Optimization of virtual and real registration technology based on augmented reality in a surgical navigation system

Electrical stimulation: a novel therapeutic strategy to heal biological wounds

Electrical stimulation (ES) has emerged as a powerful therapeutic modality for enhancing biological wound healing. This non-invasive technique utilizes low-level electrical currents to promote tissue regeneration and expedite the wound healing process. ES has been shown to accelerate wound closure, reduce inflammation, enhance angiogenesis, and modulate cell migration and proliferation through various mechanisms. The principle goal of wound management is the rapid recovery of the anatomical continuity of the skin, to prevent infections from the external environment and maintain homeostasis conditions inside. ES at the wound site is a compelling strategy for skin wound repair. Several ES applications are described in medical literature like AC, DC, and PC to improve cutaneous perfusion and accelerate wound healing. This review aimed to evaluate the primary factors and provides an overview of the potential benefits and mechanisms of ES in wound healing, and its ability to stimulate cellular responses, promote tissue regeneration, and improve overall healing outcomes. We also shed light on the application of ES which holds excellent promise as an adjunct therapy for various types of wounds, including chronic wounds, diabetic ulcers, and surgical incisions.

Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies

INTRODUCTION

The integration of augmented reality (AR) in spine surgery marks a significant advancement, enhancing surgical precision and patient outcomes. AR provides immersive, three-dimensional visualizations of anatomical structures, facilitating meticulous planning and execution of spine surgeries. This technology not only improves spatial understanding and real-time navigation during procedures but also aims to reduce surgical invasiveness and operative times. Despite its potential, challenges such as model accuracy, user interface design, and the learning curve for new technology must be addressed. AR's application extends beyond the operating room, offering valuable tools for medical education and improving patient communication and satisfaction.

MATERIAL AND METHODS

A literature review was conducted by searching PubMed and Scopus databases using keywords related to augmented reality in spine surgery, covering publications from January 2020 to January 2024.

RESULTS

In total, 319 articles were identified through the initial search of the databases. After screening titles and abstracts, 11 articles in total were included in the qualitative synthesis.

CONCLUSION

Augmented reality (AR) is becoming a transformative force in spine surgery, enhancing precision, education, and outcomes despite hurdles like technical limitations and integration challenges. AR's immersive visualizations and educational innovations, coupled with its potential synergy with AI and machine learning, indicate a bright future for surgical care. Despite the existing obstacles, AR's impact on improving surgical accuracy and safety marks a significant leap forward in patient treatment and care.

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

The effect of electromyographic feedback functional electrical stimulation on the plantar pressure in stroke patients with foot drop

Purpose

The purpose of this study was to observe, using Footscan analysis, the effect of electromyographic feedback functional electrical stimulation (FES) on the changes in the plantar pressure of drop foot patients.

Methods

This case-control study enrolled 34 stroke patients with foot drop. There were 17 cases received FES for 20 min per day, 5 days per week for 4 weeks (the FES group) and the other 17 cases only received basic rehabilitations (the control group). Before and after 4 weeks, the walking speed, spatiotemporal parameters and plantar pressure were measured.

Results

After 4 weeks treatments, Both the FES and control groups had increased walking speed and single stance phase percentage, decreased step length symmetry index (SI), double stance phase percentage and start time of the heel after 4 weeks (p < 0.05). The increase in walking speed and decrease in step length SI in the FES group were more significant than the control group after 4 weeks (p < 0.05). The FES group had an increased initial contact phase, decreased SI of the maximal force (Max F) and impulse in the medial heel after 4 weeks (p < 0.05).

Conclusion

The advantages of FES were: the improvement of gait speed, step length SI, and the enhancement of propulsion force were more significant. The initial contact phase was closer to the normal range, which implies that the control of ankle dorsiflexion was improved. The plantar dynamic parameters between the two sides of the foot were more balanced than the control group. FES is more effective than basic rehabilitations for stroke patients with foot drop based on current spatiotemporal parameters and plantar pressure results.

From static to dynamic: The influence of mechanotransduction on skin equivalents analyzed by bioimaging and RNAseq

In this study, we explore the impact of mechanical stimuli on skin models using an innovative skin-on-a-chip platform, addressing the limitations of conventional transwell-cultured skin equivalents. This platform facilitates cyclic mechanical stimulation through compression and stretching, combined with automated media perfusion. Our findings, using bioimaging and bulk RNA sequencing, reveal increased expression of Keratin 10 and Keratin 14, indicating enhanced skin differentiation and mechanical integrity. The increase in desmosomes and tight junctions, observed through Claudin-1 and Desmoplakin 1 & 2 analysis, suggests improved keratinocyte differentiation due to mechanical stimulation. Gene expression analyses reveal a nuanced regulatory response, suggesting a potential connection to the Hippo pathway, indicative of a significant cellular reaction to mechanical stimuli. The results show the important influence of mechanical stimulation on skin model integrity and differentiation, demonstrating the potential of our microfluidic platform in advancing skin biology research and pharmaceutical testing.

The study of HMOX1 DNA methylation and gene expression and the diagnostic potential of miR-153-3p in preeclampsia

Background: The objective was to elucidate the potential epigenetic regulatory mechanism in HMOX1 expression in preeclampsia. Materials & methods: HMOX1 promoter DNA methylation was evaluated in the placental tissue and blood of preeclamptic and normotensive pregnant women. HMOX1 and miR-153-3p gene expression were assessed in placental tissue and peripheral blood mononuclear cells (PBMCs). Related microarray datasets in the Gene Expression Omnibus database were also analyzed. Results: In placental tissue, despite HMOX1 expression downregulation, there was no significant change in HMOX1 methylation. In PBMCs, there was no significant alteration in HMOX1 expression, while hypomethylation was observed in blood. The miR-153-3p expression increased in the placental tissue and in the PBMCs of preeclampsia. Conclusion: DNA methylation does not affect HMOX1 expression, while miR-153-3p might be a biomarker for preeclampsia.

Clinical application of optical and electromagnetic navigation system in CT-guided radiofrequency ablation of lung metastases

PURPOSE

To evaluate the clinical value of CT-guided radiofrequency ablation (RFA) in the diagnosis and treatment of pulmonary metastases under optical and electromagnetic navigation.

METHODS

Data on CT-guided radiofrequency ablation treatment of 93 metastatic lung lesions in 70 patients were retrospectively analyzed. There were 46 males and 24 females with a median age of 60.0 years (16-85 years). All lesions were ≤3cm in diameter. 57 patients were treated with 17 G radiofrequency ablation needle puncture directly ablated the lesion without biopsy, and 13 patients were treated with 16 G coaxial needle biopsy followed by radiofrequency ablation. There were 25 cases in the optical navigation group, 25 in the electromagnetic navigation group, and 20 in the non-navigation group. The navigation group was performed by primary interventionalists with less than 5 years of experience, and the non-navigation group was performed by interventionalists with more than 5 years of experience.

RESULT

All operations were successfully performed. There was no statistically significant difference in the overall distribution of follow-up results among the optical, electromagnetic, and no navigation groups. Complete ablation was achieved in 84 lesions (90.3%). 7 lesions showed incomplete ablation and were completely inactivated after repeat ablation. 2 lesions progressed locally, and one of them still had an increasing trend after repeat ablation. No serious complications occurred after the operation.

CONCLUSIONS

Treatment with optical and electromagnetic navigation systems by less experienced operators has similar outcomes to traditional treatments without navigational systems performed by more experienced operators.

Tumor-on-a-chip models combined with mini-tissues or organoids for engineering tumor tissues

The integration of tumor-on-a-chip technology with mini-tissues or organoids has emerged as a powerful approach in cancer research and drug development. This review provides an extensive examination of the diverse biofabrication methods employed to create mini-tissues, including 3D bioprinting, spheroids, microfluidic systems, and self-assembly techniques using cell-laden hydrogels. Furthermore, it explores various approaches for fabricating organ-on-a-chip platforms. This paper highlights the synergistic potential of combining these technologies to create tumor-on-a-chip models that mimic the complex tumor microenvironment and offer unique insights into cancer biology and therapeutic responses.

From nature to cancer therapy: Evaluating the Streptomyces clavuligerus secondary metabolites for potential protein kinase inhibitors

