Specificity Measures Research Articles

Diagnostic Accuracy of Detecting Diabetic Retinopathy by Using Digital Fundus Photographs in the Peripheral Health Facilities of Bangladesh: Validation Study.

BackgroundDiabetic retinopathy can cause blindness even in the absence of symptoms. Although routine eye screening remains the mainstay of diabetic retinopathy treatment and it can prevent 95% of blindness, this screening is not available in many low- and middle-income countries even though these countries contribute to 75% of the global diabetic retinopathy burden.ObjectiveThe aim of this study was to assess the diagnostic accuracy of diabetic retinopathy screening done by non-ophthalmologists using 2 different digital fundus cameras and to assess the risk factors for the occurrence of diabetic retinopathy.MethodsThis validation study was conducted in 6 peripheral health facilities in Bangladesh from July 2017 to June 2018. A double-blinded diagnostic approach was used to test the accuracy of the diabetic retinopathy screening done by non-ophthalmologists against the gold standard diagnosis by ophthalmology-trained eye consultants. Retinal images were taken by using either a desk-based camera or a hand-held camera following pupil dilatation. Test accuracy was assessed using measures of sensitivity, specificity, and positive and negative predictive values. Overall agreement with the gold standard test was reported using the Cohen kappa statistic (κ) and area under the receiver operating curve (AUROC). Risk factors for diabetic retinopathy occurrence were assessed using binary logistic regression.ResultsIn 1455 patients with diabetes, the overall sensitivity to detect any form of diabetic retinopathy by non-ophthalmologists was 86.6% (483/558, 95% CI 83.5%-89.3%) and the specificity was 78.6% (705/897, 95% CI 75.8%-81.2%). The accuracy of the correct classification was excellent with a desk-based camera (AUROC 0.901, 95% CI 0.88-0.92) and fair with a hand-held camera (AUROC 0.710, 95% CI 0.67-0.74). Out of the 3 non-ophthalmologist categories, registered nurses and paramedics had strong agreement with kappa values of 0.70 and 0.85 in the diabetic retinopathy assessment, respectively, whereas the nonclinical trained staff had weak agreement (κ=0.35). The odds of having retinopathy increased with the duration of diabetes measured in 5-year intervals (P<.001); the odds of having retinopathy in patients with diabetes for 5-10 years (odds ratio [OR] 1.81, 95% CI 1.37-2.41) and more than 10 years (OR 3.88, 95% CI 2.91-5.15) were greater than that in patients with diabetes for less than 5 years. Obesity was found to have a negative association (P=.04) with diabetic retinopathy.ConclusionsDigital fundus photography is an effective screening tool with acceptable diagnostic accuracy. Our findings suggest that diabetic retinopathy screening can be accurately performed by health care personnel other than eye consultants. People with more than 5 years of diabetes should receive priority in any community-level retinopathy screening program. In a country like Bangladesh where no diabetic retinopathy screening services exist, the use of hand-held cameras can be considered as a cost-effective option for potential system-wide implementation.

67702 At-Home Screening Tool for Anosmia

ABSTRACT IMPACT: By developing and validating a simple and cost-effective at-home screening tool for loss of smell, we can efficiently detect infection with COVID-19, neuropsychiatric disease such as Alzheimer’s, and post-operative smell loss. OBJECTIVES/GOALS: To develop and validate a feasible and cost-effective screening tool for olfactory dysfunction (OD) using common household items. METHODS/STUDY POPULATION: The study has two phases. In the Development phase, 120 participants with self-reported smell changes will complete a survey with a list of 45 household items to smell. Item reduction to develop the NASAL Short Smell Test will occur by measuring content validity, factor analysis, and internal consistency. In the Validation phase, 200 participants with self-reported smell changes will take the NASAL Short Smell Test at baseline and again at three weeks. In both phases, the validated University of Pennsylvania Smell Identification Test (UPSIT) will be used as the gold standard. Measures of performance as well as test-retest reliability and sensitivity to change will be measured. RESULTS/ANTICIPATED RESULTS: We anticipate that the majority of participants will have at least half of the items in their household and will report ability to smell for each. Measures of sensitivity, specificity, likelihood ratios, and UPSIT score correlations will allow us to evaluate performance of each item. Item reduction will allow us to create the NASAL Short Smell Test, in which a handful of common items will be used to create a screening tool for smell loss. The Validation phase will allow us to measure discriminative performance of this tool as well as test-retest reliability and sensitivity to change, which we expect to be at least comparable to the validated UPSIT. DISCUSSION/SIGNIFICANCE OF FINDINGS: Current tools for diagnosis of OD are costly, time-consuming, and often require a clinician to evaluate. The validation of the simple at-home NASAL Short Smell Test to screen for OD will allow us to detect infection with COVID-19, neuropsychiatric disease, or post-operative smell loss quickly and efficiently.

