Emergency care during the transferal of patients with traumatic brain injury to designated trauma center

This study will provide valuable insights into emergency care during the transferal of patients with moderate and severe TBI to designated trauma center. Importantly, the results will be used to improve care during transferal, particularly in low and middle-income countries, where facilities are lacking and resources are limited

Nurses' perceptions of using an evidence-based care bundle for initial emergency nursing management of patients with severe traumatic brain injury: A qualitative study.

Evidence to guide initial emergency nursing care of patients with severe traumatic brain injury (TBI) in Thailand is currently not available in a useable form. A care bundle was used to summarise an evidence-based approach to the initial emergency nursing management of patients with severe TBI and was implemented in one Thai emergency department. The aim of this study was to describe Thai emergency nurses' perceptions of care bundle use. A descriptive qualitative study was used to describe emergency nurses' perceptions of care bundle use during the implementation phase (Phase-One) and then post-implementation (Phase-Two). Ten emergency nurses participated in Phase-One, while 12 nurses participated in Phase-Two. In Phase-One, there were five important factors identified in relation to use of the care bundle including quality of care, competing priorities, inadequate equipment, agitated patients, and teamwork. In Phase Two, participants perceived that using the care bundle helped them to improve quality of care, increased nurses' knowledge, skills, and confidence. Care bundles are one strategy to increase integration of research evidence into clinical practice and facilitate healthcare providers to deliver optimal patient care in busy environments with limited resources

Feasibility Study Of M-Health Transition Care Program For Traumatic Brain Injury Caregivers

Background: Caring in discharge transition for patients with moderate to severe traumatic brain injury (TBI) has impacted caregivers. MHealth has become popular for communication between a patient/caregiver and a health profession integrated into numerous public well-being programs in low-middle income countries but is limited for TBI caregivers. Purpose: This study aims to assess the feasibility of the mHealth supportive care transition program based on transitional care theory for improving discharge readiness and reducing caregivers\u27 transition stress and burden of caregiving and the patient\u27s readmission rate. Methods: Seven family caregivers who met inclusion criteria were recruited. The mHealth supportive care transition program includes education and face-to-face information assisted by an android-based application, skill demonstration, assessment of the readiness of hospital discharge, and weekly monitoring and follow-up after the patient\u27s discharge is given. The outcomes were evaluated using a validated and standardized scale designed to measure transition stress and the burden of caregiving at the baseline, two weeks, and one-month post-discharge, including the patient\u27s readmission one month (within 28 days) after discharge. Feedback through the mHealth satisfaction questionnaire on the trial feasibility was also collected. Results: The initial findings showed that all subjects experienced a decrease of stress transition and caregiver burden at two weeks and one-month post-discharge follow-up. High satisfaction scores on mHealth were also reported and no patient was readmitted within 28 days. Conclusion: This feasibility study showed the mHealth supportive care transition program is feasible for implementation, but it is required to test the effectiveness in the next phase on RCT with a larger sample size

Multi-Model Approach for Tongue Image Classification in Traditional Thai Medicine

Nowadays, complementary medicine is gaining widespread acceptance and is widely accepted, particularly within traditional Thai medicine (TTM). Tongue inspection is a primary method for diagnosing health conditions, as it reflects organ functionality. However, diagnostic results can vary depending on the expertise of TTM practitioners. In this work, we propose methods that incorporate transfer learning (TL) from deep learning (DL), machine learning (ML), and statistical models, using various tongue features. We introduced a collected dataset for evaluation. Experimental results demonstrated that the DenseNet121 model, trained on tongue images pre-processed with histogram equalisation (HE), achieved the best performance, with accuracy, sensitivity, and specificity of 0.89, 0.83, and 0.92, respectively. Model ensembling and paired t-tests were used to analyse the results. Finally, we identified the best approach and models for potential clinical use to assist in the pre-diagnostic analysis of tongue images for TTM practitioners and general users via our web application at http:// bioservices.sci.psu.ac.th/

kasikrit/PCa-binary-segmentation: Version 1.0.1

No description provided

Dataset of 50 prostate slides

Dataset of 50 prostate slides for the paper entitled "Semantic segmentation of gleason patterns in prostate adenocarcinoma exploiting an ensemble model of attention-based U-Net"</p

The 50 slides of dataset and trained models

Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architecturesAn accurate determination of the Gleason Score or Gleason Pattern (GP) is crucial in the diagnosis of prostate cancer (PCa) because it is one of the criterion used to guide treatment decisions for prognostic-risk groups. However, the manually designation of GP by a pathologist using a microscope is prone to error and subject to significant inter-observer variability. Deep learning has been used to automatically differentiate GP on digitized slides, aiding pathologists and reducing inter-observer variability, especially in the early GP of cancer. This paper presents a binary semantic segmentation for the GP of prostate adenocarcinoma. The segmentation separates benign and malignant tissues, with the malignant class consisting of adenocarcinoma GP3 and GP4 tissues annotated from 50 unique digitized whole slide images (WSIs) of prostate needle core biopsy specimens stained with hematoxylin and eosin. The pyramidal digitized WSIs were extracted into image patches with a size of 256 x 256 pixels at a magnification of 20X. An ensemble approach is proposed combining U-Net-based architectures, including traditional U-Net, attention-based U-Net, and residual attention-based U-Net. This work initially considers a PCa tissue analysis using a combination of attention gate units with residual convolution units. The performance evaluation revealed a mean Intersection-over-Union of 0.79 for the two classes, 0.88 for the benign class, and 0.70 for the malignant class. The proposed method was then used to produce pixel-level segmentation maps of PCa adenocarcinoma tissue slides in the testing set. We developed a screening tool to discriminate between benign and malignant prostate tissue in digitized images of needle biopsy samples using an AI approach. We aimed to identify malignant adenocarcinoma tissues from our own collected, annotated, and organized dataset. Our approach returned the performance which was accepted by the pathologists.</p