Utilization of Machine Learning-Based Computer Vision and Voice Analysis to Derive Digital Biomarkers of Cognitive Functioning in Trauma Survivors

Applicability of artificial intelligence in neuropsychological rehabilitation of patients with brain injury

Neuropsychological rehabilitation plays a critical role in helping those recovering from brain injuries restore cognitive and functional abilities. Artificial Intelligence, with its potential, may revolutionize this field further; therefore, this article explores applications of AI for neuropsychological rehabilitation of patients suffering brain injuries. This study employs a systematic review methodology to comprehensively review existing literature regarding Artificial Intelligence use in neuropsychological rehabilitation for people with brain injuries. The systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search of electronic databases (PubMed, Scopus, PsycINFO, etc.) showed a total of 212 potentially relevant articles. After removing duplicates and screening titles and abstracts, 186 articles were selected for assessment. Following the assessment, 55 articles met the inclusion criteria and were included in this systematic review. A thematic analysis approach is employed to analyze and synthesize the extracted data. Themes, patterns, and trends are identified across the included studies, allowing for a comprehensive understanding of the applicability of AI in neuropsychological rehabilitation for patients with brain injuries. The identified topics were: AI Applications in Diagnostics of Brain Injuries and their Neuropsychological Repercussions; AI in Personalization and Monitoring of Neuropsychological Rehabilitation for traumatic brain injury (TBI); Leveraging AI for Predicting and Optimizing Neuropsychological Rehabilitation Outcomes in TBI Patients. Based on the review, it was concluded that AI has the potential to enhance neuropsychological rehabilitation for patients with brain injuries. By leveraging AI techniques, personalized rehabilitation programs can be developed, treatment outcomes can be predicted, and interventions can be optimized.

Quantifying Healthy Aging in Older Veterans Using Computational Audio Analysis

BACKGROUND

Researchers are increasingly interested in better methods for assessing the pace of aging in older adults, including vocal analysis. The present study sought to determine whether paralinguistic vocal attributes improve estimates of the age and risk of mortality in older adults.

METHODS

To measure vocal age, we curated interviews provided by male U.S. World War II Veterans in the Library of Congress collection. We used diarization to identify speakers and measure vocal features and matched recording data to mortality information. Veterans (N = 2 447) were randomly split into testing (n = 1 467) and validation (n = 980) subsets to generate estimations of vocal age and years of life remaining. Results were replicated to examine out-of-sample utility using Korean War Veterans (N = 352).

RESULTS

World War II Veterans' average age was 86.08 at the time of recording and 91.28 at the time of death. Overall, 7.4% were prisoners of war, 43.3% were Army Veterans, and 29.3% were drafted. Vocal age estimates (mean absolute error = 3.255) were within 5 years of chronological age, 78.5% of the time. With chronological age held constant, older vocal age estimation was correlated with shorter life expectancy (aHR = 1.10; 95% confidence interval: 1.06-1.15; p < .001), even when adjusting for age at vocal assessment.

CONCLUSIONS

Computational analyses reduced estimation error by 71.94% (approximately 8 years) and produced vocal age estimates that were correlated with both age and predicted time until death when age was held constant. Paralinguistic analyses augment other assessments for individuals when oral patient histories are recorded.

Effectiveness of a scalable, remotely delivered stepped-care intervention to reduce symptoms of psychological distress among Polish migrant workers in the Netherlands: study protocol for the RESPOND randomised controlled trial

BACKGROUND

The COVID-19 pandemic has negatively affected the mental health of international migrant workers (IMWs). IMWs experience multiple barriers to accessing mental health care. Two scalable interventions developed by the World Health Organization (WHO) were adapted to address some of these barriers: Doing What Matters in times of stress (DWM), a guided self-help web application, and Problem Management Plus (PM +), a brief facilitator-led program to enhance coping skills. This study examines whether DWM and PM + remotely delivered as a stepped-care programme (DWM/PM +) is effective and cost-effective in reducing psychological distress, among Polish migrant workers with psychological distress living in the Netherlands.

METHODS

The stepped-care DWM/PM + intervention will be tested in a two-arm, parallel-group, randomized controlled trial (RCT) among adult Polish migrant workers with self-reported psychological distress (Kessler Psychological Distress Scale; K10 > 15.9). Participants (n = 212) will be randomized into either the intervention group that receives DWM/PM + with psychological first aid (PFA) and care-as-usual (enhanced care-as-usual or eCAU), or into the control group that receives PFA and eCAU-only (1:1 allocation ratio). Baseline, 1-week post-DWM (week 7), 1-week post-PM + (week 13), and follow-up (week 21) self-reported assessments will be conducted. The primary outcome is psychological distress, assessed with the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS). Secondary outcomes are self-reported symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), resilience, quality of life, and cost-effectiveness. In a process evaluation, stakeholders' views on barriers and facilitators to the implementation of DWM/PM + will be evaluated.

DISCUSSION

To our knowledge, this is one of the first RCTs that combines two scalable, psychosocial WHO interventions into a stepped-care programme for migrant populations. If proven to be effective, this may bridge the mental health treatment gap IMWs experience.

TRIAL REGISTRATION

Dutch trial register NL9630, 20/07/2021, https://www.onderzoekmetmensen.nl/en/trial/27052.

Machine Learning and the Digital Measurement of Psychological Health

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

The current state of artificial intelligence-augmented digitized neurocognitive screening test

The cognitive screening test is a brief cognitive examination that could be easily performed in a clinical setting. However, one of the main drawbacks of this test was that only a paper-based version was available, which restricts the test to be manually administered and graded by medical personnel at the health centers. The main solution to these problems was to develop a potential remote assessment for screening individuals with cognitive impairment. Currently, multiple studies have been adopting artificial intelligence (AI) technology into these tests, evolving the conventional paper-based neurocognitive test into a digitized AI-assisted neurocognitive test. These studies provided credible evidence of the potential of AI-augmented cognitive screening tests to be better and provided the framework for future studies to further improve the implementation of AI technology in the cognitive screening test. The objective of this review article is to discuss different types of AI used in digitized cognitive screening tests and their advantages and disadvantages.

Safety monitoring method of moving target in underground coal mine based on computer vision processing

Coal is one of the main energy sources in China. The country attaches great importance to the development of coal mining industry, and coal production is on the rise. At the same time, mine safety accidents are becoming more and more frequent, and the country is paying more and more attention to mine safety accidents. The underground environment of coal mine is complex, noisy and uneven, and there will be problems such as occlusion and high false detection rate during video monitoring. In order to ensure the safety of underground personnel, moving target detection and tracking based on video monitoring information is of great significance for coal mine safety production. The purpose of this paper is to study how to analyze and study the monitoring of moving targets in coal mines based on computer vision processing, and describe the image processing methods. This paper puts forward the problem of target monitoring, which is based on image processing, and then elaborates on the concept of image enhancement and related algorithms. From the average gradient, the algorithm in this paper is 56.60% higher than the histogram equalization algorithm, and 68.26% higher than the dark primary color prior dehazing algorithm. and designs and analyzes cases of image enhancement in coal mines. The experimental results show that the information entropy of the algorithm in this paper is 31.10% higher than that of the dark primary color prior dehazing algorithm, and 18.72% higher than that of the histogram equalization algorithm. It can be seen that the algorithm in this paper can achieve better enhancement effect.

Assessment of early neurocognitive functioning increases the accuracy of predicting chronic PTSD risk

Post-traumatic stress disorder (PTSD) is a protracted and debilitating consequence of traumatic events. Identifying early predictors of PTSD can inform the disorder's risk stratification and prevention. We used advanced computational models to evaluate the contribution of early neurocognitive performance measures to the accuracy of predicting chronic PTSD from demographics and early clinical features. We consecutively enrolled adult trauma survivors seen in a general hospital emergency department (ED) to a 14-month long prospective panel study. Extreme Gradient Boosting algorithm evaluated the incremental contribution to 14 months PTSD risk of demographic variables, 1-month clinical variables, and concurrent neurocognitive performance. The main outcome variable was PTSD diagnosis, 14 months after ED admission, obtained by trained clinicians using the Clinician-Administered PTSD Scale (CAPS). N = 138 trauma survivors (mean age = 34.25 ± 11.73, range = 18-64; n = 73 [53%] women) were evaluated 1 month after ED admission and followed for 14 months, at which time n = 33 (24%) met PTSD diagnosis. Demographics and clinical variables yielded a discriminatory accuracy of AUC = 0.68 in classifying PTSD diagnostic status. Adding neurocognitive functioning improved the discriminatory accuracy (AUC = 0.88); the largest contribution emanating from poorer cognitive flexibility, processing speed, motor coordination, controlled and sustained attention, emotional bias, and higher response inhibition, and recall memory. Impaired cognitive functioning 1-month after trauma exposure is a significant and independent risk factor for PTSD. Evaluating cognitive performance could improve early screening and prevention.

Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem

Psychiatric research traditionally relies on subjective observation, which is time-consuming and labor-intensive. The widespread use of digital devices, such as smartphones and wearables, enables the collection and use of vast amounts of user-generated data as "digital biomarkers." These tools may also support increased participation of psychiatric patients in research and, as a result, the production of research results that are meaningful to them. However, sharing mental health data and research results may expose patients to discrimination and stigma risks, thus discouraging participation. To earn and maintain participants' trust, the first essential requirement is to implement an appropriate data governance system with a clear and transparent allocation of data protection duties and responsibilities among the actors involved in the process. These include sponsors, investigators, operators of digital tools, as well as healthcare service providers and biobanks/databanks. While previous works have proposed practical solutions to this end, there is a lack of consideration of positive data protection law issues in the extant literature. To start filling this gap, this paper discusses the GDPR legal qualifications of controller, processor, and joint controllers in the complex ecosystem unfolded by the integration of digital biomarkers in psychiatric research, considering their implications and proposing some general practical recommendations.