Deep learning of Parkinson's movement from video, without human-defined measures.

BACKGROUND: The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures. OBJECTIVES: (1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model. METHODS: 152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips. RESULTS: For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group. CONCLUSIONS: A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features.

The management of heart failure cardiogenic shock: an international RAND appropriateness panel.

BACKGROUND: Observational data suggest that the subset of patients with heart failure related CS (HF-CS) now predominate critical care admissions for CS. There are no dedicated HF-CS randomised control trials completed to date which reliably inform clinical practice or clinical guidelines. We sought to identify aspects of HF-CS care where both consensus and uncertainty may exist to guide clinical practice and future clinical trial design, with a specific focus on HF-CS due to acute decompensated chronic HF. METHODS: A 16-person multi-disciplinary panel comprising of international experts was assembled. A modified RAND/University of California, Los Angeles, appropriateness methodology was used. A survey comprising of 34 statements was completed. Participants anonymously rated the appropriateness of each statement on a scale of 1 to 9 (1-3 as inappropriate, 4-6 as uncertain and as 7-9 appropriate). RESULTS: Of the 34 statements, 20 were rated as appropriate and 14 were rated as inappropriate. Uncertainty existed across all three domains: the initial assessment and management of HF-CS; escalation to temporary Mechanical Circulatory Support (tMCS); and weaning from tMCS in HF-CS. Significant disagreement between experts (deemed present when the disagreement index exceeded 1) was only identified when deliberating the utility of thoracic ultrasound in the immediate management of HF-CS. CONCLUSION: This study has highlighted several areas of practice where large-scale prospective registries and clinical trials in the HF-CS population are urgently needed to reliably inform clinical practice and the synthesis of future societal HF-CS guidelines.

Contactless hand tremor amplitude measurement using smartphones: development and pilot evaluation.

Background - Physiological tremor is defined as an involuntary and rhythmic shaking. Tremor of the hand is a key symptom of multiple neurological diseases, and its frequency and amplitude differs according to both disease type and disease progression. In routine clinical practice, tremor frequency and amplitude are assessed by expert rating using a 0 to 4 integer scale. Such ratings are subjective and have poor inter-rater reliability. There is thus a clinical need for a practical and accurate method for objectively assessing hand tremor.Objective - to develop a proof-of-principle method to measure hand tremor amplitude from smartphone videos.Methods - We created a computer vision pipeline that automatically extracts salient points on the hand and produces a 1-D time series of movement due to tremor, in pixels. Using the smartphones' depth measurement, we convert this measure into real distance units. We assessed the accuracy of the method using 60 videos of simulated tremor of different amplitudes from two healthy adults. Videos were taken at distances of 50, 75 and 100 cm between hand and camera. The participants had skin tone II and VI on the Fitzpatrick scale. We compared our method to a gold-standard measurement from a slide rule. Bland-Altman methods agreement analysis indicated a bias of 0.04 cm and 95% limits of agreement from -1.27 to 1.20 cm. Furthermore, we qualitatively observed that the method was robust to limited occlusion.Clinical relevance - We have demonstrated how tremor amplitude can be measured from smartphone videos. In conjunction with tremor frequency, this approach could be used to help diagnose and monitor neurological diseases.

Spatial communication systems across languages reflect universal action constraints.

The extent to which languages share properties reflecting the non-linguistic constraints of the speakers who speak them is key to the debate regarding the relationship between language and cognition. A critical case is spatial communication, where it has been argued that semantic universals should exist, if anywhere. Here, using an experimental paradigm able to separate variation within a language from variation between languages, we tested the use of spatial demonstratives-the most fundamental and frequent spatial terms across languages. In n = 874 speakers across 29 languages, we show that speakers of all tested languages use spatial demonstratives as a function of being able to reach or act on an object being referred to. In some languages, the position of the addressee is also relevant in selecting between demonstrative forms. Commonalities and differences across languages in spatial communication can be understood in terms of universal constraints on action shaping spatial language and cognition.

Does emotion regulation network mediate the effect of social network on psychological distress among older adults?

