OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD.

Mental health is vital to human well-being, and prevention strategies to address mental illness have a significant impact on the burden of disease and quality of life. With the recent developments in body-worn sensors, it is now possible to continuously collect data that can be used to gain insights into mental health states. This has the potential to optimize psychiatric assessment, thereby improving patient experiences and quality of life.

Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.

Objective: Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram (ECG) analysis and provide recommendations for selection of explanations methods.

Materials And Methods: A residual deep neural network was trained on ECGs to predict intervals and amplitudes. Nine commonly used explanation methods (Saliency, Deconvolution, Guided backpropagation, Gradient SHAP, SmoothGrad, Input × gradient, DeepLIFT, Integrated gradients, GradCAM) were qualitatively evaluated by medical experts and objectively evaluated using a perturbation-based method.

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges.

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care.

Using an AI-based avatar for interviewer training at Children's Advocacy Centers: Proof of Concept.

This proof-of- concept study focused on interviewers' behaviors and perceptions when interacting with a dynamic AI child avatar alleging abuse. Professionals ( = 68) took part in a virtual reality (VR) study in which they questioned an avatar presented as a child victim of sexual or physical abuse. Of interest was how interviewers questioned the avatar, how productive the child avatar was in response, and how interviewers perceived the VR interaction.

The impact of hypovolemia and PEEP on recirculation in venovenous ECMO: an experimental porcine model.

Background: Recirculation is a common problem in venovenous extracorporeal membrane oxygenation (VV ECMO) and may limit the effect of ECMO treatment due to less efficient blood oxygenation or unfavorable ECMO and ventilator settings. The impact of hypovolemia and positive end expiratory pressure (PEEP) on recirculation is unclear and poorly described in guidelines, despite clinical importance. The aim of this study was to investigate how hypovolemia, autotransfusion and PEEP affect recirculation in comparison to ECMO cannula distance and circuit flow.

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations.

Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations.

A large-scale multivariate soccer athlete health, performance, and position monitoring dataset.

Data analysis for athletic performance optimization and injury prevention is of tremendous interest to sports teams and the scientific community. However, sports data are often sparse and hard to obtain due to legal restrictions, unwillingness to share, and lack of personnel resources to be assigned to the tedious process of data curation. These constraints make it difficult to develop automated systems for analysis, which require large datasets for learning.

Flavonoids regulate LDLR through different mechanisms tied to their specific structures.

Flavonoids, polyphenolic compounds found in plant-based diets, are associated with reduced risk of cardiovascular disease and longevity. These components are reported to reduce plasma levels of low-density lipoprotein (LDL) through an upregulation of the LDL receptor (LDLR), but the mechanism is still largely unknown. In this study, we have systematically screened the effect of 12 flavonoids from six different flavonoid subclasses on the effect on LDLR.

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology.

Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction.

Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear film lipid layer, studying the expression of tear proteins might increase the understanding of the etiology of the condition. Machine learning is able to detect patterns in complex data.

Enhancing investigative interview training using a child avatar system: a comparative study of interactive environments.

The impact of investigative interviews by police and Child Protective Services (CPS) on abused children can be profound, making effective training vital. Quality in these interviews often falls short and current training programs are insufficient in enabling adherence to best practice. We present a system for simulating an interactive environment with alleged abuse victims using a child avatar.

CELLULAR, A Cell Autophagy Imaging Dataset.

Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target.

Health-related quality of life after surviving intensive care for COVID-19: a prospective multicenter cohort study.

In survivors of severe coronavirus disease 2019 (COVID-19) incomplete mental and physical recovery may considerably impact daily activities and health-related quality of life (HRQoL). HRQoL can be evaluated with the RAND-36 questionnaire, a multidimensional instrument that assesses physical and mental aspects of health in eight dimensions. The objective was to investigate HRQoL in intensive care patients previously treated for COVID-19 at three Nordic university hospitals, in a prospective multi-center cohort study.

AAPM Medical physics practice guideline 15.A: Peer review in clinical physics.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States.

Extracorporeal driveline vibrations to detect left ventricular assist device thrombosis - A porcine model study.

Background: Pump thrombosis (PT) and related adverse complications contributed to the HeartWare Ventricular Assist Device (HVAD) market withdrawal. Many patients still receive lifelong support, with deficient PT surveillance based on pump power trends. Analysis of pump vibrations is better for detecting PT.

Enhancing questioning skills through child avatar chatbot training with feedback.

Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to trainees.

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.

Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand.

VISEM-Tracking, a human spermatozoa tracking dataset.

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics.

Resource Allocations for Common Radiation Oncology Procedures.

Purpose: Radiation Oncology is a complex, resource-intensive discipline. The complexity of the radiation oncology treatment process has increased significantly in recent years with the introduction of more advanced imaging, planning, and treatment delivery technology and enhanced use of multidisciplinary care paths. We conducted a multi-institutional study to estimate the average time by functional unit for a wide range of modern radiation oncology treatment regimens.

Detection of inflow obstruction in left ventricular assist devices by accelerometer: A porcine model study.

Background: Left ventricular assist devices (LVAD) provide circulatory blood pump support for severe heart failure patients. Pump inflow obstructions may lead to stroke and pump malfunction. We aimed to verify in vivo that gradual inflow obstructions, representing prepump thrombosis, are detectable by a pump-attached accelerometer, where the routine use of pump power (P) is deficient.

Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images.

Background: Misalignment to the incorrect vertebral body remains a rare but serious patient safety risk in image-guided radiotherapy (IGRT).

Purpose: Our group has proposed that an automated image-review algorithm be inserted into the IGRT process as an interlock to detect off-by-one vertebral body errors. This study presents the development and multi-institutional validation of a convolutional neural network (CNN)-based approach for such an algorithm using patient image data from a planar stereoscopic x-ray IGRT system.

GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos.

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes.

Video Analytics in Elite Soccer: A Distributed Computing Perspective.

Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA's 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables.