultebraucks,&nbsp;Vijay Yadav,&nbsp;Isaac R Galatzer-Levy","doi":"10.1159/000512394","DOIUrl":"https://doi.org/10.1159/000512394","url":null,"abstract":"<p><strong>Background: </strong>Alterations in multiple domains of cognition have been observed in individuals who have experienced a traumatic stressor. These domains may provide important insights in identifying underlying neurobiological dysfunction driving an individual's clinical response to trauma. However, such assessments are burdensome, costly, and time-consuming. To overcome barriers, efforts have emerged to measure multiple domains of cognitive functioning through the application of machine learning (ML) models to passive data sources.</p><p><strong>Methods: </strong>We utilized automated computer vision and voice analysis methods to extract facial, movement, and speech characteristics from semi-structured clinical interviews in 81 trauma survivors who additionally completed a cognitive assessment battery. A ML-based regression framework was used to identify variance in visual and auditory measures that relate to multiple cognitive domains.</p><p><strong>Results: </strong>Models derived from visual and auditory measures collectively accounted for a large variance in multiple domains of cognitive functioning, including motor coordination (<i>R</i>² = 0.52), processing speed (<i>R</i>² = 0.42), emotional bias (<i>R</i>² = 0.52), sustained attention (<i>R</i>² = 0.51), controlled attention (<i>R</i>² = 0.44), cognitive flexibility (<i>R</i>² = 0.43), cognitive inhibition (<i>R</i>² = 0.64), and executive functioning (<i>R</i>² = 0.63), consistent with the high test-retest reliability of traditional cognitive assessments. Face, voice, speech content, and movement have all significantly contributed to explaining the variance in predicting functioning in all cognitive domains.</p><p><strong>Conclusions: </strong>The results demonstrate the feasibility of automated measurement of reliable proxies of cognitive functioning through low-burden passive patient evaluations. This makes it easier to monitor cognitive functions and to intervene earlier and at a lower threshold without requiring a time-consuming neurocognitive assessment by, for instance, a licensed psychologist with specialized training in neuropsychology.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"16-23"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25391025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proof of Concept for an \"eyePhone\" App to Measure Video Head Impulses.","authors":"T Maxwell Parker,&nbsp;Nathan Farrell,&nbsp;Jorge Otero-Millan,&nbsp;Amir Kheradmand,&nbsp;Ayodele McClenney,&nbsp;David E Newman-Toker","doi":"10.1159/000511287","DOIUrl":"https://doi.org/10.1159/000511287","url":null,"abstract":"<p><strong>Objective: </strong>Differentiating benign from dangerous causes of dizziness or vertigo presents a major diagnostic challenge for many clinicians. Bedside presentations of peripheral vestibular disorders and posterior fossa strokes are often indistinguishable other than by a few subtle vestibular eye movements. The most challenging of these to interpret is the head impulse test (HIT) of vestibulo-ocular reflex (VOR) function. There have been major advances in portable video-oculography (VOG) quantification of the video HIT (vHIT), but these specialized devices are not routinely available in most clinical settings. As a first step towards smartphone-based diagnosis of strokes in patients presenting vestibular symptoms, we sought proof of concept that we could use a smartphone application (\"app\") to accurately record the vHIT.</p><p><strong>Methods: </strong>This was a cross-sectional agreement study comparing a novel index test (smartphone-based vHIT app) to an accepted reference standard test (VOG-based vHIT) for measuring VOR function. We recorded passive (examiner-performed) vHIT sequentially with both methods in a convenience sample of patients visiting an otoneurology clinic. We quantitatively correlated VOR gains (ratio of eye to head movements during the HIT) from each side/ear and experts qualitatively assessed the physiologic traces by the two methods.</p><p><strong>Results: </strong>We recruited 11 patients; 1 patient's vHIT could not be reliably quantified with either device. The novel and reference test VOR gain measurements for each ear (<i>n</i> = 20) were highly correlated (Pearson's <i>r</i> = 0.9, <i>p</i> = 0.0000001) and, qualitatively, clinically equivalent.</p><p><strong>Conclusions: </strong>This preliminary study provides proof of concept that an \"eyePhone\" app could be used to measure vHIT and eventually developed to diagnose vestibular strokes by smartphone.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000511287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25391021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey on Acceptance of Passive Technology Monitoring for Early Detection of Cognitive Impairment.","authors":"Sylvia Josephy-Hernandez,&nbsp;Catherine Norise,&nbsp;Jee-Young Han,&nbsp;Kara M Smith","doi":"10.1159/000512207","DOIUrl":"https://doi.org/10.1159/000512207","url":null,"abstract":"<p><strong>Introduction: </strong>Digital biomarkers may act as a tool for early detection of changes in cognition. It is important to understand public perception of technologies focused on monitoring cognition to better guide the design of these tools and inform patients appropriately about the associated risks and benefits. Health care systems may also play a role in the clinical, legal, and financial implications of such technologies.</p><p><strong>Objective: </strong>To evaluate public opinion on the use of passive technology for monitoring cognition.