The study aimed to evaluate the antioxidant, protein kinase inhibitory (PKIs) potential, cytotoxicity activity of Streptomyces clavuligerus extract. DPPH assay revealed a robust free radical scavenging capacity (IC50 28.90 ± 0.24 µg/mL) of organic extract with a maximum inhibition percentage of 61 ± 1.04%. PKIs assay revealed the formation of a whitish bald zone by S. clavuligerus extracts which indicates the presence of PKIs. The cytotoxicity activity of organic fraction of extract through Sulforhodamine B assay on MCF-7, Hop-62, SiHa, and PC-3 cell lines demonstrated the lowest GI50 value against the MCF-7 cell line followed by the PC-3 cell line, showing potent growth inhibitory potential against human breast cancer and human prostate cancer cell line. HR-LCMS analysis identified multiple secondary metabolites from the organic and aqueous extracts of S. clavuligerus when incubated at 30°C under 200 rpm for 3 days. All the secondary metabolites were elucidated for their potential to inhibit RTKs by molecular docking, molecular dynamic simulation, MM/GBSA calculations, and free energy approach. It revealed the superior inhibitory potential of epirubicin (Epi) and dodecaprenyl phosphate-galacturonic acid (DPGA) against fibroblast growth factors receptor (FGFR). Epi also exhibited excellent inhibitory activity against the platelet-derived growth factor receptor (PDGFR), while DPGA effectively inhibited the vascular endothelial growth factor receptor. Additionally, the presence Epi in S. clavuligerus extract was validated through the HPLC technique. Thus, our findings highlight a superior inhibitory potential of Epi against FGFR and PDGFR RTKs than the FDA-approved drug.

Enhancing bone tissue engineering with 3D-Printed polycaprolactone scaffolds integrated with tragacanth gum/bioactive glass

Tissue-engineered bone substitutes, characterized by favorable physicochemical, mechanical, and biological properties, present a promising alternative for addressing bone defects. In this study, we employed an innovative 3D host-guest scaffold model, where the host component served as a mechanical support, while the guest component facilitated osteogenic effects. More specifically, we fabricated a triangular porous polycaprolactone framework (host) using advanced 3D printing techniques, and subsequently filled the framework's pores with tragacanth gum-45S5 bioactive glass as the guest component. Comprehensive assessments were conducted to evaluate the physical, mechanical, and biological properties of the designed scaffolds. Remarkably, successful integration of the guest component within the framework was achieved, resulting in enhanced bioactivity and increased strength. Our findings demonstrated that the scaffolds exhibited ion release (Si, Ca, and P), surface apatite formation, and biodegradation. Additionally, in vitro cell culture assays revealed that the scaffolds demonstrated significant improvements in cell viability, proliferation, and attachment. Significantly, the multi-compartment scaffolds exhibited remarkable osteogenic properties, indicated by a substantial increase in the expression of osteopontin, osteocalcin, and matrix deposition. Based on our results, the framework provided robust mechanical support during the new bone formation process, while the guest component matrix created a conducive micro-environment for cellular adhesion, osteogenic functionality, and matrix production. These multi-compartment scaffolds hold great potential as a viable alternative to autografts and offer promising clinical applications for bone defect repair in the future.

Endodontic Tissue Regeneration: A Review for Tissue Engineers and Dentists

The paradigm shift in the endodontic field from replacement toward regenerative therapies has witnessed the ever-growing research in tissue engineering and regenerative medicine targeting pulp-dentin complex in the past few years. Abundant literature on the subject that has been produced, however, is scattered over diverse areas of knowledge. Moreover, the terminology and concepts are not always consensual, reflecting the range of research fields addressing this subject, from endodontics to biology, genetics, and engineering, among others. This fact triggered some misinterpretations, mainly when the denominations of different approaches were used as synonyms. The evaluation of results is not precise, leading to biased conjectures. Therefore, this literature review aims to conceptualize the commonly used terminology, summarize the main research areas on pulp regeneration, identify future trends, and ultimately clarify whether we are really on the edge of a paradigm shift in contemporary endodontics toward pulp regeneration.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

A Pilot Human Cadaveric Study on Accuracy of the Augmented Reality Surgical Navigation System for Thoracolumbar Pedicle Screw Insertion Using a New Intraoperative Rapid Registration Method

To evaluate the feasibility and accuracy of AR-assisted pedicle screw placement using a new intraoperative rapid registration method of combining preoperative CT scanning and intraoperative C-arm 2D fluoroscopy in cadavers. Five cadavers with intact thoracolumbar spines were employed in this study. Intraoperative registration was performed using anteroposterior and lateral views of preoperative CT scanning and intraoperative 2D fluoroscopic images. Patient-specific targeting guides were used for pedicle screw placement from Th1-L5, totaling 166 screws. Instrumentation for each side was randomized (augmented reality surgical navigation (ARSN) vs. C-arm) with an equal distribution of 83 screws in each group. CT was performed to evaluate the accuracy of both techniques by assessing the screw positions and the deviations between the inserted screws and planned trajectories. Postoperative CT showed that 98.80% (82/83) screws in ARSN group and 72.29% (60/83) screws in C-arm group were within the 2-mm safe zone (p < 0.001). The mean time for instrumentation per level in ARSN group was significantly shorter than that in C-arm group (56.17 ± 3.33 s vs. 99.22 ± 9.03 s, p < 0.001). The overall intraoperative registration time was 17.2 ± 3.5 s per segment. AR-based navigation technology can provide surgeons with accurate guidance of pedicle screw insertion and save the operation time by using the intraoperative rapid registration method of combining preoperative CT scanning and intraoperative C-arm 2D fluoroscopy.

Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images

COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset.

Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective

Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of a threshold-based image segmentation technique and random forest classifier for COVID-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify COVID-19 individuals into three asperity classes such as early, progressive, and advanced, with an accuracy of 95.5% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify coronavirus severity.

A coupled thermal-electrical-structural model for balloon-based thermoplasty treatment of atherosclerosis

OBJECTIVES

The outcome of balloon-based atherosclerosis thermoplasty is closely related to the temperature/stress distribution during the treatment. For precise prediction of a required thermal lesion in the heterogeneous and thin atherosclerotic vessel, a numerical model incorporating heat-induced tissue expansion or shrinkage and the strain caused by balloon dilation is necessary.

METHODS

A fully coupled thermal-electrical-structural new model was established. The model features a heterogeneous structure including eccentric plaque, healthy artery and surrounding tissue. Tissue expansion/shrinkage and hyperelasticity material model were taken into consideration. Different heating strategies and plaque mechanical properties were investigated. The temperature distribution was compared with the traditional thermal-electrical coupled model. The possibility of thermoplasty treatment using balloons with different sizes was also explored.

RESULTS

The temperature, the electrical intensity and the stress during the thermoplasty were obtained. Lower stress was found in the heating region where tissue shrinkage occurred. The ablation depth was predicted to be ∼0.42 mm larger without coupling the biomechanical influence. The mechanical properties and input condition significantly affect the temperature and stress distribution considering the small dimensions of the tissue. Besides, with a 12.5% reduction of balloon diameter, the largest Von Mises stress decreases by 25.4%.

CONCLUSIONS

It is confirmed that a coupled thermal-electrical-structural model is needed for precise temperature prediction in the balloon-based thermoplasty of the heterogeneous and thin tissue. The model presented may help with future development of optimized treatment planning considering both ablation depth and minimum stress.

Evaluation of Mandibular Movements in Patients with Bell's Palsy Using Kinematic Variables

Background

Since the function of muscles, and subsequently the mandibular joint, is affected in patients with Bell's palsy, therefore, the evaluation of facial muscles and mandibular function in these patients can be effective in diagnosis, prevention, and treatment planning. The present study aimed to evaluate the degree of displacement and range of motion (ROM) of the mandible and the ability of the facial symmetrical muscles of patients with Bell's palsy.

Methods

This was a quasi-experimental comparative study. The variables evaluated were mandibular movement in a vertical direction and side-to-side displacement. Ten patients with Bell's palsy and 10 healthy eligible volunteers participated in the present study. Three mobile video cameras (to record jaw movements), 9 color markers, Kinovea software, House-Brackmann index, Toledo protocol, and a specialized patient questionnaire were used. Descriptive and inferential statistics were used for data analysis, the Kolmogorov-Smirnov test was used to investigate the normality of data distribution, and independent samples the t test and paired samples t test were used to compare means.

Results

The maximum lateral on the sound side was 12.40 and 4.49 mm during lateral movements of the patients' mandible, while this value was between 12.30 and 3 mm on the involved side. There is a difference between the affected side and the nonaffected side in terms of the mean lateral movements of the patients' mandible. However, this difference in the mean ROM on both sides is not statistically significant. The maximum mouth opening in healthy individuals during mandibular movements was between 40 and 60 mm, while this value was between 25 and 50 mm in the patients with Bell's palsy. This study shows a significant difference (P = 0.007) between patients and healthy individuals in terms of the mean of maximum mouth opening ( P < 0.05).

Conclusion

The results of this study showed that the ROM of temporomandibular joint (TMJ) of the patient is the same as that of normal subjects, but the side-to-side motion is more than normal which should be considered in rehabilitation treatments. The present study emphasizes the need to implement a mandibular kinematic evaluation protocol in patients with bell's palsy to prevent damage to the TMJ in the long term.