A diagnostic evaluation of single screen testing for malaria in the returning traveler: A large retrospective cohort study.

Screening for malaria in the returning traveler has often required repeat testing; however, audit data suggest that patients have not been reattending. We sought to ascertain if this was safe by examining the diagnostic efficacy of a single screen consisting of a rapid diagnostic test (RDT) and a thin film. We conducted a retrospective cohort study of patients with suspected malaria who attended in the past 5years from two large teaching hospitals. We assessed the diagnostic accuracy of a single screen, reporting measures of sensitivity and specificity. To establish a reference standard, we cross-linked data with the national malaria registry held at Public Health England and regional centers. The cohort consisted of 1365 patients, of whom 33 opted out of the research and one did not have a complete initial screen. Of those 1331 screens there were 74 cases of Plasmodium falciparum (prevalence of 5.6%) and 104 of any malaria species (prevalence of 7.8%). Sensitivity for the detection of P.falciparum was 100.00% (95% confidence interval [CI]= 95.1 to 100), with a specificity of 99.4% (95% CI= 98.9 to 99.8). For the detection of any species of malaria the sensitivity was slightly lower due to the presence of one false negative; sensitivity was 99.0% (95% CI= 94.8 to 100) and specificity was 99.5% (95% CI= 98.9 to 99.8). A single thin film and RDT is likely to be sufficient as a first screen for falciparum malaria in the returning traveler with important caveats. For those sent home from emergency departments, appropriate safety netting must be provided. Further prospective study is required to investigate this approach.

Spatiotemporally explicit earthquake prediction using deep neural network

Due to the complexity of predicting future earthquakes, machine learning algorithms have been used by several researchers to increase the Accuracy of the forecast. However, the concentration of previous studies has chiefly been on the temporal rather than spatial parameters. Additionally, the less correlated variables were typically eliminated in the feature analysis and did not enter the model. This study introduces and investigates the effect of spatial parameters on four ML algorithms' performance for predicting the magnitude of future earthquakes in Iran as one of the most earthquake-prone countries in the world. We compared the performances of conventional methods of Support Vector Machine (SVM), Decision Tree (DT), and a Shallow Neural Network (SNN) with the contemporary Deep Neural Network (DNN) method for predicting the magnitude of the biggest upcoming earthquake in the next week. Information Gain analysis, Accuracy, Sensitivity, Positive Predictive Value, Negative Predictive Value, and Specificity measures were exploited to investigate the outcome of using a new parameter, called Fault Density, calculated using Kernel Density Estimation and Bivariate Moran's I, on the performance of the earthquake prediction, in comparison to other commonly used parameters. We discussed the behavior of the four models while dealing with different combinations of parameters and different classes of earthquake magnitudes. The results showed promising performance of the proposed parameter for the earthquakes of high magnitudes, especially using SVM and DNN models.

A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.

Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image processing techniques has been a more reliable interpretation in the illustration of breast distortion. In this study, a fully process-integrated approach with developing a CAD system is presented for the detection of breast masses based on texture description, spectral clustering, and Support Vector Machine (SVM). To this end, breast Regions of Interest (ROIs) are automatically detected from digital mammograms via gray-scale enhancement and data cleansing. The ROIs are segmented as labeled multi-sectional patterns using spectral clustering by the means of intensity descriptors relying on the region's histogram and texture descriptors based on the Gray Level Co-occurrence Matrix (GLCM). In the next step, shape and probabilistic features are derived from the segmented sections and given to the Genetic Algorithm (GA) to do the feature selection. The optimal feature vector comprising a fusion of selected shape and probabilistic features is submitted to linear kernel SVM for robust and reliable classification of mass tissues from the non-mass. Linear discrimination analysis (LDA) is also performed to ascertain the significance of the nominated feature space. The classification results of the proposed approach are presented by sensitivity, specificity, and accuracy measures, which are 89.5%, 91.2%, and 90%, respectively.

&lt;p class="Body"&gt;&lt;strong&gt;Integrating a morphological description with DNA barcode data of a new species of the genus &lt;em&gt;Pimeliaphilus &lt;/em&gt;(Acariformes: Pterygosomatidae) with the analysis of its host specificity and a key to the genus&lt;/strong&gt;&lt;/p&gt;

Pimeliaphilus hemidactyli sp. nov., a new species of the mite genus Pimeliaphilus Trägårdh (Acariformes: Pterygosomatidae), is described from four host species of Hemidactylus: H. murrayi Gleadow (type host), H. frenatus Duméril &amp; Bibron, H. parvimaculatus Deraniyagala and H. leschenaultii Duméril &amp; Bibron (Sauria: Gekkonidae) from India. P. hemidactyli sp. nov. is the most similar to P. sharifi Abdussalam, 1941 and P. insignis (Berlese, 1892); this species can be distinguished from P. sharifi by the presence of 11 dorsal setae on the idiosoma, the absence of leg setae v”GII, v”TrIII and the presence of v’TrIV, and from P. sharifi by the presence of 11 dorsal setae on idiosoma, 1 pair of genital setae and the absence of coxal setae 4c. The standard morphological description of the new species is supplemented with sequencing data for cytochrome c oxidase subunit I (COI) and nuclear ribosomal genes: 18S rDNA and hypervariable region D2 of nuclear 28S rRNA. The present study also addresses specialisation amongst Pimeliaphilus hemidactyli sp. nov. using two measures of host specificity: (1) the number of host species used by the mites species at the study area; and (2) a measure that considers the taxonomic distinctness of the hosts used by the mite, weighted for its prevalence in the different hosts. The results of this study indicate that this species is highly host specific (a stenoxenous species) and that, in the presence of coexisting gecko species, it prefers Murray’s House gecko (H. murrayi) as a host (prevalence of 41%). A historical review of the genus is presented and a key to the genus is constructed.

GIS-based multi-criteria decision making and entropy approaches for groundwater potential zones delineation

In arid and semi-arid, due to the lack of surface water, groundwater plays key role in people survival. Therefore, assessing groundwater potential zones is vital to conserve and manage this resource properly. The main object of this study was to compare two methods of AHP and entropy for delineating groundwater potential zone in arid region of Iran. For this regard, withdraw in groundwater level in 16 piezometer wells along with eight GWP conditioning factors of slope degree, stream power index (SPI), landuse, geology; rainfall volume, elevation, soil and lineament density have been considered in the training process while the discharge in the other 16 wells was involved in verifying process of the two models. GWP maps were provided by overlaying all the thematic maps in terms of the obtained weights from AHP and entropy methods. Finally, the models performance was compared through statistical measures of Sensivity, Specificity and True Skill Statistics (TSS). The results show that IOE model (TSS = 0.16) have higher prediction performance than AHP approach (TSS = 0). Therefore, IOE was more promising tools for GWP modeling.