Socio-emotional interactions are integral for regulating emotions and buffering psychological distress. Social neuroscience perspectives on aging suggest that empathetic interpersonal interactions are supported by the activation of brain regions involved in regulating negative affect. The current study tested whether resting state functional connectivity of a network of brain regions activated during cognitive emotion regulation, i.e., emotion regulation network (ERN), statistically mediates the frequency of social contact with friends or family on psychological distress. Here, a 10-min resting-state functional MRI scan was collected along with self-reported anxiety/depressive, somatic, and thought problems and social networking from 90 community-dwelling older adults (aged 65-85 years). The frequency of social interactions with family, but not friends and neighbors, was associated with lower psychological distress. The magnitude of this effect was reduced by 33.34% to non-significant upon adding resting state ERN connectivity as a mediator. Follow-up whole-brain graph network analyses revealed that efficiency and centrality of the left inferior frontal gyrus and the right middle temporal gyrus relate to greater family interactions and lower distress. These hubs may help to buffer psychological problems in older adults through interactions involving empathetic and cognitive emotion regulation with close family.

Early innovations in maritime telemedical services: the KDKF Radio Medico Station.

"MAN PUT HIS TONGUE AGAINST REFRIGERATOR PIPE AND GOT IT FROZEN; HAVE THAWED IT OUT AND IT IS NOW BLISTERED AND SWOLLEN BUT NOT PAINFUL. ARRIVING HONOLULU FRIDAY; HOW CAN I HELP HIM MEANWHILE?" Thus read a message relayed via radiogram across the ocean to the physician stationed at the Seamen's Church Institute's (SCI) KDKF radio station, established by the Institute in 1920 on top of its thirteen-story seafarer services center at the southern tip of Manhattan. Though radio was in its infancy, radio telegraphy had already proven its revolutionary power, featuring prominently in far more serious maritime emergencies such as the sinking of Titanic. SCI's KDKF radio station aimed to address a less dramatic but no less important problem in blue water navigation: access to medical care.

Parkinsonian Hand or Clinician's Eye? Finger Tap Bradykinesia Interrater Reliability for 21 Movement Disorder Experts.

BACKGROUND: Bradykinesia is considered the fundamental motor feature of Parkinson's disease (PD). It is central to diagnosis, monitoring, and research outcomes. However, as a clinical sign determined purely by visual judgement, the reliability of humans to detect and measure bradykinesia remains unclear. OBJECTIVE: To establish interrater reliability for expert neurologists assessing bradykinesia during the finger tapping test, without cues from additional examination or history. METHODS: 21 movement disorder neurologists rated finger tapping bradykinesia, by Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Modified Bradykinesia Rating Scale (MBRS), in 133 videos of hands: 73 from 39 people with idiopathic PD, 60 from 30 healthy controls. Each neurologist rated 30 randomly-selected videos. 19 neurologists were also asked to judge whether the hand was PD or control. We calculated intraclass correlation coefficients (ICC) for absolute agreement and consistency of MDS-UPDRS ratings, using standard linear and cumulative linked mixed models. RESULTS: There was only moderate agreement for finger tapping MDS-UPDRS between neurologists, ICC 0.53 (standard linear model) and 0.65 (cumulative linked mixed model). Among control videos, 53% were rated > 0 by MDS-UPDRS, and 24% were rated as bradykinesia by MBRS subscore combination. Neurologists correctly identified PD/control status in 70% of videos, without strictly following bradykinesia presence/absence. CONCLUSION: Even experts show considerable disagreement about the level of bradykinesia on finger tapping, and frequently see bradykinesia in the hands of those without neurological disease. Bradykinesia is to some extent a phenomenon in the eye of the clinician rather than simply the hand of the person with PD.

Clinicians' implicit and explicit attitudes about the legitimacy of functional neurological disorders correlate with referral decisions.