Methods to Measure Water Permeability

Water permeability is a key feature of the cell plasma membranes, and it has seminal importance for several cell functions such as cell volume regulation, cell proliferation, cell migration, and angiogenesis to name a few. The transport of water occurs mainly through plasma membrane water channels, aquaporins. Aquaporins have very important function in physiological and pathophysiological states. Due to the above, the experimental assessment of the water permeability of cells and tissues is necessary. The development of new methodologies of measuring water permeability is a vibrant scientific field that constantly develops during the last three decades along with the advances in imaging mainly. In this chapter we describe and critically assess several methods that have been developed for the measurement of water permeability both in living cells and in tissues with a focus in the first category.

3D printing in biotechnology-An insight into miniaturized and microfluidic systems for applications from cell culture to bioanalytics

Since its invention in the 1980s, 3D printing has evolved into a versatile technique for the additive manufacturing of diverse objects and tools, using various materials. The relative flexibility, straightforwardness, and ability to enable rapid prototyping are tremendous advantages offered by this technique compared to conventional methods for miniaturized and microfluidic systems fabrication (such as soft lithography). The development of 3D printers exhibiting high printer resolution has enabled the fabrication of accurate miniaturized and microfluidic systems-which have, in turn, substantially reduced both device sizes and required sample volumes. Moreover, the continuing development of translucent, heat resistant, and biocompatible materials will make 3D printing more and more useful for applications in biotechnology in the coming years. Today, a wide variety of 3D-printed objects in biotechnology-ranging from miniaturized cultivation chambers to microfluidic lab-on-a-chip devices for diagnostics-are already being deployed in labs across the world. This review explains the 3D printing technologies that are currently used to fabricate such miniaturized microfluidic devices, and also seeks to offer some insight into recent developments demonstrating the use of these tools for biotechnological applications such as cell culture, separation techniques, and biosensors.

Soft robotics and functional electrical stimulation advances for restoring hand function in people with SCI: a narrative review, clinical guidelines and future directions

Background

Recovery of hand function is crucial for the independence of people with spinal cord injury (SCI). Wearable devices based on soft robotics (SR) or functional electrical stimulation (FES) have been employed to assist the recovery of hand function both during activities of daily living (ADLs) and during therapy. However, the implementation of these wearable devices has not been compiled in a review focusing on the functional outcomes they can activate/elicit/stimulate/potentiate. This narrative review aims at providing a guide both for engineers to help in the development of new technologies and for clinicians to serve as clinical guidelines based on the available technology in order to assist and/or recover hand function in people with SCI.

Methods

A literature search was performed in Scopus, Pubmed and IEEE Xplore for articles involving SR devices or FES systems designed for hand therapy or assistance, published since 2010. Only studies that reported functional outcomes from individuals with SCI were selected. The final collections of both groups (SR and FES) were analysed based on the technical aspects and reported functional outcomes.

Results

A total of 37 out of 1101 articles were selected, 12 regarding SR and 25 involving FES devices. Most studies were limited to research prototypes, designed either for assistance or therapy. From an engineering perspective, technological improvements for home-based use such as portability, donning/doffing and the time spent with calibration were identified. From the clinician point of view, the most suitable technical features (e.g., user intent detection) and assessment tools should be determined according to the particular patient condition. A wide range of functional assessment tests were adopted, moreover, most studies used non-standardized tests.

Conclusion

SR and FES wearable devices are promising technologies to support hand function recovery in subjects with SCI. Technical improvements in aspects such as the user intent detection, portability or calibration as well as consistent assessment of functional outcomes were the main identified limitations. These limitations seem to be be preventing the translation into clinical practice of these technological devices created in the laboratory.

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.

Application of a three-dimensional visualization model in intraoperative guidance of percutaneous nephrolithotomy

OBJECTIVES

To establish a three-dimensional visualization model of percutaneous nephrolithotomy, apply it to guiding intraoperative puncture in a mixed reality environment, and evaluate its accuracy and clinical value.

METHODS

Patients with percutaneous nephrolithotomy indications were prospectively divided into three-dimensional group and control group with a ratio of 1:2. For patients in three-dimensional group, positioning markers were pasted on the skin and enhanced computed tomography scanning was performed in the prone position. Holographic three-dimensional models were made and puncture routes were planned before operation. During the operation, the three-dimensional model was displayed through HoloLens glass and visually registered with the patient's body. Puncture of the target renal calyx was performed under three-dimensional-image guiding and ultrasonic monitoring. Patients in the control group underwent routine percutaneous nephrolithotomy in the prone position under the monitoring of B-ultrasound. Deviation distance of the kidney, puncture time, puncture attempts, channel coincidence rate, stone clearance rate, and postoperative complications were assessed.

