Robots in Healthcare: a Scoping Review.

Purpose of Review: Robots are increasingly being adopted in healthcare to carry out various tasks that enhance patient care. This scoping review aims to establish the types of robots being used in healthcare and identify where they are deployed. Recent Findings: Technological advancements have enabled robots to conduct increasingly varied and complex roles in healthcare. For instance, precision tasks such as improving dexterity following stroke or assisting with percutaneous coronary intervention. Summary: This review found that robots have played 10 main roles across a variety of clinical environments. The two predominant roles were surgical and rehabilitation and mobility. Although robots were mainly studied in the surgical theatre and rehabilitation unit, other settings ranged from the hospital ward to inpatient pharmacy. Healthcare needs are constantly evolving, as demonstrated by COVID-19, and robots may assist in adapting to these changes. The future will involve increased telepresence and infrastructure systems will have to improve to allow for this. Supplementary Information: The online version contains supplementary material available at 10.1007/s43154-022-00095-4.

DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders.

There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or "Discover Confirm Independent Component Analysis", a software package that implements the methodology in support of our hypothesis. It relies on a "discover-confirm" approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at https://bitbucket.org/masauburn/disconica.

Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data.

Significance: Autism is a developmental disorder that is currently diagnosed using behavioral tests which can be subjective. Consequently, objective non-invasive imaging biomarkers of Autism are being actively researched. The common theme emerging from previous functional magnetic resonance imaging (fMRI) studies is that Autism is characterized by alterations of fMRI-derived functional connections in certain brain networks which may provide a biomarker for objective diagnosis. However, identification of individuals with Autism solely based on these measures has not been reliable, especially when larger sample sizes are taken into consideration. Objective: We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups. Methods: In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm). Results: We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group. Conclusion: The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.