Virtual Reality for Pulmonary Rehabilitation: Comprehensive Review.

BACKGROUND: Pulmonary rehabilitation is a vital component of comprehensive care for patients with respiratory conditions, such as lung cancer, chronic obstructive pulmonary disease, and asthma, and those recovering from respiratory diseases like COVID-19. It aims to enhance patients' functional ability and quality of life, and reduce symptoms, such as stress, anxiety, and chronic pain. Virtual reality is a novel technology that offers new opportunities for customized implementation and self-control of pulmonary rehabilitation through patient engagement. OBJECTIVE: This review focused on all types of virtual reality technologies (nonimmersive, semi-immersive, and fully immersive) that witnessed significant development and were released in the field of pulmonary rehabilitation, including breathing exercises, biofeedback systems, virtual environments for exercise, and educational models. METHODS: The review screened 7 electronic libraries from 2010 to 2023. The libraries were ACM Digital Library, Google Scholar, IEEE Xplore, MEDLINE, PubMed, Sage, and ScienceDirect. Thematic analysis was used as an additional methodology to classify our findings based on themes. The themes were virtual reality training, interaction, types of virtual environments, effectiveness, feasibility, design strategies, limitations, and future directions. RESULTS: A total of 2319 articles were identified, and after a detailed screening process, 32 studies were reviewed. Based on the findings of all the studies that were reviewed (29 with a positive label and 3 with a neutral label), virtual reality can be an effective solution for pulmonary rehabilitation in patients with lung cancer, chronic obstructive pulmonary disease, and asthma, and in individuals and children who are dealing with mental health-related disorders, such as anxiety. The outcomes indicated that virtual reality is a reliable and feasible solution for pulmonary rehabilitation. Interventions can provide immersive experiences to patients and offer tailored and engaging rehabilitation that promotes improved functional outcomes of pulmonary rehabilitation, breathing body awareness, and relaxation breathing techniques. CONCLUSIONS: The identified studies on virtual reality in pulmonary rehabilitation showed that virtual reality holds great promise for improving the outcomes and experiences of patients. The immersive and interactive nature of virtual reality interventions offers a new dimension to traditional rehabilitation approaches, providing personalized exercises and addressing psychological well-being. However, additional research is needed to establish standardized protocols, identify the most effective strategies, and evaluate long-term benefits. As virtual reality technology continues to advance, it has the potential to revolutionize pulmonary rehabilitation and significantly improve the lives of patients with chronic lung diseases.

ERN GENTURIS clinical practice guidelines for the diagnosis, treatment, management and surveillance of people with schwannomatosis.

A Guideline Group (GG) was convened from multiple specialties and patients to develop the first comprehensive schwannomatosis guideline. The GG undertook thorough literature review and wrote recommendations for treatment and surveillance. A modified Delphi process was used to gain approval for recommendations which were further altered for maximal consensus. Schwannomatosis is a tumour predisposition syndrome leading to development of multiple benign nerve-sheath non-intra-cutaneous schwannomas that infrequently affect the vestibulocochlear nerves. Two definitive genes (SMARCB1/LZTR1) have been identified on chromosome 22q centromeric to NF2 that cause schwannoma development by a 3-event, 4-hit mechanism leading to complete inactivation of each gene plus NF2. These genes together account for 70-85% of familial schwannomatosis and 30-40% of isolated cases in which there is considerable overlap with mosaic NF2. Craniospinal MRI is generally recommended from symptomatic diagnosis or from age 12-14 if molecularly confirmed in asymptomatic individuals whose relative has schwannomas. Whole-body MRI may also be deployed and can alternate with craniospinal MRI. Ultrasound scans are useful in limbs where typical pain is not associated with palpable lumps. Malignant-Peripheral-Nerve-Sheath-Tumour-MPNST should be suspected in anyone with rapidly growing tumours and/or functional loss especially with SMARCB1-related schwannomatosis. Pain (often intractable to medication) is the most frequent symptom. Surgical removal, the most effective treatment, must be balanced against potential loss of function of adjacent nerves. Assessment of patients' psychosocial needs should be assessed annually as well as review of pain/pain medication. Genetic diagnosis and counselling should be guided ideally by both blood and tumour molecular testing.

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review.

INTRODUCTION: Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain. METHODS: A systematic review of the current literature was conducted using the PubMed database library. RESULTS: Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed. CONCLUSION: ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.

Robust Estimation of Pulse Rate from a Wrist-type PPG During Intensive Exercise.

Estimation of pulse rate from a wrist-type PPG during motion is a notoriously difficult problem because of the presence of motion artifact (MA) which corrupts the signal in both the time and frequency domains. In this paper, we propose a new method for deriving pulse rate under intense exercise conditions which employs Ensemble Empirical Mode Decomposition and power spectral analysis to extract the pulsatile component of the signal. The method was validated on an openly available database containing PPG and ground-truth ECG-derived pulse rate measurements from 12 subjects during a running experiment. Our proposed technique showed a high estimation accuracy with a mean absolute error of 2.14 bpm over the entire database and a correlation coefficient between the estimates and the ground truth of 0.98. Our approach matched the performance of the state-of-the-art TROIKA framework without utilizing simultaneously recorded accelerometry data to remove the MA component. With over 97.5% of estimates within a 10% margin from the ground truth, our technique shows a lot of potential for inclusion in next generation wrist-worn wearable monitors in both sports and clinical settings.

Estimation of pulse rate from ambulatory PPG using ensemble empirical mode decomposition and adaptive thresholding.

A new method for deriving pulse rate from PPG obtained from ambulatory patients is presented. The method employs Ensemble Empirical Mode Decomposition to identify the pulsatile component from noise-corrupted PPG, and then uses a set of physiologically-relevant rules followed by adaptive thresholding, in order to estimate the pulse rate in the presence of noise. The method was optimized and validated using 63 hours of data obtained from ambulatory hospital patients. The F1 score obtained with respect to expertly annotated data was 0.857 and the mean absolute errors of estimated pulse rates with respect to heart rates obtained from ECG collected in parallel were 1.72 bpm for "good" quality PPG and 4.49 bpm for "bad" quality PPG. Both errors are within the clinically acceptable margin-of-error for pulse rate/heart rate measurements, showing the promise of the proposed approach for inclusion in next generation wearable sensors.