Wound healing assessment using digital photography: a review

Digital photography as a non-invasive, simple, objective, reproducible, and practical imaging modality has been investigated for the wound healing assessment over the last three decades, and now has been widely used in clinical daily routine. Advances in the field of image analysis and computational intelligence techniques along with the improvements in digital camera instrumentation, expand the applications of standardized digital photography in diagnostic dermatology such as evaluation of tumours, erythema, and ulcers. A series of digital images taken at regular intervals carries the most informative wound healing indexes, color and dimension, that may help clinicians to evaluate the effectiveness of a particular treatment regimen, to relieve patient discomfort, to globally assess the healing kinetics, and to quantitatively compare different therapies; however, the extent of underlying tissue damage cannot be fully detected. This paper is an introductory review of the important investigations proposed by researchers in the context of clinical wound assessment. The principles of wound assessment using digital photography were shortly described, followed by review of the related literature in four main domains: wound tissue segmentation, automated wound area measurement, wound three dimensional (3D) analysis and volumetric measurement, and monitoring and evaluation of wound tissue changes during healing

High-frequency ultrasound imaging in wound assessment: current perspectives

Non-invasive imaging modalities for wound assessment have become increasingly popular over the past two decades. The wounds can be developed superficially or from within deep tissues, depending on the nature of the dominant risk factors. Developing a reproducible quantitative method to assess wound-healing status has demonstrated to be a convoluted task. Advances in High-Frequency Ultrasound (HFU) skin scanners have expanded their application as they are cost-effective and reproducible diagnostic tools in dermatology, including for the measurement of skin thickness, the assessment of skin tumours, the estimation of the volume of melanoma and non-melanoma skin cancers, the visualisation of skin structure and the monitoring of the healing of acute and chronic wounds. Previous studies have revealed that HFU images carry dominant parameters and depict the phenomena occurring within deep tissue layers during the wound-healing process. However, the investigations have mostly focussed on the validation of HFU images, and few studies have utilised HFU imaging in quantitative assessment of wound generation and healing. This paper is an introductory review of the important studies proposed by the researchers in the context of wound assessment. The principles of dermasonography are briefly explained, followed by a review of the relevant literature that investigated the wound-healing process and tissue structures within the wound using HFU imaging

Gait analysis of national athletes after anterior cruciate ligament reconstruction following three stages of rehabilitation program: symmetrical perspective

This study aimed to objectively evaluate changes in gait kinematics, kinetics and symmetry among anterior cruciate ligament (ACL) reconstructed athletes during rehabilitation. Twenty-two national athletes with ACL reconstruction and 15 healthy athletes were recruited for the study. Gait data were collected between the weeks 4–5, 8–9, and 12–13 post-operation using three-dimensional motion analysis system. Five separate components, including knee range of motion (ROM), vertical ground reaction force (VGRF), their symmetries and knee extension moment were evaluated. One way and repeated measure multivariate analysis of variance (MANOVA) were used to analyze the knee ROMs. The VGRF and extension moment were tested using repeated measure ANOVA and independent sample t-test. Findings indicated significant alterations in all measured components between patients’ Test 1 and control group. Repeated measure analysis revealed significant effect for time in components of knee angular and VGRF (P < 0.001), their symmetry index (P = 0.03) and knee extension moment (P = 0.045). Univariate outcomes demonstrated significant improvement in the injured limb's stance and swing (P < 0.001), and single-stance (P = 0.005) ROMs over time. Symmetry indexes of stance and swing ROM, and VGRF reduced significantly by 26.3% (P = 0.001), 17.9% (P < 0.001), and 31.9% (P = 0.03) respectively. After three months, symmetry indexes of single-stance ROM and VGRF along with operated knee extension moment were the only variables which showed significant differences with control group. The rehabilitation program allowed national athletes to restore the operated limb's gait parameters except knee extension moment by 12–13 weeks post-reconstruction; however, more time is required to normalize single-stance ROM and VGRF asymmetries

Quantitative assessment of wound healing using high-frequency ultrasound image analysis

Purpose: We aimed to develop a method for quantitative assessment of wound healing in ulcerated diabetic feet. Methods: High‐frequency ultrasound (HFU) images of 30 wounds were acquired in a controlled environment on post‐debridement days 7, 14, 21, and 28. Meaningful features portraying changes in structure and intensity of echoes during healing were extracted from the images, their relevance and discriminatory power being verified by analysis of variance. Relative analysis of tissue healing was conducted by developing a features‐based healing function, optimised using the pattern‐search method. Its performance was investigated through leave‐one‐out cross‐validation technique and reconfirmed using principal component analysis. Results: The constructed healing function could depict tissue changes during healing with 87.8% accuracy. The first principal component derived from the extracted features demonstrated similar pattern to the constructed healing function, accounting for 86.3% of the data variance. Conclusion: The developed wound analysis technique could be a viable tool in quantitative assessment of diabetic foot ulcers during healing

Effects of Tele-Pilates and Tele-Yoga on Biochemicals, Physical, and Psychological Parameters of Females with Multiple Sclerosis

