Technology and Health Care最新文献

Background: Obstructive sleep apnea (OSA) is a common sleep disordered breathing disorder, which can cause serious damage to multiple human systems. Although polysomnography (PSG) is the current gold standard for diagnosis, it is complex and expensive. Therefore, it is of great significance to find a simple, economical and rapid primary screening and diagnosis method to replace PSG for the diagnosis of OSA.

Objective: The purpose of this study is to propose a new method for the diagnosis and classification of OSA, which is used to automatically detect the duration of sleep apnea hypopnea events (AHE), so as to estimate the ratio(S) of the total duration of all-night AHE to the total sleep time only based on the sound signal of sleep respiration, and to identify OSA.

Methods: We performed PSG tests on participants and extracted relevant sleep breathing sound signal data. This study is carried out in two stages. In the first stage, the relevant PSG report data of eligible subjects were recorded, the total duration of AHE in each subject's data was extracted, and the S value was calculated to evaluate the severity of OSA. In the second stage, only the sleep breath sound signal data of the same batch of subjects were used for automatic detection, and the S value in the sleep breath sound signal was extracted, and the S value was compared with the PSG diagnosis results to calculate the accuracy of the experimental method.

Results: Among 225 subjects. Using PSG as the reference standard, the S value extracted from the PSG diagnostic data report can accurately diagnose OSA(accuracy rate 99.56%) and distinguish its severity (accuracy rate 95.11%). The accuracy of the S value detected in the sleep breathing sound signal in the diagnosis of severe OSA reached 100%.

Conclusion: The results show that the experimental parameter S value is feasible in OSA diagnosis and classification. OSA can be identified and evaluated only by sleep breathing sounds. This method helps to simplify the diagnostic grading of traditional OSA and lays a foundation for the subsequent development of simple diagnostic grading equipment.

Background: Standing is a basic human function that healthy adults take for granted, yet it is a complex perceptual-motor process that requires sensation of position and motion from the sensory systems.

Objective: We assessed agreement between center of pressure data from a laboratory force-platform and head position data from an HTC Vive head-mounted display (HMD) for the evaluation of standing postural control. We investigated the impact of different statistical choices when assessing the relationship between two measurements. Specifically: 1) How does correlation and agreement statistics relate before and after logarithmic transformation? 2) Is there systemic or proportional bias between the force-platform and HMD measurements?

Methods: We tested 37 adults (26 controls, 11 with unilateral vestibular hypofunction) standing on foam, observing a static or dynamic visual scene projected from the HMD. We quantified anterior-posterior and medio-lateral sway via Directional Path, Root Mean Square Velocity, Variance, and Power Spectral Density (PSD) from a force-platform and the HMD.

Results: Intra-class correlations (ICCs) were moderate-to-good for the non-transformed data and good-to-excellent after logarithmic transformation for all outcomes except for PSD above 1 Hz. Correlations were higher than ICCs. Bland-Altman plots indicated proportional bias but not after logarithmic transformation.

Conclusions: Both devices correlated linearly, and measure people's postural responses but cannot be used interchangeably, mostly because they appear to diverge with larger sway as evident on Bland-Altman plots of non-transformed data. Agreement between devices was excellent for low frequency movement but poor for high frequency small corrective movements.

Background: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification.

Objective: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification.

Methods: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT).

Results: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%.

Conclusions: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Background: The global implementation of Electronic Health Records has significantly enhanced the quality of medical care and the overall delivery of public health services. The incorporation of Evidence-Based Medicine offers numerous benefits and enhances the efficacy of decision-making in areas such as prevention, prognosis, diagnosis, and therapeutic approaches.

Objective: The objective of this paper is to propose an architectural design of an Evidence-Based Medicine information system based on the Electronic Health Record, taking into account the existing and future level of interoperability of health information systems in Greece.

Methods: A study of the suggested evidence-based medicine architectures found in the existing literature was conducted. Moreover, the interoperability architecture of health information systems in Greece was analyzed. The architecture design reviewed by specialized personnel and their recommendations were incorporated into the final design of the proposed architecture.