Age- and sex-specific visceral fat reference cutoffs and their association with cardio-metabolic risk

Although excess visceral fat (VAT) is associated with numerous cardio-metabolic risk factors, measurement of this fat depot has historically been difficult. Recent dual X-ray absorptiometry approaches have provided an accessible estimate of VAT that has shown acceptable validity against gold standard methods. The aims of this study were to (i) evaluate DXA measured VAT as a predictor of elevated blood lipids and blood pressure and (ii) calculate thresholds associated with these cardio-metabolic risk factors. The sample comprised 1482 adults (56.4% women) aged 18-66 years. Total body scans were performed using a GE Lunar Prodigy, and VAT analyses were enabled through Corescan software (v 16.0). Blood pressure and blood lipids were measured by standard procedures. Regression models assessed how VAT mass was associated with each cardio-metabolic risk factor compared to other body composition measures. Measures of sensitivity and specificity were used to determine age- and sex-specific cut points for VAT mass associated with high cardio-metabolic risk. Similar to waist circumference, VAT mass was a strong predictor of cardio-metabolic risk especially in men over age 40. Four cut-offs for VAT mass were proposed, above which the cardio-metabolic risk increased: 700 g in women <40 yrs; 800 g in women 40+ yrs; 1000g in men <40 yrs; and 1200 g in men 40+ yrs. In general, these cut-offs discriminated well between those with high and low cardio-metabolic risk. In both sexes, DXA measured VAT was associated with traditional cardio-metabolic risk factors, particularly high blood pressure in those 40+ yrs and low HDL < 40 yrs. These reference values provide a simple, accessible method to assess cardio-metabolic risk in adults.

Freezing of Gait in Parkinson's Disease: Risk Factors, Their Interactions, and Associated Nonmotor Symptoms.

Background Freezing of gait (FOG) is a debilitating and incompletely understood symptom in Parkinson's disease (PD). Objective To determine the principal clinical factors predisposing to FOG in PD, their interactions, and associated nonmotor symptoms. Methods 164 PD subjects were assessed in a cross-sectional retrospective study, using the MDS-UPDRS scale, MMSE, and Clinical Dementia Rating Scale. Clinical factors associated with FOG were determined using univariate analysis and nominal logistic regression. Receiver operating characteristic curves were computed, to obtain measures of sensitivity and specificity of predictors of FOG. Subgroups of patients with FOG were compared with those without FOG, based on defining aspects of their clinical phenotype. Results Relative to non-FOG patients, those with FOG had a longer disease duration, higher PIGD and balance-gait score, higher LED, and more motor complications (p < 0.0001) and were more likely to exhibit urinary dysfunction (p < 0.0003), cognitive impairment, hallucinations, and psychosis (p=0.003). The balance-gait score and motor complications, at their optimum cutoff values, together predicted FOG with 86% accuracy. Interactions were noted between cognitive dysfunction and both the Bal-Gait score and motor complication status, cognitive impairment or dementia increasing the likelihood of FOG in subjects without motor complications (p=0.0009), but not in those with motor complications. Conclusions Both disease and treatment-related factors, notably LED, influence the risk of FOG in PD, with a selective influence of cognitive dysfunction in patients with balance-gait disorder but not in those with motor fluctuations. These findings may help to inform clinical management and highlight distinct subgroups of patients with PD-FOG, which are likely to differ in their network pathophysiology.

Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation

BackgroundIn increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years.MethodsFor the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50–75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell’s optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts.ResultsThe model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model’s prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity.ConclusionsA new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions.

Psychometric Properties of the Obsessive Compulsive Inventory-Child Version in Iranian Clinical and Community Samples.

This study evaluated the psychometric properties of the Persian version of the Obsessive-Compulsive Inventory-Child Version (OCI-CV) in clinical and community samples. Factor structure (confirmatory factor analysis), validity (convergent/discriminant, and predictive), and reliability (internal consistency, and 4-week retest) of the Persian version of the OCI-CV were investigated in a sample of 391 children and adolescents 7-17years comprised of two groups: a clinical sample of youth with OCD (n = 62), and a community sample (n = 329). Participants completed the OCI-CV, Multidimensional Anxiety Scale for Children (MASC), Children's Depression Inventory (CDI), and Child Behavior Checklist (CBCL). Similar to the English version of the OCI-CV, the Persian version of the scale demonstrated a stable six-factor structure, good convergent and discriminant validity through its correlations with other specific measures of pediatric psychopathology, acceptable sensitivity and specificity for the detection of OCD, and good reliability in terms of internal consistency and temporal stability. These findings suggest that the OCI-CV is a valid and reliable measure to assess obsessive-compulsive symptom dimensions in Iranian youth. Findings provide cross cultural support on the utility of OCI-CV as a self-report measure of obsessive-compulsive symptomology.

