CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI.

Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image variations, cardiac image segmentation is a challenging task. Quantification of cardiac parameters requires high-performance segmentation of the left ventricle (LV), right ventricle (RV), and left ventricle myocardium from the background. The first proposed solution here is to manually segment the regions, which is a time-consuming and error-prone procedure. In this context, many semi- or fully automatic solutions have been proposed recently, among which deep learning-based methods have revealed high performance in segmenting regions in CMRI data. In this study, a self-adaptive multi attention (SMA) module is introduced to adaptively leverage multiple attention mechanisms for better segmentation. The convolutional-based position and channel attention mechanisms with a patch tokenization-based vision transformer (ViT)-based attention mechanism in a hybrid and end-to-end manner are integrated into the SMA. The CNN- and ViT-based attentions mine the short- and long-range dependencies for more precise segmentation. The SMA module is applied in an encoder-decoder structure with a ResNet50 backbone named CardSegNet. Furthermore, a deep supervision method with multi-loss functions is introduced to the CardSegNet optimizer to reduce overfitting and enhance the model's performance. The proposed model is validated on the ACDC2017 (n=100), M&Ms (n=321), and a local dataset (n=22) using the 10-fold cross-validation method with promising segmentation results, demonstrating its outperformance versus its counterparts.

Can Binaural Beat Music Be Useful as a Method to Reduce Dental Patients' Anxiety?

BACKGROUND: One of the main issues in dentistry and a barrier to offering dental treatment is anxiety. The usage of music is one of the nonmedical ways to reduce anxiety. Binaural beat technology is used as a music treatment technique. The goal of this study wasto determine whether employing binaural beat technology during and after dental appointments can help patients feel less anxiety and pain. METHODS: In this clinical trial, 80 patients who were candidates for mandibular wisdom tooth surgery (in 2 test and control groups) were examined. In the control group, after the injection of anaesthesia and before surgery, they waited for 10 minutes and during this time no intervention was done. In the test group, thought, after the injection of anaesthesia, the patients were asked to listen to binaural beat music with headphones for 10 minutes. The level of anxiety of the patients before and after the intervention was checked with the Spielberger State-Trait Anxiety Inventory and finally the data were entered into SPSS version 21 software. RESULTS: The score of overt anxiety (P = .524) and covert anxiety (P = .118) before the start of the study was not significant between the 2 groups, but overt anxiety (P = .001) and covert anxiety (P = .000) after the intervention in the test group decreased significantly. CONCLUSIONS: The research showed that the use of binaural beat music has significantly reduced the level of overt and covert anxiety in patients and can be used as an alternative nonpharmacologic method to reduce anxiety.

A GU-Net-Based Architecture Predicting Ligand-Protein-Binding Atoms.

Background: The first step in developing new drugs is to find binding sites for a protein structure that can be used as a starting point to design new antagonists and inhibitors. The methods relying on convolutional neural network for the prediction of binding sites have attracted much attention. This study focuses on the use of optimized neural network for three-dimensional (3D) non-Euclidean data. Methods: A graph, which is made from 3D protein structure, is fed to the proposed GU-Net model based on graph convolutional operation. The features of each atom are considered as attributes of each node. The results of the proposed GU-Net are compared with a classifier based on random forest (RF). A new data exhibition is used as the input of RF classifier. Results: The performance of our model is also examined through extensive experiments on various datasets from other sources. GU-Net could predict the more number of pockets with accurate shape than RF. Conclusions: This study will enable future works on a better modeling of protein structures that will enhance knowledge of proteomics and offer deeper insight into drug design process.

RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation.