Results: The proposed integrated architecture of an Evidence-Based Medicine system based on the Electronic Health Record integrates and utilizes citizens' health data while leveraging the existing knowledge available in the literature.

Conclusions: Taking into consideration the recently established National Interoperability Framework, which aligns with the European Interoperability Framework, the proposed realistic architectural approach contributes to improving the quality of healthcare provided through the ability to make safe, timely and accurate decisions by physicians.

Background: Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field.

Objective: Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances.

Methods: In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features.

Results: The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness.

Conclusions: The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.

Background: Innovations in healthcare technologies have the potential to address challenges, including the monitoring of fluid balance.

Objective: This study aims to evaluate the functionality and accuracy of a digital technology compared to standard manual documentation in a real-life setting.

Methods: The digital technology, LICENSE, was designed to calculate fluid balance using data collected from devices measuring urine, oral and intravenous fluids. Participating patients were connected to the LICENSE system, which transmitted data wirelessly to a database. These data were compared to the nursing staff's manual measurements documented in the electronic patient record according to their usual practice.

Results: We included 55 patients in the Urology Department needing fluid balance charting and observed them for an average of 22.9 hours. We found a mean difference of -44.2 ml in total fluid balance between the two methods. Differences ranged from -2230 ml to 2695 ml, with a divergence exceeding 500 ml in 57.4% of cases. The primary source of error was inaccurate or omitted manual documentation. However, errors were also identified in the oral LICENSE device.

Conclusions: When used correctly, the LICENSE system performs satisfactorily in measuring urine and intravenous fluids, although the oral device requires revision due to identified errors.

Background: The first-line treatment for cervical dystonia (CD) consists of repeated intramuscular injections of botulinum toxin (BoNT). However, the efficacy in some patients may be unsatisfactory and they may discontinue treatment.

Objective: To examine the factors associated with the maximum rate of remission in patients with CD after initial botulinum neurotoxin type A (or botulinum toxin type A abbreviated as BTX-A or BoNT-A) treatment.

Methods: Patients with CD who received BoNT-A injections were evaluated using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) and the Tsui scale, with follow-up endpoints lasting until the start of the second injection. Patients who did not receive a second injection of BoNT-A were followed up for at least 5 months. The maximum remission rates were determined using the lowest Tsui and TWSTRS total scores during the follow-up period. We obtained basic information about these patients such as age, gender, duration of disease, presence of additional disease, types of torticollis, presence of anxiety, depression, tremors, single-photon emission computed tomography (SPECT) findings, injected dose, and so on from their medical records.

Results: A total of 70 patients with CD participated in this study, with males comprising 35.7% (25 individuals) with an average age of 45 ± 14 years old. The duration of disease was an independent risk factor for determining whether a complete remission has been attained using the Tsui scale (odds ratio [OR] = 0.978, 95% confidence interval [CI]: 0.959-0.997, P= 0.026). The optimal cut-off point for predicting patients who were unable to achieve complete remission based on duration of disease was 7.5 months (AUG = 0.711). Patients with CD with additional disease had greater difficulty achieving complete remission than those with CD alone based on TWSTRS assessments (P= 0.049). During the study, approximately 17% of all participants reported experiencing adverse reactions that lasted between 1 to 3 weeks before disappearing.

Conclusion: BoNT is an effective and safe method for treating CD. The maximum remission rates of patients after their first injections are influenced by the duration of their disease. Thus, treatment using BoNT injections must be administered as soon as possible.

Background: Malnutrition is a widespread problem in critically ill patients with neurological disorders.

Objective: The purpose of this study is to investigate the effect of a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support on the outcome metrics in patients with neurological disorders who are critically ill.