OBJECTIVES: Uncertainty regarding the legitimacy of functional neurological disorder (FND) remains among some health care professionals. Despite treatment guidelines and consensus recommendations, variability in clinical practice referral decisions persists. Evidence from other conditions suggests such clinical decision making is impacted by practitioners' implicit and explicit attitudes. We aimed to identify whether health care professionals hold implicit and/or explicit attitudes about the legitimacy of FND and whether these attitudes are associated with referral decision making. DESIGN/METHODS: We included 66 health care professionals who work with people with neurological conditions: n = 37 medical doctors, mainly neurologists (n = 18) and psychiatrists (n = 10), and n = 29 doctoral level practitioner psychologists. Participants completed an Implicit Association Test (IAT), Implicit Relational Assessment Procedure (IRAP), a referral decision-making vignette task and self-report measures of explicit attitudes on FND-legitimacy, therapeutic optimism and clinician confidence. Multiple Sclerosis (MS) was used as a comparator condition. RESULTS: Participants self-reported strong explicit FND-legitimate and MS-legitimate attitudes but demonstrated an implicit FND-illegitimate/MS-legitimate bias. Deeper examination provided by the IRAP data indicated pro-FND-legitimate attitudes, but no bias for or against FND-illegitimate-contrasting the pro-MS-legitimate, anti-MS-illegitimate attitudes for the comparator condition. Attitudes about FND-illegitimacy were negatively associated with likelihood of referral to physical interventions such as physiotherapy. Medical doctors had lower treatment optimism and stronger explicit attitudes that FND is illegitimate than psychologists. CONCLUSIONS: At an implicit level, clinicians are uncertain about the illegitimacy of FND, and such attitudes are associated with lower likelihood of referral to physiotherapy in particular. Improved education on FND among health care professionals is indicated.

Guiding Labelling Effort for Efficient Learning With Georeferenced Images.

We describe a novel semi-supervised learning method that reduces the labelling effort needed to train convolutional neural networks (CNNs) when processing georeferenced imagery. This allows deep learning CNNs to be trained on a per-dataset basis, which is useful in domains where there is limited learning transferability across datasets. The method identifies representative subsets of images from an unlabelled dataset based on the latent representation of a location guided autoencoder. We assess the method's sensitivities to design options using four different ground-truthed datasets of georeferenced environmental monitoring images, where these include various scenes in aerial and seafloor imagery. Efficiency gains are achieved for all the aerial and seafloor image datasets analysed in our experiments, demonstrating the benefit of the method across application domains. Compared to CNNs of the same architecture trained using conventional transfer and active learning, the method achieves equivalent accuracy with an order of magnitude fewer annotations, and 85 % of the accuracy of CNNs trained conventionally with approximately 10,000 human annotations using just 40 prioritised annotations. The biggest gains in efficiency are seen in datasets with unbalanced class distributions and rare classes that have a relatively small number of observations.

Moving towards intelligent telemedicine: Computer vision measurement of human movement.

BACKGROUND: Telemedicine video consultations are rapidly increasing globally, accelerated by the COVID-19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual judgement because video cameras are so ubiquitous in personal devices and new techniques, such as DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations. OBJECTIVES: To determine the validity and reliability of DLC applied to laptop videos to measure finger tapping, a validated test of human movement. METHOD: Sixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak, a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101, ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking. RESULTS: Over 96% (529/552) of DLC measures were within +/-0.5 Hz of the Optotrak measures. At tapping frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with the laptop camera's low FPS. Computer vision methods hold potential for moving us towards intelligent telemedicine by providing human movement analysis during consultations. However, further developments are required to accurately measure the fastest movements.

To the emergency room and back again: Circular healthcare pathways for acute functional neurological disorders.

BACKGROUND AND OBJECTIVES: Studies of Functional Neurological Disorders (FND) are usually outpatient-based. To inform service development, we aimed to describe patient pathways through healthcare events, and factors affecting risk of emergency department (ED) reattendance, for people presenting acutely with FND. METHODS: Acute neurology/stroke teams at a UK city hospital were contacted regularly over 8 months to log FND referrals. Electronic documentation was then reviewed for hospital healthcare events over the preceding 8 years. Patient pathways through healthcare events over time were mapped, and mixed effects logistic regression was performed for risk of ED reattendance within 1 year. RESULTS: In 8 months, 212 patients presented acutely with an initial referral suggesting FND. 20% had subsequent alternative diagnoses, but 162 patients were classified from documentation review as possible (17%), probable (28%) or definite (55%) FND. In the preceding 8 years, these 162 patients had 563 ED attendances and 1693 inpatient nights with functional symptoms, but only 26% were referred for psychological therapy, only 66% had a documented diagnosis, and care pathways looped around ED. Three better practice pathway steps were each associated with lower risk of subsequent ED reattendance: documented FND diagnosis (OR = 0.32, p = 0.004), referral to clinical psychology (OR = 0.35, p = 0.04) and outpatient neurology follow-up (OR = 0.25, p < 0.001). CONCLUSION: People that present acutely to a UK city hospital with FND tend to follow looping pathways through hospital healthcare events, centred around ED, with low rates of documented diagnosis and referral for psychological therapy. When better practice occurs, it is associated with lower risk of ED reattendance.

