scholarly journals Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology

2021 ◽  
pp. 29-36
Author(s):  
Anzar Abbas ◽  
Vijay Yadav ◽  
Emma Smith ◽  
Elizabeth Ramjas ◽  
Sarah B. Rutter ◽  
...  
Keyword(s):  
Computer Vision ◽  
Symptom Severity ◽  
Head Movement ◽  
Negative Symptoms ◽  
Negative Relationship ◽  
Video Data ◽  
Remote Measurement ◽  
Healthy Controls ◽  
Motor Functioning ◽  
Clinical Scores

<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.

scholarly journals Computer vision-based assessment of motor functioning in schizophrenia: Use of smartphones for remote measurement of schizophrenia symptomatology

2020 ◽  
Author(s):  
Anzar Abbas ◽  
Vijay Yadav ◽  
Emma Smith ◽  
Elizabeth Ramjas ◽  
Sarah B Rutter ◽  
...  
Keyword(s):  
Computer Vision ◽  
Symptom Severity ◽  
Head Movement ◽  
Negative Symptoms ◽  
Negative Relationship ◽  
Video Data ◽  
Remote Measurement ◽  
Healthy Controls ◽  
Motor Functioning ◽  
Clinical Scores

Introduction: 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. Methods: 18 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 head movement through a pre-trained 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. Results: A logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (p < 0.05). Linear regression between head movement and clinical scores of schizophrenia symptom severity showed that head movement has a negative relationship with schizophrenia symptom severity (p < 0.05), primarily with negative symptoms of schizophrenia. Conclusions: 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.

scholarly journals Body dysmorphic disorder and self-esteem: a meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nora Kuck ◽  
Lara Cafitz ◽  
Paul-Christian Bürkner ◽  
Laura Hoppen ◽  
Sabine Wilhelm ◽  
...  
Keyword(s):  
Depressive Symptom ◽  
Symptom Severity ◽  
Body Dysmorphic Disorder ◽  
Meta Analysis ◽  
Negative Relationship ◽  
Self Esteem ◽  
Sample Type ◽  
Healthy Controls ◽  
Partial Correlations ◽  
Depressive Symptom Severity

Abstract Objective Body dysmorphic disorder (BDD) is associated with low self-esteem. The aim of this meta-analysis was to examine the strength of the cross-sectional relationship between BDD symptom severity and global self-esteem in individuals with BDD, mentally healthy controls, community or student samples, and cosmetic surgery patients. Moreover, the role of depressive symptom severity in this relationship and other moderating factors were investigated. Methods A keyword-based literature search was performed to identify studies in which BDD symptoms and global self-esteem were assessed. Random effects meta-analysis of Fisher’s z-transformed correlations and partial correlations controlling for the influence of depressive symptom severity was conducted. In addition to meta-analysis of the observed effects, we corrected the individual correlations for variance restrictions to address varying ranges of BDD symptom severity across samples. Results Twenty-five studies with a total of 6278 participants were included. A moderately negative relationship between BDD symptom severity and global self-esteem was found (r = −.42, CI = [−.48, −.35] for uncorrected correlations, r = −.45, CI = [−.51, −.39] for artifact-corrected correlations). A meta-analysis of partial correlations revealed that depressive symptom severity could partly account for the aforementioned relationship (pr = −.20, CI = [−.25, −.15] for uncorrected partial correlations, pr = −.23, CI = [−.28, −.17] for artifact-corrected partial correlations). The sample type (e.g., individuals with BDD, mentally healthy controls, or community samples) and diagnosis of BDD appeared to moderate the relationship only before artifact correction of effect sizes, whereas all moderators were non-significant in the meta-analysis of artifact-corrected correlations. Conclusions The findings demonstrate that low self-esteem is an important hallmark of BDD beyond the influence of depressive symptoms. It appears that negative evaluation in BDD is not limited to appearance but also extends to other domains of the self. Altogether, our findings emphasize the importance of addressing self-esteem and corresponding core beliefs in prevention and treatment of BDD.

scholarly journals Body dysmorphic disorder and self-esteem: A meta-analysis

2020 ◽  
Author(s):  
Nora Kuck ◽  
Lara Cafitz ◽  
Paul - Christian Bürkner ◽  
Laura Nosthoff-Horstmann ◽  
Sabine Wilhelm ◽  
...  
Keyword(s):  
Depressive Symptom ◽  
Symptom Severity ◽  
Body Dysmorphic Disorder ◽  
Meta Analysis ◽  
Negative Relationship ◽  
Self Esteem ◽  
Sample Type ◽  
Healthy Controls ◽  
Partial Correlations ◽  
Depressive Symptom Severity

