Mobile metadata assisted community database of chronic wound images

Development and validation of HBV surveillance models using big data and machine learning

BACKGROUND

The construction of a robust healthcare information system is fundamental to enhancing countries' capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China's rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization's (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China.

METHODS

Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital's Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework.

RESULTS

The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients' physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05).

CONCLUSIONS

Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264).

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34's deep representation learning and UNet's efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

Stable closure of acute and chronic wounds and pressure ulcers and control of draining fistulas from osteomyelitis in persons with spinal cord injuries: non-interventional study of MPPT passive immunotherapy delivered via telemedicine in community care

Background

Micropore particle technology (MPPT) is a topical wound treatment. It is a passive immunotherapy, acting via the skin and wound microbiome without the use of antimicrobial action. In a general patient population, it removed wound infections 60% and initiated tissue regeneration 50% quicker than antibiotics and antiseptics. As MPPT supports the immune system, the aim was to confirm that MPPT is also effective in immunocompromised individuals. People with spinal cord injury (SCI) are immunodeficient due to their injury and not an underlying disease and recruit 50% fewer immune cells to an injury. The study, therefore, determined the efficacy, safety, health economics, and sustainability of MPPT in acute and chronic wounds and pressure ulcers in this patient population.

Methods

Pressure ulcers in SCI persons are an orphan indication, patient variability is high, and ICH E10 excludes comparators due to ethical concerns. The study design was, therefore, a single-arm, non-interventional, observational, post-market surveillance study of MPPT for treating wounds and pressure ulcers and removing soft tissue infection in connection with draining fistulas in SCI persons. The study was based on telemedicine in community care.

Results

The study included 44 wounds. All acute and chronic grade 1-4 wounds and pressure ulcers reached stable closure. In wounds acting as fistulas draining from an underlying, primary focus of infection, e.g., osteomyelitis, MPPT removed the soft tissue infection in approx. 2.5 months and supported regeneration, considerably reducing fistula sizes. Compared to standard care, per-wound cost savings were 51 to 94% depending on wound grade and age, and substantial nursing resources were freed up. The telemedicine approach was well received by participants and supported independence and self-care. The use of antimicrobials, plastics, and synthetic polymers was essentially eliminated. MPPT did not require bed rest.

Conclusion

The study confirmed that MPPT is safe and effective in treating acute and chronic wounds in immunocompetent and immunocompromised individuals, including wounds with antimicrobial-resistant infections. MPPT also removes soft tissue infections caused by an underlying primary focus of infection, such as osteomyelitis. Non-healing wounds currently represent an unmet clinical need. The findings suggest that a therapy acting via the microbiome without antimicrobial actions is effective.

A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia

The purpose of this study is to investigate the efficacy of bilateral upper-limb training (BULT) in helping people with upper-limb impairments due to stroke or brain illness regain their previous level of function. Patients recuperating from a stroke or cerebral disease were given the option of undergoing BULT or conventional training to enhance their upper-limb function. Participants were randomly allocated to one of the several different fitness programs. Results from the action research arm test, Box and block test, Wolf motor function test, Fugal-Meyer evaluation, and any other tests administered were taken into account. Some researchers have found that exercising with BULT for just 30 min per day for 6 weeks yields significant results. There were a total of 1,411 individuals from 10 randomized controlled trials included in this meta-analysis. Meta-analysis findings revealed that biofeedback treatment outperformed conventional rehabilitation therapy in reducing lower leg muscular strain, complete spasm scale score, electromyography score, and inactive ankle joint range of motion. An analysis of the literature found that BULT improved limb use in people who had suffered a stroke and hemiplegia but it did not provide any additional benefit over unilateral training.

Flavonoids as Potential Wound-Healing Molecules: Emphasis on Pathways Perspective

Wounds are considered to be a serious problem that affects the healthcare sector in many countries, primarily due to diabetes and obesity. Wounds become worse because of unhealthy lifestyles and habits. Wound healing is a complicated physiological process that is essential for restoring the epithelial barrier after an injury. Numerous studies have reported that flavonoids possess wound-healing properties due to their well-acclaimed anti-inflammatory, angiogenesis, re-epithelialization, and antioxidant effects. They have been shown to be able to act on the wound-healing process via expression of biomarkers respective to the pathways that mainly include Wnt/β-catenin, Hippo, Transforming Growth Factor-beta (TGF-β), Hedgehog, c-Jun N-Terminal Kinase (JNK), NF-E2-related factor 2/antioxidant responsive element (Nrf2/ARE), Nuclear Factor Kappa B (NF-κB), MAPK/ERK, Ras/Raf/MEK/ERK, phosphatidylinositol 3-kinase (PI3K)/Akt, Nitric oxide (NO) pathways, etc. Hence, we have compiled existing evidence on the manipulation of flavonoids towards achieving skin wound healing, together with current limitations and future perspectives in support of these polyphenolic compounds as safe wound-healing agents, in this review.

Augmented Reality and Virtual Reality Transforming Spinal Imaging Landscape: A Feasibility Study

This article discusses a systematic review of the state-of-the-art on augmented reality (AR) and virtual reality (VR) in spinal navigation, where early clinical validations have shown promising outlook on accuracy and scalability parameters. The objective of this research is to evaluate clinical relevance for AR-VR enabled spinal surgical technologies and develop an economic feasibility model for stakeholders, such as patients, hospitals, and research organizations with technology adoption. From the influencing parameters, we identified the research gaps that can be explored going forward and a list of high priority research challenges that could provide an attractive research and development investment case for industry players.

Performance Analysis of Compression Techniques for Chronic Wound Image Transmission Under Smartphone-Enabled Tele-Wound Network

The healing status of chronic wounds is important for monitoring the condition of the wounds. This article designs and discusses the implementation of smartphone-enabled compression technique under a tele-wound network (TWN) system. Nowadays, there is a huge demand for memory and bandwidth savings for clinical data processing. Wound images are captured using a smartphone through a metadata application page. Then, they are compressed and sent to the telemedical hub with a set partitioning in hierarchical tree (SPIHT) compression algorithm. The transmitted image can then be reduced, followed by an improvement in the segmentation accuracy and sensitivity. Better wound healing treatment depends on segmentation and classification accuracy. The proposed framework is evaluated in terms of rates (bits per pixel), compression ratio, peak signal to noise ratio, transmission time, mean square error and diagnostic quality under telemedicine framework. A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time (105s), is simple to implement, saves memory (18 KB), improves segmentation accuracy (98.39%), and yields better results than the same without using SPIHT. The results favor the possibility of developing a practical smartphone-enabled telemedicine system and show the potential for being implemented in the field of clinical evaluation and the management of chronic wounds in the future.

Telemedicine Supported Chronic Wound Tissue Prediction Using Classification Approaches

Telemedicine helps to deliver health services electronically to patients with the advancement of communication systems and health informatics. Chronic wound (CW) detection and its healing rate assessment at remote distance is very much difficult due to unavailability of expert doctors. This problem generally affects older ageing people. So there is a need of better assessment facility to the remote people in telemedicine framework. Here we have proposed a CW tissue prediction and diagnosis under telemedicine framework to classify the tissue types using linear discriminant analysis (LDA). The proposed telemedicine based wound tissue prediction (TWTP) model is able to identify wound tissue and correctly predict the wound status with a good degree of accuracy. The overall performance of the proposed wound tissue prediction methodology has been measured based on ground truth images. The proposed methodology will assist the clinicians to take better decision towards diagnosis of CW in terms of quantitative information of three types of tissue composition at low-resource set-up.