Accuracy of Smartphone Video for Contactless Measurement of Hand Tremor Frequency.

BACKGROUND: Computer vision can measure movement from video without the time and access limitations of hospital accelerometry/electromyography or the requirement to hold or strap a smartphone accelerometer. OBJECTIVE: To compare computer vision measurement of hand tremor frequency from smartphone video with a gold standard measure accelerometer. METHODS: A total of 37 smartphone videos of hands, at rest and in posture, were recorded from 15 participants with tremor diagnoses (9 Parkinson's disease, 5 essential tremor, 1 functional tremor). Video pixel movement was measured using the computing technique of optical flow, with contemporaneous accelerometer recording. Fast Fourier transform and Bland-Altman analysis were applied. Tremor amplitude was scored by 2 clinicians. RESULTS: Bland-Altman analysis of dominant tremor frequency from smartphone video compared with accelerometer showed excellent agreement: 95% limits of agreement -0.38 Hz to +0.35 Hz. In 36 of 37 videos (97%), there was <0.5 Hz difference between computer vision and accelerometer measurement. There was no significant correlation between the level of agreement and tremor amplitude. CONCLUSION: The study suggests a potential new, contactless point-and-press measure of tremor frequency within standard clinical settings, research studies, or telemedicine.

Supervised classification of bradykinesia in Parkinson's disease from smartphone videos.

BACKGROUND: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. AIM: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. METHODS: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0-1) or mild/moderate/severe bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis. RESULTS: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. CONCLUSION: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

Seeing the unseen: Could Eulerian video magnification aid clinician detection of subclinical Parkinson's tremor?

INTRODUCTION: Eulerian magnification amplifies very small movements in video, revealing otherwise invisible motion. This raises the possibility that it could enable clinician visualisation of subclinical tremor using a standard camera. We tested whether Eulerian magnification of apparently atremulous hands reveals a Parkinsonian tremor more frequently in Parkinson's than in controls. METHOD: We applied Eulerian magnification to smartphone video of 48 hands that appeared atremulous during recording (22 hands from 11 control participants, 26 hands from 17 idiopathic Parkinson's participants). Videos were rated for Parkinsonian tremor appearance (yes/no) before and after Eulerian magnification by three movement disorder specialist neurologists. RESULTS: The proportion of hands correctly classified as Parkinsonian or not by clinicians was significantly higher after Eulerian magnification (OR = 2.67; CI = [1.39, 5.17]; p < 0.003). Parkinsonian-appearance tremors were seen after magnification in a number of control hands, but the proportion was greater in the Parkinson's hands. CONCLUSION: Eulerian magnification slightly improves clinician ability to identify apparently atremulous hands as Parkinsonian. This suggests that some of the apparent tremor revealed may be subclinical Parkinson's (pathological) tremor, and Eulerian magnification may represent a first step towards contactless visualisation of such tremor. However, the technique also reveals apparent tremor in control hands. Therefore, our method needs additional elaboration and would not be of direct clinical use in its current iteration.

Time series clustering to examine presence of decrement in Parkinson's finger-tapping bradykinesia.

Parkinson's disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no bradykinesia and participants with severe bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance- Our work visually presents the archetypal types of bradykinesia amplitude decrement, as seen in the Parkinson's finger-tapping test. We found two main patterns, one corresponding to no bradykinesia, and the other showing linear decrement over time.

Recovering Depth from Still Images for Underwater Dehazing Using Deep Learning.

Estimating depth from a single image is a challenging problem, but it is also interesting due to the large amount of applications, such as underwater image dehazing. In this paper, a new perspective is provided; by taking advantage of the underwater haze that may provide a strong cue to the depth of the scene, a neural network can be used to estimate it. Using this approach the depthmap can be used in a dehazing method to enhance the image and recover original colors, offering a better input to image recognition algorithms and, thus, improving the robot performance during vision-based tasks such as object detection and characterization of the seafloor. Experiments are conducted on different datasets that cover a wide variety of textures and conditions, while using a dense stereo depthmap as ground truth for training, validation and testing. The results show that the neural network outperforms other alternatives, such as the dark channel prior methods and it is able to accurately estimate depth from a single image after a training stage with depth information.