Rapid Intrapartum Test for Maternal Group B Streptococcal Colonisation and its Effect on Antibiotic Use in Labouring Women with Risk Factors for Early Onset Neonatal Infection (GBS2): Cluster Randomised Trial with Nested Test Accuracy Study

Background: We evaluated whether use of point-of-care rapid intrapartum test to detect maternal GBS colonisation reduces maternal antibiotics exposure compared with usual care, where antibiotics are administered due to clinical risk factors. We assessed the accuracy of the rapid test in diagnosing maternal GBS colonisation, against the reference standard of selective enrichment culture. Methods: Parallel-group cluster randomised trial with nested test accuracy study and microbiological sub-study in 20 maternity units in UK randomised to a strategy of rapid test (GeneXpert GBS system, Cepheid) or usual care. The main outcomes were maternal intrapartum antibiotics administered to prevent neonatal early-onset GBS infection, accuracy of the rapid test, and antibiotic resistance profiles in maternal and infant samples. The trial was registered ISRCTN74746075 Findings: 722 mothers (749 babies) participated in rapid test units; 906 mothers (951 babies) in usual care units. There was no evidence of a difference in the primary outcome (Risk ratio 1.16, 95% CI 0.83 to 1.64) between the rapid test (41%, 297/716) and usual care (36%, 328/906) units. The sensitivity and specificity measures of the rapid test were 86% (95% CI 81 to 91%) and 89% (95% CI 85 to 92%) respectively. Interpretation: Use of intrapartum rapid test to diagnose maternal GBS colonisation did not reduce the rates of antibiotics administered for preventing neonatal early-onset GBS infection than usual care, although with considerable uncertainty. The accuracy of the rapid test is within acceptable limits. Trial Registration: ISRCTN74746075 Funding: National Institute for Health Research Health Technology Assessment programme (13/82/04). Declaration of Interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf. JP is the Chief Executive of Group B Strep Support, a Charity working to stop GBS in babies. JP received support from Cepheid to attend an academic conference in 2016, from Pfizer to attend a summit for vaccine advocacy stakeholders in the USA in 2019 and from i-CONSENT to attend workshops in London in 2018 and Brussels in 2019 regarding consent for vaccine trials. JP was a member of the Department of Health research prioritisation expert group in 2016. JP and JG are members of the NICE guideline update committee for Neonatal infection: antibiotics for prevention and treatment (update). JG was a member of the NICE Diagnostics Advisory Committee that produced DG38 Rapid tests for group A streptococcal infections in people with a sore throat (which included assessment of the Cepheid GeneXpert Xpert® Xpress Strep A test) (Published November 2019). JPD, JP, JG are grant holders for HTA 17/86/06 (GBS3), a cluster randomised trial of routine screening for GBS. JPD and JP are grant applicants for a study to determine a serocorrelate of immune protection against GBS. All other authors declare no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. JJD is supported by the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. Ethics Approval Statement: This study was approved by the West Midlands – Edgbaston Multi centre Research Ethics Committee (16/WM/0036), including waiver of individual research consent.

Inter‐cohort staging efficacy of gaussian process progression model for Alzheimer’s disease