ObjectiveThe aim of this meta-analysis was to examine the strength of the cross-sectional relationship between body dysmorphic disorder (BDD) symptom severity and global self-esteem in individuals with BDD, mentally healthy controls, community or student samples, and cosmetic surgery patients. Moreover, the role of depressive symptom severity in this relationship and other moderating factors were investigated. MethodsA keyword-based literature search was performed to identify studies in which BDD symptoms and global self-esteem were assessed. Random effects meta-analysis of Fisher’s z-transformed correlations and partial correlations controlling for the influence of depressive symptom severity was conducted. In addition to meta-analysis of the observed effects, we corrected the individual correlations for variance restrictions to address varying ranges of BDD symptom severity across samples. ResultsTwenty-five studies with a total of 6149 participants were included. A moderately negative relationship between BDD symptom severity and global self-esteem was found (r = -.42, CI = [-.48, -.35] for uncorrected correlations, r = -.45, CI = [-.51, -.39] for artifact-corrected correlations). A meta-analysis of partial correlations revealed that depressive symptom severity could partly account for the aforementioned relationship (pr = -.2, CI = [-.25, -.15] for uncorrected partial correlations, pr = -.23, CI = [-.28, -.17] for artifact-corrected partial correlations). The sample type (e.g., individuals with BDD, mentally healthy controls, or community samples) and diagnosis of BDD appeared to moderate the relationship only before artifact correction of effect sizes, whereas all moderators were non-significant in the meta-analysis of artifact-corrected correlations. ConclusionsThe findings demonstrate that low self-esteem is an important hallmark of BDD beyond the influence of depressive symptoms. It appears that negative evaluation in BDD is not limited to appearance but also extends to other domains of the self. Altogether, our findings emphasize the importance of addressing self-esteem and corresponding core beliefs in prevention and treatment of BDD.

scholarly journals Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients (Preprint)

2020 ◽  
Author(s):  
Isaac Galatzer-Levy ◽  
Anzar Abbas ◽  
Anja Ries ◽  
Stephanie Homan ◽  
Laura Sels ◽  
...  
Keyword(s):  
Head Movement ◽  
Suicide Risk ◽  
Routine Care ◽  
Video Data ◽  
Auditory Information ◽  
Motor Functioning ◽  
Clinical Interviews ◽  
And Gender ◽  
Key Indicators ◽  
Facial Expressivity

BACKGROUND Multiple symptoms of suicide risk are assessed based on visual and auditory information including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. OBJECTIVE The goal of this work was to determine if key indicators of the suicide severity could be measured in an objective and automated manner using video data captured during clinical interviews that provided structured questions, but were otherwise kept deliberately open to mimic psychiatric interviewing in routine care. METHODS In the current study we utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide severity using the Beck Suicide Scale (BSS), controlling for age and gender using multiple linear regression. RESULTS Suicide severity was associated with multiple visual and auditory markers including speech prevalence (β = -0.68; P = .017, r2 = .40, overall expressivity (β = -0.46; P = 0.10, r2 = .27), and head movement measured as head pitch variability (β = -1.24; P = .006, r2 = .48) and head yaw variability (β = -0.54; p = .055, r2 = .32). CONCLUSIONS Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and significant linear associations with severity of suicidal ideation.

Differentiating Laparoscopic Skills of Trainees with Computer Vision Based Metrics

2021 ◽  
Vol 65 (1) ◽  
pp. 304-308
Author(s):  
Shiyu Deng ◽  
Chaitanya Kulkarni ◽  
Tianzi Wang ◽  
Jacob Hartman-Kenzler ◽  
Laura E. Barnes ◽  
...  
Keyword(s):  
Computer Vision ◽  
Transfer Task ◽  
High Sensitivity ◽  
Video Data ◽  
Machine Learning Algorithms ◽  
Fixation Rate ◽  
Skill Levels ◽  
Stationary Target ◽  
Practice Trials ◽  
Context Dependent

Context dependent gaze metrics, derived from eye movements explicitly associated with how a task is being performed, are particularly useful for formative assessment that includes feedback on specific behavioral adjustments for skill acquisitions. In laparoscopic surgery, context dependent gaze metrics are under investigated and commonly derived by either qualitatively inspecting the videos frame by frame or mapping the fixations onto a static surgical task field. This study collected eye-tracking and video data from 13 trainees practicing the peg transfer task. Machine learning algorithms in computer vision were employed to derive metrics of tool speed, fixation rate on (moving or stationary) target objects, and fixation rate on tool-object combination. Preliminary results from a clustering analysis on the measurements from 499 practice trials indicated that the metrics were able to differentiate three skill levels amongst the trainees, suggesting high sensitivity and potential of context dependent gaze metrics for surgical assessment.