Optical coherence tomography (OCT) helps ophthalmologists assess macular edema, accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is necessary for OCT-guided treatment management, which relies on a precise image segmentation step. As manual analysis of retinal fluids is a time-consuming, subjective, and error-prone task, there is increasing demand for fast and robust automatic solutions. In this study, a new convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. The model benefits from hierarchical representation learning of textural, contextual, and edge features using a new self-adaptive dual-attention (SDA) module, multiple self-adaptive attention-based skip connections (SASC), and a novel multi-scale deep self-supervision learning (DSL) scheme. The attention mechanism in the proposed SDA module enables the model to automatically extract deformation-aware representations at different levels, and the introduced SASC paths further consider spatial-channel interdependencies for concatenation of counterpart encoder and decoder units, which improve representational capability. RetiFluidNet is also optimized using a joint loss function comprising a weighted version of dice overlap and edge-preserved connectivity-based losses, where several hierarchical stages of multi-scale local losses are integrated into the optimization process. The model is validated based on three publicly available datasets: RETOUCH, OPTIMA, and DUKE, with comparisons against several baselines. Experimental results on the datasets prove the effectiveness of the proposed model in retinal OCT fluid segmentation and reveal that the suggested method is more effective than existing state-of-the-art fluid segmentation algorithms in adapting to retinal OCT scans recorded by various image scanning instruments.

Population-based study of multisystem inflammatory syndrome associated with COVID-19 found that 36% of children had persistent symptoms.

AIM: Our aim was to describe the outcomes of multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19. METHODS: This national, population-based, longitudinal, multicentre study used Swedish data that were prospectively collected between 1 December 2020 and 31 May 2021. All patients met the World Health Organization criteria for MIS-C. The outcomes 2 and 8 weeks after diagnosis are presented, and follow-up protocols are suggested. RESULTS: We identified 152 cases, and 133 (87%) participated. When followed up 2 weeks after MIS-C was diagnosed, 43% of the 119 patients had abnormal results, including complete blood cell counts, platelet counts, albumin levels, electrocardiograms and echocardiograms. After 8 weeks, 36% of 89 had an abnormal patient history, but clinical findings were uncommon. Echocardiogram results were abnormal in 5% of 67, and the most common complaint was fatigue. Older children and those who received intensive care were more likely to report symptoms and have abnormal cardiac results. CONCLUSION: More than a third (36%) of the patients had persistent symptoms 8 weeks after MIS-C, and 5% had abnormal echocardiograms. Older age and higher levels of initial care appeared to be risk factors. Structured follow-up visits are important after MIS-C.

Point-of-care testing in a high-income country paediatric emergency department: a qualitative study in Sweden.

OBJECTIVES: In many resource-limited health systems, point-of-care tests (POCTs) are the only means for clinical patient sample analyses. However, the speed and simplicity of POCTs also makes their use appealing to clinicians in high-income countries (HICs), despite greater laboratory accessibility. Although also part of the clinical routine in HICs, clinician perceptions of the utility of POCTs are relatively unknown in such settings as compared with others. In a Swedish paediatric emergency department (PED) where POCT use is routine, we aimed to characterise healthcare providers' perspectives on the clinical utility of POCTs and explore their implementation in the local setting; to discuss and compare such perspectives, to those reported in other settings; and finally, to gather requests for ideal novel POCTs. DESIGN: Qualitative focus group discussions study. A data-driven content analysis approach was used for analysis. SETTING: The PED of a secondary paediatric hospital in Stockholm, Sweden. PARTICIPANTS: Twenty-four healthcare providers clinically active at the PED were enrolled in six focus groups. RESULTS: A range of POCTs was routinely used. The emerging theme Utility of our POCT use is double-edged illustrated the perceived utility of POCTs. While POCT services were considered to have clinical and social value, the local routine for their use was named to distract clinicians from the care for patients. Requests were made for ideal POCTs and their implementation. CONCLUSION: Despite their clinical integration, deficient implementation routines limit the benefits of POCT services to this well-resourced paediatric clinic. As such deficiencies are shared with other settings, it is suggested that some characteristics of POCTs and of their utility are less related to resource level and more to policy deficiency. To address this, we propose the appointment of skilled laboratory personnel as ambassadors to hospital clinics offering POCT services, to ensure higher utility of such services.

Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images.