RESULTS

Twenty-one and 40 patients were enrolled in three-dimensional and control group, respectively. For three-dimensional group, the average deviation between virtual and real kidney was 3.1 ± 2.9 mm. All punctures were performed according to preoperative planning. Compared with the control group, the three-dimensional group had shorter puncture time (8.9 ± 3.3 vs 14.5 ± 6.1 min, P < 0.001), fewer puncture attempts (1.4 ± 0.6 vs 2.2 ± 1.5, P = 0.009), and might also have a better performance in stone clearance rate (90.5% vs 72.5%, P = 0.19) and postoperative complications (P = 0.074).

CONCLUSIONS

The percutaneous nephrolithotomy three-dimensional model manifested acceptable accuracy and good value for guiding puncture in a mixed reality environment.

Endothelial inflammation and neutrophil transmigration are modulated by extracellular matrix composition in an inflammation-on-a-chip model

Inflammatory diseases are often characterised by excessive neutrophil infiltration from the blood stream to the site of inflammation, which damages healthy tissue and prevents resolution of inflammation. Development of anti-inflammatory drugs is hindered by lack of in vitro and in vivo models which accurately represent the disease microenvironment. In this study, we used the OrganoPlate to develop a humanized 3D in vitro inflammation-on-a-chip model to recapitulate neutrophil transmigration across the endothelium and subsequent migration through the extracellular matrix (ECM). Human umbilical vein endothelial cells formed confluent vessels against collagen I and geltrex mix, a mix of basement membrane extract and collagen I. TNF-α-stimulation of vessels upregulated inflammatory cytokine expression and promoted neutrophil transmigration. Intriguingly, major differences were found depending on the composition of the ECM. Neutrophils transmigrated in higher number and further in geltrex mix than collagen I, and did not require an N-formyl-methionyl-leucyl-phenylalanine (fMLP) gradient for transmigration. Inhibition of neutrophil proteases inhibited neutrophil transmigration on geltrex mix, but not collagen I. These findings highlight the important role of the ECM in determining cell phenotype and response to inhibitors. Future work could adapt the ECM composition for individual diseases, producing accurate models for drug development.

AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models.

A Novel Approach for Identification of Biomakers in Diabetic Retinopathy Recognition

In the emergence of anti-Antivascular endothelial growth factor (VEGF) drugs such as ranibizumab and bevacizumab, it has become obvious that the presence of outer retinal and subretinal fluid is the primary signal of the need for anti-VEGF therapy, and used to identify disease activity and assist diabetic retinopathy treatment. Despite advancements in diabetic retinopathy (DR) treatments, early detection is critical for DR management and remains a significant barrier. Clinical DR can be distinguished from non proliferative DR without visible vision loss and vision-threatening consequences such as macular edoema and proliferative retinopathy by retinal alterations in diabetes. The proposed method aggrandize the process of accurate detection of biomakers responsible for higher risk of diabetic retinopathy development in color fundus images. Furthermore, the proposed approach could be employed to quantify these lesions and their distributions efficientively as evident in the experimentation results.

The effects of coronavirus disease 2019 (COVID-19) pandemic on people with epilepsy (PwE): an online survey-based study

During the unprecedented COVID-19 pandemic in 2020, the whole world faced an unusual health emergency. Medical care of chronic neurological diseases, such as Epilepsy, is being neglected. In this survey, we aimed to evaluate the impact of the COVID-19 pandemic on the care of people with Epilepsy (PwE) and to identify their risk factors for seizure worsening to direct better future medical care. We administered a web-based survey (submitted on August 5, 2020). It included socio-demographic, Epilepsy-related, and psychometric data (The Depression, Anxiety, and Stress Scale—21 Items(DASS21) and The Pittsburgh Sleep Quality Index (PSQI). Regression analysis identified predictors of seizure worsening. We collected responses from an online survey of PwE during the pandemic. Out of 151 responders, 71 patients complained of issues related to Epilepsy management and all of whom reached the treating physician and solved their problems. Sleep quality was compromised in 84 patients (55.6%). Two-thirds of the patients in our cohort (66.2%) reported depression, 72.2% reported anxiety, and 75.5% reported stress. Eight patients (5.3%) got COVID-19 infection, and only one patient suffered from mild worsening of the seizure. The main concerns were shortage of medications for 46 (30.5%) patients, getting Coronavirus infection for 67 (44.4%) patients, and seizure worsening for 32 (21.3%) patients. Thirty-five patients (23.2%) reported seizure worsening, which was best explained by retirement or jobless state, having moderate or severe stress, poor sleep quality, vagus nerve stimulation (VNS), fear of getting COVID-19 infection, fear of worsening of seizures, or shortage of medication. During the current COVID-19 pandemic, a significant percentage of PwE experienced worsening of their seizures. This unusual, challenging experience clarifies the urgent need to establish telemedicine services and home-based management of Epilepsy, including ambulatory EEG, home video, and medication delivery to patients’ homes to provide continuous medical care.

SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network

COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies implied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population. Motivated by various contributions to open-source community to tackle COVID-19 pandemic, we introduce SARS-Net, a CADx system combining Graph Convolutional Networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of COVID-19 infection in a patient. In this paper, we introduce and evaluate the performance of a custom-made deep learning architecture SARS-Net, to classify and detect the Chest X-ray images for COVID-19 diagnosis. Quantitative analysis shows that the proposed model achieves more accuracy than previously mentioned state-of-the-art methods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set.

Development of an innovative surgical navigation system for sacrospinous fixation in pelvic surgery

STUDY OBJECTIVE

To validate the use of an innovative navigation method for sacrospinous fixation in surgery-like conditions as a new teaching tool and surgical method.

DESIGN

2-month-experiment prospective pilot study between July and August 2021.

SETTING

Biomechanics laboratory academic research.

POPULATION

29 participants: 9 gynecological surgeons and 20 participants with no medical background.

MEASUREMENT AND MAIN RESULTS

The experiment was composed of two training phases dedicated to improve the hand-eye coordination and suture skills on a training mock-up, and of a suturing phase on a pelvic mock-up designed to recreate the surgery-like conditions of a sacrospinous fixation. The surgeons provided qualitative feedback on the bio-accuracy of the mock-ups and evaluated the ease-of-use of the navigation software. Non-surgeons were included to assess the progression of the suture performance between two experiments performed one week apart (Session 1 & 2). The main objective for participants was to reach a virtual target and to stitch sacrospinous ligaments. For Session 1, an overall comfort score of 7.2/10 was attributed to the tool; 14 (42%) surgeon suture attempts and 63 (65%) non-surgeon suture attempts were accurate (i.e. below the 5-mm threshold). 22 (67%) surgeon suture attempts and 28 (34%) non-surgeon suture attempts were fast (i.e. in the first two quantiles of the duration dataset). An improvement of the non-surgeon performance was observed between the two sessions in terms of duration (Session 1: 46±20 sec; Session 2: 37±18 sec; p=0.047) and distance (Session 1: 3.8±1.3 mm; Session 2: 3.2±1.4 mm; p=10^-5) for the last suturing exercise.

CONCLUSION

This new motion-capture-based navigation method for sacrospinous fixation tested under surgery-like conditions seemed to be accurate and effective. The next step will be to design a pelvis model more adapted to the constraints of a sacrospinous fixation and to validate the benefits of this method compared to current techniques.

An amplitude-based characteristic parameter extraction algorithm for cerebral edema detection based on electromagnetic induction

Background

Cerebral edema is a common condition secondary to any type of neurological injury. The early diagnosis and monitoring of cerebral edema is of great importance to improve the prognosis. In this article, a flexible conformal electromagnetic two-coil sensor was employed as the electromagnetic induction sensor, associated with a vector network analyzer (VNA) for signal generation and receiving. Measurement of amplitude data over the frequency range of 1–100 MHz is conducted to evaluate the changes in cerebral edema. We proposed an Amplitude-based Characteristic Parameter Extraction (Ab-CPE) algorithm for multi-frequency characteristic analysis over the frequency range of 1–100 MHz and investigated its performance in electromagnetic induction-based cerebral edema detection and distinction of its acute/chronic phase. Fourteen rabbits were enrolled to establish cerebral edema model and the 24 h real-time monitoring experiments were carried out for algorithm verification.

Results

The proposed Ab-CPE algorithm was able to detect cerebral edema with a sensitivity of 94.1% and specificity of 95.4%. Also, in the early stage, it can detect cerebral edema with a sensitivity of 85.0% and specificity of 87.5%. Moreover, the Ab-CPE algorithm was able to distinguish between acute and chronic phase of cerebral edema with a sensitivity of 85.0% and specificity of 91.0%.

Conclusion

The proposed Ab-CPE algorithm is suitable for multi-frequency characteristic analysis. Combined with this algorithm, the electromagnetic induction method has an excellent performance on the detection and monitoring of cerebral edema.