</p><p><strong>Methods: </strong>This was a one-time, Internet-based survey conducted in English and Spanish.</p><p><strong>Results: </strong>Within the English survey distributed in the USA (<i>n</i> = 173), 58.1% of respondents would be highly likely to agree to passive monitoring of cognition via a smartphone application. Thirty-eight percent of those with a higher degree of experience with technology were likely to agree to monitoring versus 20% of those with less experience with technology (<i>p</i> = 0.003). Sixty-two percent of non-health-care professionals were likely to agree to monitoring versus 45% of health-care workers (<i>p</i> = 0.012). There were significant concerns regarding privacy (<i>p</i> < 0.01). We compared the surveys answered in Spanish in Costa Rica via logistic regression (<i>n</i> = 43, total <i>n</i> = 216), adjusting for age, education level, health-care profession, owning a smartphone, experience with technology, and perception of cognitive decline. Costa Rican/Spanish-speaking respondents were 7 times more likely to select a high probability of agreeing to such a technology (<i>p</i> < 0.01). English-speaking respondents from the USA were 5 times more likely to be concerned about the impact on health insurance (<i>p</i> = 0.001) and life insurance (<i>p</i> = 0.01).</p><p><strong>Conclusions: </strong>Understanding public perception and ethical implications should guide the design of digital biomarkers for cognition. Privacy and the health-care system in which the participants take part are 2 major factors to be considered. It is the responsibility of researchers to convey the ethical and legal implications of cognition monitoring.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"9-15"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25391023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prelims","authors":"","doi":"10.1159/000512783","DOIUrl":"https://doi.org/10.1159/000512783","url":null,"abstract":"","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 1","pages":"I - IV"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45326396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NODE. Health Meeting Report and Panel Discussion - The FDA's Changing Regulatory Landscape for Digital Health Technologies and Digital Health Innovation during COVID-19: A Discussion with Eric Topol and Bakul Patel, Moderated by Aenor Sawyer.","authors":"Anna Andoni, Shayann Ramedani, Benjamin I Rosner, Aenor Sawyer","doi":"10.1159/000512681","DOIUrl":"10.1159/000512681","url":null,"abstract":"","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 3","pages":"128-133"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Health, Telehealth, and Primary Care Post-COVID: A Discussion with Kim Boyd and Joe Kvedar, Moderated by Benjamin Rosner.","authors":"Benjamin I Rosner, Masha Morozov, Anna Andoni","doi":"10.1159/000513229","DOIUrl":"10.1159/000513229","url":null,"abstract":"","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 3","pages":"123-127"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38816767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Repeatability of Commonly Used Speech and Language Features for Clinical Applications.","authors":"Gabriela M Stegmann,&nbsp;Shira Hahn,&nbsp;Julie Liss,&nbsp;Jeremy Shefner,&nbsp;Seward B Rutkove,&nbsp;Kan Kawabata,&nbsp;Samarth Bhandari,&nbsp;Kerisa Shelton,&nbsp;Cayla Jessica Duncan,&nbsp;Visar Berisha","doi":"10.1159/000511671","DOIUrl":"https://doi.org/10.1159/000511671","url":null,"abstract":"<p><strong>Introduction: </strong>Changes in speech have the potential to provide important information on the diagnosis and progression of various neurological diseases. Many researchers have relied on open-source speech features to develop algorithms for measuring speech changes in clinical populations as they are convenient and easy to use. However, the repeatability of open-source features in the context of neurological diseases has not been studied.</p><p><strong>Methods: </strong>We used a longitudinal sample of healthy controls, individuals with amyotrophic lateral sclerosis, and individuals with suspected frontotemporal dementia, and we evaluated the repeatability of acoustic and language features separately on these 3 data sets.</p><p><strong>Results: </strong>Repeatability was evaluated using intraclass correlation (ICC) and the within-subjects coefficient of variation (WSCV). In 3 sets of tasks, the median ICC were between 0.02 and 0.55, and the median WSCV were between 29 and 79%.</p><p><strong>Conclusion: </strong>Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. Researchers should exercise caution when developing digital health models with open-source speech features. We provide a detailed summary of feature-by-feature repeatability results (ICC, WSCV, SE of measurement, limits of agreement for WSCV, and minimal detectable change) in the online supplementary material so that researchers may incorporate repeatability information into the models they develop.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 3","pages":"109-122"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000511671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38816765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies.","authors":"Matthias Tietsch, Amir Muaremi, Ieuan Clay, Felix Kluge, Holger Hoefling, Martin Ullrich, Arne Küderle, Bjoern M Eskofier, Arne Müller","doi":"10.1159/000511611","DOIUrl":"10.1159/000511611","url":null,"abstract":"<p><p>Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects' habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"50-58"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768099/pdf/dib-0004-0050.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}