Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.

Virtual reality in the management of stress and anxiety disorders: A retrospective analysis of 61 people treated in the metaverse.

Mental health is the second largest group of health disorders associated with prolonged disability. Treating conditions such as stress and anxiety are a global health challenge due to inadequate funding and resources. Therefore, providing virtual treatment in the metaverse may provide a novel method of treatment for these conditions.

Virtual reality in the management of patients with low back and neck pain: a retrospective analysis of 82 people treated solely in the metaverse.

Background: Clinically, neck pain disorders (NPD) and non-specific low back pain (NS-LBP) are respectively the fourth and first most common conditions associated with the greatest number of years lived with disability. Remote delivery of care may benefit healthcare sustainability, reduce environmental pollution, and free up space for those requiring care non-virtual care.

Methods: A retrospective analysis was performed on 82 participants with NS-LBP and/or NPD who received exercise therapy delivered solely in the metaverse using virtually reality.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g.