AbstractBackgroundUnderstanding the path that leads from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) provides insights into dementia pathophysiology and can inform patient stratification in clinical trials. Our objective is to train statistical computational models for estimating an average disease trajectory to better understand how patients evolve on the basis of biomarkers of from the ADNI population, then validate the model on MCI subjects from the PharmaCog cohort.MethodThe disease trajectory was built via a Gaussian Process Progression Model (GPPM) (Lorenzi et al. 2017, DOI: 10.1016/j.neuroimage.2017.08.059) trained on longitudinal biomarker data from 338 subjects in the ADNI database who had converted from MCI to AD at the time of writing. Biomarkers related to cognitive scores, cerebrospinal fluid and T13D magnetic resonance imaging were used to build the disease model. Validation was performed by staging 139 MCI subjects from the PharmaCog database, 20 of whom progressed to AD, along the progression model timeline. Only baseline cross sectional measures of biomarkers were used to stage PharmaCog subjects in order to simulate clinical settings. Measures of sensitivity, specificity, balanced accuracy and area under curve (AUC) were used to measure the staging performance.ResultThe staging of PharmaCog subjects shows a clear separation between MCI stable (sMCI) subjects and MCI progressors (pMCI) on the disease timeline. On average, sMCIs were staged at year 72.7±3.6 while pMCIs are staged 3 years later (p‐value&lt;0.01) at year 75.5±2.8. Classification of sMCI vs pMCI subjects returned a sensitivity equal to 0.68, specificity equal to 0.85, balanced accuracy equal to 0.77 and the ROC curve (Figure 1) had AUC equal to 0.75.ConclusionWe identified MCI converters to AD in a clinical data set (PharmaCog) using a data‐driven computational model of AD progression trained on research data (ADNI). Classification performance was comparable to other data‐driven tools (Young et al., 2014, DOI: 10.1093/brain/awu176), but performed in a more challenging clinical setting. This validates the staging efficacy of such models and shows their utility for future application in healthcare and clinical trials.

Evaluation of binary diagnostic tests accuracy for medical researches

Abstract Objectives The aim of this study is to introduce the features of diagnostic tests. In addition, it will be demonstrated which performance measures can be used for diagnostic tests with binary results, the properties of these measures and how to interpret them. Materials and Methods The evaluation of the diagnostic test performance measures may differ depending on whether the test result is numerical or binary. When the diagnostic test result is continuous numerical data, ROC analysis is often utilized. The performance of a diagnostic test with binary results are usually evaluated using the measures of sensitivity and specificity. However, there are some important measures other than these two measures for binary test results. These measures are predictive values, overall accuracy, diagnostic odds ratio, Youden index, and likelihood ratios. Results A hypothetical data has been produced based on the studies conducted on the performance of rapid tests (Specific IgM/IgG) according to the RT-PCR test for Covid 19 in the literature. An example of a diagnostic test (Specific IgM/IgG) with a binary result is given and all measurements and their confidence interval are obtained for this data. The performance of rapid test was examined and interpreted. Conclusion It is important to design and evaluate the performance of diagnostic/screening tests for health care. In this review, some basic definitions, performance measures that can be used only in evaluating the diagnostic tests with binary results and their confidence intervals are mentioned. Having many different measures provides different interpretations in the evaluation of test performance. Accurately predicting the performance of a diagnostic test depends on many factors. These factors can be study design, criteria of participant selection, sample size calculation, test methods etc. There are guidelines that ensure that all information regarding the conditions under which the study was conducted is in report, in terms of such factors. Therefore, these guidelines are recommended for use of the checklist by many publishers.

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.

The diagnostic performance of CA125 for the detection of ovarian and non-ovarian cancer in primary care: A population-based cohort study.