Background: People with multiple sclerosis (PwMS) suffer from some comorbidities, including physical and psychiatric disorders, low quality of life (QoL), hormonal dysregulation, and hypothalamic-pituitary-adrenal axis dysfunction. The current study aimed to investigate the effects of eight weeks of tele-yoga and tele-Pilates on the serum levels of prolactin and cortisol and selected physical and psychological factors. Methods: Forty-five females with relapsing remitting multiple sclerosis, based on age (18–65), expanded disability status scale (0–5.5), and body mass index (20–32), were randomly assigned to tele-Pilates, tele-yoga, or control groups (n = 15). Serum blood samples and validated questionnaires were collected before and after interventions. Results: Following online interventions, there was a significant increase in the serum levels of prolactin (p = 0.004) and a significant decrease in cortisol (p = 0.04) in the time × group interaction factors. In addition, significant improvements were observed in depression (p = 0.001), physical activity levels (p p ≤ 0.001), and the speed of walking (p < 0.001). Conclusion: Our findings suggest that tele-yoga and tele-Pilates training could be introduced as patient-friendly, non-pharmacological, add-on therapeutic methods for increasing prolactin and decreasing cortisol serum levels and achieving clinically relevant improvements in depression, walking speed, physical activity level, and QoL in female MS patients

Roles of Inorganic Oxide Based HTMs towards Highly Efficient and Long-Term Stable PSC—A Review

In just a few years, the efficiency of perovskite-based solar cells (PSCs) has risen to 25.8%, making them competitive with current commercial technology. Due to the inherent advantage of perovskite thin films that can be fabricated using simple solution techniques at low temperatures, PSCs are regarded as one of the most important low-cost and mass-production prospects. The lack of stability, on the other hand, is one of the major barriers to PSC commercialization. The goal of this review is to highlight the most important aspects of recent improvements in PSCs, such as structural modification and fabrication procedures, which have resulted in increased device stability. The role of different types of hole transport layers (HTL) and the evolution of inorganic HTL including their fabrication techniques have been reviewed in detail in this review. We eloquently emphasized the variables that are critical for the successful commercialization of perovskite devices in the final section. To enhance perovskite solar cell commercialization, we also aimed to obtain insight into the operational stability of PSCs, as well as practical information on how to increase their stability through rational materials and device fabrication

Smart-Hydroponic-Based Framework for Saffron Cultivation: A Precision Smart Agriculture Perspective

Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite the use of different artificial methods for cultivation, hydroponic approaches using the IoT prove to give the best results. The presented study consists of potential artificial approaches used for cultivation and the selection of hydroponics as the best approach out of these based on different parameters. This paper also provides a comparative analysis of six present hydroponic approaches. The research work on different factors of saffron, such as the parameters responsible for growth, reasons for the decline in growth, and different agronomical variables, has been shown graphically. A smart hydroponic system for saffron cultivation has been proposed using the NFT (nutrient film technique) and renewable sources of energy

Smart-Hydroponic-Based Framework for Saffron Cultivation: A Precision Smart Agriculture Perspective

Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite the use of different artificial methods for cultivation, hydroponic approaches using the IoT prove to give the best results. The presented study consists of potential artificial approaches used for cultivation and the selection of hydroponics as the best approach out of these based on different parameters. This paper also provides a comparative analysis of six present hydroponic approaches. The research work on different factors of saffron, such as the parameters responsible for growth, reasons for the decline in growth, and different agronomical variables, has been shown graphically. A smart hydroponic system for saffron cultivation has been proposed using the NFT (nutrient film technique) and renewable sources of energy

A Novel Machine-Learning-Based Hybrid CNN Model for Tumor Identification in Medical Image Processing

The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event extraction has become a research hotspot in academia. Many academic conferences publish it as an evaluation task and provide a series of high-quality annotation data. This article aims at the characteristics of discrete attributes of tumor-related medical events and proposes a medical event. The standard extraction method realizes the combined extraction of the primary tumor site and primary tumor size characteristics, as well as the extraction of tumor metastasis sites. In addition, given the problems of the small number and types of annotation texts for tumor-related medical events, a key-based approach is proposed. A pseudo-data-generation algorithm that randomly replaces information in the whole domain improves the transfer learning ability of the standard extraction method for different types of tumor-related medical event extractions. The proposed method won third place in the clinical medical event extraction and evaluation task of the CCKS2020 electronic medical record. A large number of experiments on the CCKS2020 dataset verify the effectiveness of the proposed method

A Novel Machine-Learning-Based Hybrid CNN Model for Tumor Identification in Medical Image Processing

The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event extraction has become a research hotspot in academia. Many academic conferences publish it as an evaluation task and provide a series of high-quality annotation data. This article aims at the characteristics of discrete attributes of tumor-related medical events and proposes a medical event. The standard extraction method realizes the combined extraction of the primary tumor site and primary tumor size characteristics, as well as the extraction of tumor metastasis sites. In addition, given the problems of the small number and types of annotation texts for tumor-related medical events, a key-based approach is proposed. A pseudo-data-generation algorithm that randomly replaces information in the whole domain improves the transfer learning ability of the standard extraction method for different types of tumor-related medical event extractions. The proposed method won third place in the clinical medical event extraction and evaluation task of the CCKS2020 electronic medical record. A large number of experiments on the CCKS2020 dataset verify the effectiveness of the proposed method