PURPOSE: Subretinal injections of therapeutics are commonly used to treat ocular diseases. Accurate dosing of therapeutics at target locations is crucial but difficult to achieve using subretinal injections due to leakage, and there is no method available to measure the volume of therapeutics successfully administered to the subretinal location during surgery. Here, we introduce the first automatic method for quantifying the volume of subretinal blebs, using porcine eyes injected with Ringer's lactate solution as samples. DESIGN: Ex vivo animal study. METHODS: Microscope-integrated optical coherence tomography was used to obtain 3D visualization of subretinal blebs in porcine eyes at Duke Eye Center. Two different injection phases were imaged and analyzed in 15 eyes (30 volumes), selected from a total of 37 eyes. The inclusion/exclusion criteria were set independently from the algorithm-development and testing team. A novel lightweight, deep learning-based algorithm was designed to segment subretinal bleb boundaries. A cross-validation method was used to avoid selection bias. An ensemble-classifier strategy was applied to generate final results for the test dataset. RESULTS: The algorithm performs notably better than 4 other state-of-the-art deep learning-based segmentation methods, achieving an F1 score of 93.86 ± 1.17% and 96.90 ± 0.59% on the independent test data for entry and full blebs, respectively. CONCLUSION: The proposed algorithm accurately segmented the volumetric boundaries of Ringer's lactate solution delivered into the subretinal space of porcine eyes with robust performance and real-time speed. This is the first step for future applications in computer-guided delivery of therapeutics into the subretinal space in human subjects.

Point-of-Care Approaches for Meningitis Diagnosis in a Low-Resource Setting (Southwestern Uganda): Observational Cohort Study Protocol of the "PI-POC" Trial.

BACKGROUND: A timely differential diagnostic is essential to identify the etiology of central nervous system (CNS) infections in children, in order to facilitate targeted treatment, manage patients, and improve clinical outcome. OBJECTIVE: The Pediatric Infection-Point-of-Care (PI-POC) trial is investigating novel methods to improve and strengthen the differential diagnostics of suspected childhood CNS infections in low-income health systems such as those in Southwestern Uganda. This will be achieved by evaluating (1) a novel DNA-based diagnostic assay for CNS infections, (2) a commercially available multiplex PCR-based meningitis/encephalitis (ME) panel for clinical use in a facility-limited laboratory setting, (3) proteomics profiling of blood from children with severe CNS infection as compared to outpatient controls with fever yet not severely ill, and (4) Myxovirus resistance protein A (MxA) as a biomarker in blood for viral CNS infection. Further changes in the etiology of childhood CNS infections after the introduction of the pneumococcal conjugate vaccine against Streptococcus pneumoniae will be investigated. In addition, the carriage and invasive rate of Neisseria meningitidis will be recorded and serotyped, and the expression of its major virulence factor (polysaccharide capsule) will be investigated. METHODS: The PI-POC trial is a prospective observational study of children including newborns up to 12 years of age with clinical features of CNS infection, and age-/sex-matched outpatient controls with fever yet not severely ill. Participants are recruited at 2 Pediatric clinics in Mbarara, Uganda. Cerebrospinal fluid (for cases only), blood, and nasopharyngeal (NP) swabs (for both cases and controls) sampled at both clinics are analyzed at the Epicentre Research Laboratory through gold-standard methods for CNS infection diagnosis (microscopy, biochemistry, and culture) and a commercially available ME panel for multiplex PCR analyses of the cerebrospinal fluid. An additional blood sample from cases is collected on day 3 after admission. After initial clinical analyses in Mbarara, samples will be transported to Stockholm, Sweden for (1) validation analyses of a novel nucleic acid-based POC test, (2) biomarker research, and (3) serotyping and molecular characterization of S. pneumoniae and N. meningitidis. RESULTS: A pilot study was performed from January to April 2019. The PI-POC trial enrollment of patients begun in April 2019 and will continue until September 2020, to include up to 300 cases and controls. Preliminary results from the PI-POC study are expected by the end of 2020. CONCLUSIONS: The findings from the PI-POC study can potentially facilitate rapid etiological diagnosis of CNS infections in low-resource settings and allow for novel methods for determination of the severity of CNS infection in such environment. TRIAL REGISTRATION: ClinicalTrials.gov NCT03900091; https://clinicaltrials.gov/ct2/show/NCT03900091. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21430.

Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema.

Anti-vascular endothelial growth factor (VEGF) agents are widely regarded as the first line of therapy for diabetic macular edema (DME) but are not universally effective. An automatic method that can predict whether a patient is likely to respond to anti-VEGF therapy can avoid unnecessary trial and error treatment strategies and promote the selection of more effective first-line therapies. The objective of this study is to automatically predict the efficacy of anti-VEGF treatment of DME in individual patients based on optical coherence tomography (OCT) images. We performed a retrospective study of 127 subjects treated for DME with three consecutive injections of anti-VEGF agents. Patients' retinas were imaged using spectral-domain OCT (SD-OCT) before and after anti-VEGF therapy, and the total retinal thicknesses before and after treatment were extracted from OCT B-scans. A novel deep convolutional neural network was designed and evaluated using pre-treatment OCT scans as input and differential retinal thickness as output, with 5-fold cross-validation. The group of patients responsive to anti-VEGF treatment was defined as those with at least a 10% reduction in retinal thickness following treatment. The predictive performance of the system was evaluated by calculating the precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The algorithm achieved an average AUC of 0.866 in discriminating responsive from non-responsive patients, with an average precision, sensitivity, and specificity of 85.5%, 80.1%, and 85.0%, respectively. Classification precision was significantly higher when differentiating between very responsive and very unresponsive patients. The proposed automatic algorithm accurately predicts the response to anti-VEGF treatment in DME patients based on OCT images. This pilot study is a critical step toward using non-invasive imaging and automated analysis to select the most effective therapy for a patient's specific disease condition.

Convolutional Mixture of Experts Model: A Comparative Study on Automatic Macular Diagnosis in Retinal Optical Coherence Tomography Imaging.

BACKGROUND: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals for researchers in the field. METHODS: This study is designed in order to present a comparative analysis on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (ME-CNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. For this research study, the models were evaluated on a database of three different macular OCT sets. Two first OCT sets were acquired by Heidelberg imaging systems consisting of 148 and 45 subjects respectively and set3 was constituted of 384 Bioptigen OCT acquisitions. To provide better performance insight into the CMoE ensembles, we extensively analyzed the models based on the 5-fold cross-validation method and various classification measures such as precision and average area under the ROC curve (AUC). RESULTS: Experimental evaluations showed that the MCME and WCME outperformed the ME-CNN model and presented overall precisions of 98.14% and 96.06% for aligned OCTs respectively. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. CONCLUSION: Based on the comparative analysis, although the MCME model outperformed the other CMoE models in the analysis of aligned retinal OCTs, the WCME offers a robust model for diagnosis of non-aligned retinal OCTs. This allows having a fast and robust computer-aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal layers alignment.

Introducing a New Algorithm for Classification of Etiology in Studies on Pediatric Pneumonia: Protocol for the Trial of Respiratory Infections in Children for Enhanced Diagnostics Study.

BACKGROUND: There is a need to better distinguish viral infections from antibiotic-requiring bacterial infections in children presenting with clinical community-acquired pneumonia (CAP) to assist health care workers in decision making and to improve the rational use of antibiotics. OBJECTIVE: The overall aim of the Trial of Respiratory infections in children for ENhanced Diagnostics (TREND) study is to improve the differential diagnosis of bacterial and viral etiologies in children aged below 5 years with clinical CAP, by evaluating myxovirus resistance protein A (MxA) as a biomarker for viral CAP and by evaluating an existing (multianalyte point-of-care antigen detection test system [mariPOC respi] ArcDia International Oy Ltd.) and a potential future point-of-care test for respiratory pathogens. METHODS: Children aged 1 to 59 months with clinical CAP as well as healthy, hospital-based, asymptomatic controls will be included at a pediatric emergency hospital in Stockholm, Sweden. Blood (analyzed for MxA and C-reactive protein) and nasopharyngeal samples (analyzed with real-time polymerase chain reaction as the gold standard and antigen-based mariPOC respi test as well as saved for future analyses of a novel recombinase polymerase amplification-based point-of-care test for respiratory pathogens) will be collected. A newly developed algorithm for the classification of CAP etiology will be used as the reference standard. RESULTS: A pilot study was performed from June to August 2017. The enrollment of study subjects started in November 2017. Results are expected by the end of 2019. CONCLUSIONS: The findings from the TREND study can be an important step to improve the management of children with clinical CAP. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12705.

Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble.

Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a new cost function for discriminative and fast learning of image features by applying convolutional neural networks on multiple-scale sub-images. MCME maximizes the likelihood function of the training data set and ground truth by considering a mixture model, which tries also to model the joint interaction between individual experts by using a correlated multivariate component for each expert module instead of only modeling the marginal distributions by independent Gaussian components. Two different macular OCT data sets from Heidelberg devices were considered for the evaluation of the method, i.e., a local data set of OCT images of 148 subjects and a public data set of 45 OCT acquisitions. For comparison purpose, we performed a wide range of classification measures to compare the results with the best configurations of the MCME method. With the MCME model of four scale-dependent experts, the precision rate of 98.86%, and the area under the receiver operating characteristic curve (AUC) of 0.9985 were obtained on average.

A vertical flow paper-microarray assay with isothermal DNA amplification for detection of Neisseria meningitidis.

Paper-based biosensors offer a promising technology to be used at the point of care, enabled by good performance, convenience and low-cost. In this article, we describe a colorimetric vertical-flow DNA microarray (DNA-VFM) that takes advantage of the screening capability of DNA microarrays in a paper format together with isothermal amplification by means of Recombinase Polymerase Amplification (RPA). Different assay parameters such as hybridization buffer, flow rate, printing buffer and capture probe concentration were optimized. A limit of detection (LOD) of 4.4 nM was achieved as determined by tabletop scanning. The DNA-VFM was applied as a proof of concept for detection of Neisseria meningitidis, a primary cause of bacterial meningitis. The LOD was determined to be between 38 and 2.1 × 106 copies/VFMassay, depending on the choice of DNA capture probes. The presented approach provides multiplex capabilities of DNA microarrays in a paper-based format for future point-of-care applications.

Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.

The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

Health care workers' perceptions of point-of-care testing in a low-income country-A qualitative study in Southwestern Uganda.

BACKGROUND: Point-of-care (POC) tests have become increasingly available and more widely used in recent years. They have been of particular importance to low-income settings, enabling them with clinical capacities that had previously been limited. POC testing programs hold a great potential for significant improvement in low-income health systems. However, as most POC tests are developed in high-income countries, disengagement between developers and end-users inhibit their full potential. This study explores perceptions of POC test end-users in a low-income setting, aiming to support the development of novel POC tests for low-income countries. METHODS: A qualitative study was conducted in Mbarara District, Southwestern Uganda, in October 2014. Fifty health care workers were included in seven focus groups, comprising midwives, laboratory technicians, clinical and medical officers, junior and senior nurses, and medical doctors. Discussions were audio-recorded and transcribed verbatim. Transcripts were coded through a data-driven approach for qualitative content analysis. RESULTS: Nineteen different POC tests were identified as currently being in use. While participants displayed being widely accustomed to and appreciative of the use of POC tests, they also assessed the use and characteristics of current tests as imperfect. An ideal POC test was characterized as being adapted to local conditions, thoughtfully implemented in the specific health system, and capable of improving the care of patients. Tests for specific medical conditions were requested. Opinions differed with regard to the ideal distribution of POC tests in the local health system. CONCLUSION: POC tests are commonly used and greatly appreciated in this study setting. However, there are dissatisfactions with current POC tests and their use. To maximize benefit, stakeholders need to include end-user perspectives in the development and implementation of POC tests. Insights from this study will influence our ongoing efforts to develop POC tests that will be particularly usable in low-income settings.