The serum biomarker cancer antigen 125 (CA125) is widely used as an investigation for possible ovarian cancer in symptomatic women presenting to primary care. However, its diagnostic performance in this setting is unknown. We evaluated the performance of CA125 in primary care for the detection of ovarian and non-ovarian cancers. We studied women in the United Kingdom Clinical Practice Research Datalink with a CA125 test performed between 1 May 2011-31 December 2014. Ovarian and non-ovarian cancers diagnosed in the year following CA125 testing were identified from the cancer registry. Women were categorized by age: <50 years and ≥50 years. Conventional measures of test diagnostic accuracy, including sensitivity, specificity, and positive predictive value, were calculated for the standard CA125 cut-off (≥35 U/ml). The probability of a woman having cancer at each CA125 level between 1-1,000 U/ml was estimated using logistic regression. Cancer probability was also estimated on the basis of CA125 level and age in years using logistic regression. We identified CA125 levels equating to a 3% estimated cancer probability: the "risk threshold" at which the UK National Institute for Health and Care Excellence advocates urgent specialist cancer investigation. A total of 50,780 women underwent CA125 testing; 456 (0.9%) were diagnosed with ovarian cancer and 1,321 (2.6%) with non-ovarian cancer. Of women with a CA125 level ≥35 U/ml, 3.4% aged <50 years and 15.2% aged ≥50 years had ovarian cancer. Of women with a CA125 level ≥35 U/ml who were aged ≥50 years and who did not have ovarian cancer, 20.4% were diagnosed with a non-ovarian cancer. A CA125 value of 53 U/ml equated to a 3% probability of ovarian cancer overall. This varied by age, with a value of 104 U/ml in 40-year-old women and 32 U/ml in 70-year-old women equating to a 3% probability. The main limitations of our study were that we were unable to determine why CA125 tests were performed and that our findings are based solely on UK primary care data, so caution is need in extrapolating them to other healthcare settings. CA125 is a useful test for ovarian cancer detection in primary care, particularly in women ≥50 years old. Clinicians should also consider non-ovarian cancers in women with high CA125 levels, especially if ovarian cancer has been excluded, in order to prevent diagnostic delay. Our results enable clinicians and patients to determine the estimated probability of ovarian cancer and all cancers at any CA125 level and age, which can be used to guide individual decisions on the need for further investigation or referral.

Classification of stroke using neural networks in electrical impedance tomography

Electrical impedance tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting voltages at the skin, and recovering the internal conductivity distribution. The mathematical task of EIT image reconstruction is a nonlinear and ill-posed inverse problem. Therefore any EIT image reconstruction method needs to be regularized, typically resulting in blurred images. One promising application is stroke-EIT, or classification of stroke into either ischemic or hemorrhagic. Ischemic stroke involves a blood clot, preventing blood flow to a part of the brain causing a low-conductivity region. Hemorrhagic stroke means bleeding in the brain causing a high-conductivity region. In both cases the symptoms are identical, so a cost-effective and portable classification device is needed. Typical EIT images are not optimal for stroke-EIT because of blurriness. This paper explores the possibilities of machine learning in improving the classification results. Two paradigms are compared: (a) learning from the EIT data, that is Dirichlet-to-Neumann maps and (b) extracting robust features from data and learning from them. The features of choice are virtual hybrid edge detection (VHED) functions (Greenleaf et al 2018 Anal. PDE 11) that have a geometric interpretation and whose computation from EIT data does not involve calculating a full image of the conductivity. We report the measures of accuracy, sensitivity and specificity of the networks trained with EIT data and VHED functions separately. Computational evidence based on simulated noisy EIT data suggests that the regularized grey-box paradigm (b) leads to significantly better classification results than the black-box paradigm (a).

Comparison of a traditional systematic review approach with review-of-reviews and semi-automation as strategies to update the evidence

BackgroundThe exponential growth of the biomedical literature necessitates investigating strategies to reduce systematic reviewer burden while maintaining the high standards of systematic review validity and comprehensiveness.MethodsWe compared the traditional systematic review screening process with (1) a review-of-reviews (ROR) screening approach and (2) a semi-automation screening approach using two publicly available tools (RobotAnalyst and AbstrackR) and different types of training sets (randomly selected citations subjected to dual-review at the title-abstract stage, highly curated citations dually reviewed at the full-text stage, and a combination of the two). We evaluated performance measures of sensitivity, specificity, missed citations, and workload burdenResultsThe ROR approach for treatments of early-stage prostate cancer had a poor sensitivity (0.54) and studies missed by the ROR approach tended to be of head-to-head comparisons of active treatments, observational studies, and outcomes of physical harms and quality of life. Title and abstract screening incorporating semi-automation only resulted in a sensitivity of 100% at high levels of reviewer burden (review of 99% of citations). A highly curated, smaller-sized, training set (n = 125) performed similarly to a larger training set of random citations (n = 938).ConclusionTwo approaches to rapidly update SRs—review-of-reviews and semi-automation—failed to demonstrate reduced workload burden while maintaining an acceptable level of sensitivity. We suggest careful evaluation of the ROR approach through comparison of inclusion criteria and targeted searches to fill evidence gaps as well as further research of semi-automation use, including more study of highly curated training sets.