Methods: We enrolled 84 participants who were hospitalized in the intensive care unit (ICU) of Yancheng No. 1 People's Hospital for neurological disorders between June 2018 and December 2021. The participants were randomly assigned to the control group and the test group. The control group received traditional nutritional support, while the test group was treated with a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support. We collected the general information, feeding tolerance (FT), nutritional risk score, and laboratory indicators before intervention, after intervention for one week, and after intervention for 2 weeks, and other data of the participants.

Results: After the intervention, the test group scored significantly lower than the control group in the incidence of gastroparesis and diarrhea, as well as the NUTRIC score, with statistically significant differences (P< 0.001). The prealbumin levels in the test group increased progressively prior to intervention, after intervention for one week, and after intervention for two weeks. Compared to the control group, the test group had higher prealbumin levels prior to intervention, after intervention for one week, and after intervention for two weeks, with statistically significant differences (P< 0.001).

Conclusion: We developed a multidisciplinary collaborative nutritional treatment model based on a standard unit for nutritional support. This model can improve neural function, FT, and pertinent outcome indicators and is generally applicable.

Background: Unicompartmental knee arthroplasty (UKA) is a viable alternative to total knee arthroplasty (TKA) for osteoarthritis patients with single-compartment involvement, with advantages including accelerated recovery, reduced pain, and improved function. Robotic-assisted UKA (rUKA) is a promising development that ensures precise implant positioning and limb alignment. However, concerns about complications remain.

Objective: This study looks at patient satisfaction as a key metric for determining the efficacy of rUKA versus manual UKA (mUKA).

Methods: The search strategy for this study followed PRISMA. Using precise keywords, PubMed, Scopus, Web of Science, and the Cochrane library were searched. English articles were searched until August 2, 2023. Selection criteria included mUKA and rUKA patient satisfaction studies. The NOS scale evaluated study quality. Meta-analysis was done with R and heterogeneity analysis.

Results: This systematic review examined 5 studies with 1060 UKAs (532 robotic-assisted and 528 manual). Variable satisfaction assessment methods were used. Three studies found no difference in patient satisfaction after robotic-assisted UKA, but two found a higher satisfaction. Meta-analysis showed robotic-assisted UKA improved patient satisfaction (OR = 1.72 [1.25-2.37]). Overall, most studies showed low risk of bias, except one with higher bias.

Conclusion: This review suggests that robotic assistance may enhance patient satisfaction in UKA procedures.

Background: Danshen Chuanxiong Injection (DCI) has demonstrated significant clinical efficacy in the treatment of acute pancreatitis (AP); however, the precise molecular mechanisms underlying its therapeutic effects remain incompletely understood.

Objective: In this study, we employed network pharmacology analysis to comprehensively investigate the active components, potential targets, and signaling pathways involved in DCI-mediated treatment of AP.

Methods: We utilized the mouse pancreatic acinar cell line 266-6 to establish an cholecystokinin (CCK)-induced AP cell injury model and evaluated cell viability using the Cell counting kit-8 assay. Western blotting and quantitative PCR were employed to determine the expression levels of key target proteins and genes.

Results: Network pharmacology analysis identified a total of 144 active components and 430 potential targets within DCI. By integrating data from public databases, we identified 762 AP-related genes. Among these, we identified 93 potential targets that may be involved in the therapeutic effects of DCI for AP. These targets were significantly enriched in biological processes such as oxidative stress, regulation of cytokine production, leukocyte migration, and the TNF signaling pathway. Molecular docking studies revealed a high binding affinity between the active components and the key targets AKT1 and NFKBA, indicative of potential interaction. Additionally, CCK-induced acinar cell injury led to upregulation of AKT1, NFKBA, and P53 proteins, as well as TNF, IL6, and MMP9 genes. Conversely, treatment with DCI dose-dependently attenuated CCK-induced acinar cell injury and restored the expression levels of the aforementioned proteins and genes.

Conclusion: Overall, this study provides a comprehensive understanding of the molecular mechanisms underlying the therapeutic effects of DCI in the treatment of AP. Our findings confirm the protective effect of DCI against CCK-induced acinar cell injury and its regulation of key targets.