scholarly journals The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions

10.2196/27663 ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. e27663
Author(s):  
Sandersan Onie ◽  
Xun Li ◽  
Morgan Liang ◽  
Arcot Sowmya ◽  
Mark Erik Larsen
Keyword(s):  
Computer Vision ◽  
Early Intervention ◽  
Suicide Attempt ◽  
Detection System ◽  
Automated Detection ◽  
Video Data ◽  
Narrative Review ◽  
Closed Circuit ◽  
Closed Circuit Television ◽  
Area Of Interest

Background Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors at metro stations, and using computer vision to identify a suicide attempt in progress. However, there has been no review of suicide research and interventions using CCTV and video. Objective The objective of this study was to review the literature to understand how CCTV and video data have been used in understanding and preventing suicide. Furthermore, to more fully capture progress in the field, we report on an ongoing study to respond to an identified gap in the narrative review, by using a computer vision–based system to identify behaviors prior to a suicide attempt. Methods We conducted a search using the keywords “suicide,” “cctv,” and “video” on PubMed, Inspec, and Web of Science. We included any studies which used CCTV or video footage to understand or prevent suicide. If a study fell into our area of interest, we included it regardless of the quality as our goal was to understand the scope of how CCTV and video had been used rather than quantify any specific effect size, but we noted the shortcomings in their design and analyses when discussing the studies. Results The review found that CCTV and video have primarily been used in 3 ways: (1) to identify risk factors for suicide (eg, inferring depression from facial expressions), (2) understanding suicide after an attempt (eg, forensic applications), and (3) as part of an intervention (eg, using computer vision and automated systems to identify if a suicide attempt is in progress). Furthermore, work in progress demonstrates how we can identify behaviors prior to an attempt at a hotspot, an important gap identified by papers in the literature. Conclusions Thus far, CCTV and video have been used in a wide array of applications, most notably in designing automated detection systems, with the field heading toward an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for preattempt behaviors, which may allow for early intervention.

scholarly journals Patients’ Measurement Priorities for Remote Measurement Technologies to Aid Chronic Health Conditions: Qualitative Analysis (Preprint)

2019 ◽  
Author(s):  
Sara Simblett ◽  
Faith Matcham ◽  
Hannah Curtis ◽  
Ben Greer ◽  
Ashley Polhemus ◽  
...  
Keyword(s):  
Symptom Severity ◽  
Outcome Measurement ◽  
Holistic Approach ◽  
Chronic Health Conditions ◽  
Health Conditions ◽  
Measurement Technology ◽  
Remote Measurement ◽  
Long Term Conditions ◽  
Chronic Health ◽  
End Point

BACKGROUND Remote measurement technology (RMT), including the use of mobile phone apps and wearable devices, may provide the opportunity for real-world assessment and intervention that will streamline clinical input for years to come. In order to establish the benefits of this approach, we need to operationalize what is expected in terms of a successful measurement. We focused on three clinical long-term conditions where a novel case has been made for the benefits of RMT: major depressive disorder (MDD), multiple sclerosis (MS), and epilepsy. OBJECTIVE The aim of this study was to conduct a consultation exercise on the clinical end point or outcome measurement priorities for RMT studies, drawing on the experiences of people with chronic health conditions. METHODS A total of 24 participants (16/24 women, 67%), ranging from 28 to 65 years of age, with a diagnosis of one of three chronic health conditions―MDD, MS, or epilepsy―took part in six focus groups. A systematic thematic analysis was used to extract themes and subthemes of clinical end point or measurement priorities. RESULTS The views of people with MDD, epilepsy, and MS differed. Each group highlighted unique measurements of importance, relevant to their specific needs. Although there was agreement that remote measurement could be useful for tracking symptoms of illness, some symptoms were specific to the individual groups. Measuring signs of wellness was discussed more by people with MDD than by people with MS and epilepsy. However, overlap did emerge when considering contextual factors, such as life events and availability of support (MDD and epilepsy) as well as ways of coping (epilepsy and MS). CONCLUSIONS This is a unique study that puts patients’ views at the forefront of the design of a clinical study employing novel digital resources. In all cases, measuring symptom severity is key; people want to know when their health is getting worse. Second, symptom severity needs to be placed into context. A holistic approach that, in some cases, considers signs of wellness as well as illness, should be the aim of studies employing RMT to understand the health of people with chronic conditions.

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