Use of the ABC/2 Method to Select Patients for Thrombectomy After 6 Hours of Symptom Onset.

To determine whether the ABC/2 method could accurately and reliably measure infarct volume and guide thrombectomy decision in acute stroke cases presenting with late or unknown onset. Four physicians who routinely use MRI for acute stroke imaging, blinded to the RAPID results, measured the diffusion-weighted imaging (DWI) infarct volume using the ABC/2 method. Measurements with ABC/2 (the index test) were compared with RAPID (the reference standard) to calculate sensitivity, specificity, and accuracy measures for various volume cutpoints. Thrombectomy decisions based on RAPID and raters' measurements using the criteria from the Diffusion-Weighted Imaging or Computerized Tomography Perfusion Assessment With Clinical Mismatch in the Triage of Wake-Up and Late-Presenting Strokes Undergoing Neurointervention With Trevo (DAWN) trial criteria were compared. Interrater and intrarater agreement was measured using kappa statistics. Accuracy with the ABC/2 method was greater than 80% for each rater and each volume cut point. Interrater and intrarater agreement was substantial to excellent for each volume cut point. Treatment decisions with ABC/2 volume estimations showed strong interrater and intrarater agreement, and led to similar thrombectomy decisions compared with RAPID in more than 85% of cases. DWI infarct volume measurement using ABC/2 method shows strong accuracy and reliability and may be an acceptable alternative to RAPID software for the application of DAWN criteria for thrombectomy decision-making.

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set was collected between February and October 2018. The comparison study was preformed between January and October 2019. Data were analyzed from October to February 2020. After the first round of reviews, further analysis of the data was performed from March to July 2020. The learning performance of the AI algorithm was judged using the conventional ROC curve and the area under the curve (AUC) during training and field testing on the study data set. For the AI algorithm and radiology residents, the individual finding label performance was measured using the conventional measures of label-based sensitivity, specificity, and positive predictive value (PPV). In addition, the agreement with the ground truth on the assignment of findings to images was measured using the pooled κ statistic. The preliminary read performance was recorded for AI algorithm and radiology residents using new measures of mean image-based sensitivity, specificity, and PPV designed for recording the fraction of misses and overcalls on a per image basis. The 1-sided analysis of variance test was used to compare the means of each group (AI algorithm vs radiology residents) using the F distribution, and the null hypothesis was that the groups would have similar means. The trained AI algorithm achieved a mean AUC across labels of 0.807 (weighted mean AUC, 0.841) after training. On the study data set, which had a different prevalence distribution, the mean AUC achieved was 0.772 (weighted mean AUC, 0.865). The interrater agreement with ground truth finding labels for AI algorithm predictions had pooled κ value of 0.544, and the pooled κ for radiology residents was 0.585. For the preliminary read performance, the analysis of variance test was used to compare the distributions of AI algorithm and radiology residents' mean image-based sensitivity, PPV, and specificity. The mean image-based sensitivity for AI algorithm was 0.716 (95% CI, 0.704-0.729) and for radiology residents was 0.720 (95% CI, 0.709-0.732) (P = .66), while the PPV was 0.730 (95% CI, 0.718-0.742) for the AI algorithm and 0.682 (95% CI, 0.670-0.694) for the radiology residents (P < .001), and specificity was 0.980 (95% CI, 0.980-0.981) for the AI algorithm and 0.973 (95% CI, 0.971-0.974) for the radiology residents (P < .001). These findings suggest that it is possible to build AI algorithms that reach and exceed the mean level of performance of third-year radiology residents for full-fledged preliminary read of AP frontal chest radiographs. This diagnostic study also found that while the more complex findings would still benefit from expert overreads, the performance of AI algorithms was associated with the amount of data available for training rather than the level of difficulty of interpretation of the finding. Integrating such AI systems in radiology workflows for preliminary interpretations has the potential to expedite existing radiology workflows and address resource scarcity while improving overall accuracy and reducing the cost of care.