A fully automatic surgical registration method for percutaneous abdominal puncture surgical navigation

Surgical registration that maps surgical space onto image space plays an important role in surgical navigation. Accurate surgical registration can help surgeons efficiently locate surgical instruments. The complicated marker-based surgical registration method is highly accurate, but it is time-consuming. Therefore, a marker-less surgical registration method with high-precision and high-efficiency is proposed without human intervention. Firstly, the surgical navigation system based on the multi-vision system is calibrated by using a specially-designed calibration board. When extracting the abdominal point cloud acquired by the structured light vision system, the constraint is constructed by using Computed Tomography (CT) image to filter out the points in irrelevant areas to improve the computational efficiency. The Coherent Point Drift (CPD) algorithm based on Gaussian Mixture Model (GMM) is applied in the registration of abdominal point cloud with lack of surface features. To enhance the efficiency of the CPD algorithm, firstly, the system calibration result is used in rough registration of the point cloud, and then the proper point cloud pretreatment method and its parameters are studied through experiments. Finally, the puncturing simulation experiments were carried out by using the abdominal phantom. The experimental results show that the proposed surgical registration method has high accuracy and efficiency, and has potential clinical application value.

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

Discrimination between healthy and patients with Parkinson's disease from hand resting activity using inertial measurement unit

BACKGROUND

Parkinson's disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings.

METHODS

A total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)].

RESULTS

When all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features.

CONCLUSION

Through an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.

A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques

Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In this study, we develop a hybrid model for forecasting PM₁₀ and PM_2.5 based on the multiscale characterization and ML techniques. At first, we use the empirical mode decomposition (EMD) algorithm for multiscale characterization of PM₁₀ and PM_2.5 by decomposing the original time series into numerous intrinsic mode functions (IMFs). Different individual ML algorithms such as random forest (RF), support vector regressor (SVR), k-nearest neighbors (kNN), feed forward neural network (FFNN), and AdaBoost are then used to develop EMD-ML models. The air quality time series data from Masfalah air station Makkah, Saudi Arabia are utilized for validating the EMD-ML models, and results are compared with non-hybrid ML models. The PMs (PM₁₀ and PM_2.5) concentrations data of Dehli, India are also utilized for validating the EMD-ML models. The performance of each model is evaluated using root mean square error (RMSE) and mean absolute error (MAE). The average bias in the predictive model is estimated using mean bias error (MBE). Obtained results reveal that EMD-FFNN model provides the lowest error rate for both PM₁₀ (RMSE = 12.25 and MAE = 7.43) and PM_2.5 (RMSE = 4.81 and MAE = 3.02) using Misfalah, Makkah data whereas EMD-kNN model provides the lowest error rate for PM₁₀ (RMSE = 20.56 and MAE = 12.87) and EMD-AdaBoost provides the lowest error rate for PM_2.5 (RMSE = 15.29 and MAE = 9.45) using Dehli, India data. The findings also reveal that EMD-ML models can be effectively used in forecasting PM mass concentrations and to develop rapid air quality warning systems.

Classification of Epileptic EEG Signals Using Synchrosqueezing Transform and Machine Learning

Epilepsy is a neurological disease that is very common worldwide. Patient's electroencephalography (EEG) signals are frequently used for the detection of epileptic seizure segments. In this paper, a high-resolution time-frequency (TF) representation called Synchrosqueezing Transform (SST) is used to detect epileptic seizures. Two different EEG data sets, the IKCU data set we collected, and the publicly available CHB-MIT data set are analyzed to test the performance of the proposed model in seizure detection. The SST representations of seizure and nonseizure (pre-seizure or inter-seizure) EEG segments of epilepsy patients are calculated. Various features like higher-order joint TF (HOJ-TF) moments and gray-level co-occurrence matrix (GLCM)-based features are calculated using the SST representation. By using single and ensemble machine learning methods such as k-Nearest Neighbor (kNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Boosted Trees (BT), and Subspace kNN (S-kNN), EEG features are classified. The proposed SST-based approach achieved 95.1% ACC, 96.87% PRE, 95.54% REC values for the IKCU data set, and 95.13% ACC, 93.37% PRE, 90.30% REC values for the CHB-MIT data set in seizure detection. Results show that the proposed SST-based method utilizing novel TF features outperforms the short-time Fourier transform (STFT)-based approach, providing over 95% accuracy for most cases, and compares well with the existing methods.