Health issues in a Bangalore slum: findings from a household survey using a mobile screening toolkit in Devarajeevanahalli.

BACKGROUND: Slums are home to nearly one billion people in the world and are expanding at an exponential rate. Devarjeevanahalli is a large notified slum in Bangalore, South India which is characterised by poverty, overcrowding, hazardous living environment and social complexities. The poor living conditions not only affect the health of the people living there but also poses distinctive challenges to conducting health surveys. The purpose of this paper is to report the findings of a household survey that was done to study the health condition of people living in a slum. METHODS: A community-based cross-sectional survey was designed to determine the prevalence of health conditions using a mobile screening toolkit-THULSI (Toolkit for Healthy Urban Life in Slums Initiative). Devarjeevanahalli slum was chosen purposively as it is fairly representative of any slum in a big city in India. Sample size was calculated as 1100 households and demographic parameters at the household level and parameters related to priority health conditions (hypertension, diabetes mellitus, anaemia and malnutrition) at the individual level were studied. Six zones within the slum were purposively selected and all the contiguous households were selected. The last of the six zones was partially surveyed as the desired sample size was achieved. RESULTS: A total of 1186 households were surveyed and 3693 people were screened. More than three fourth (70.4%) of the population were below poverty line. Only one third had a regular job and the average daily income was 5.3$ and 2.6$ in men and women respectively. The prevalence of hypertension (35.5%), diabetes (16.6%) and anaemia (70.9%) was high in the screened slum population. Most of the people (56.5% of hypertensives and 34.4% diabetics) were screened for the first time. Almost half of the children under the age of five years were stunted. CONCLUSIONS: Poor income security and huge burden of health issues were reported among adults and children in the household health screening in a large notified slum in South India. Most people were unaware of their disease condition prior to the screening. Relatively simple technological solutions enabled the local health team to screen the slum population despite many challenges.

Barriers to cervical cancer and breast cancer screening uptake in low- and middle-income countries: a systematic review.

There is an alarmingly high growth in breast and cervical cancers in low- and middle-income countries. Due to late presentation to doctors, there is a lower cure rate. The screening programmes in low- and middle-income countries are not comprehensive. In this paper, we systematically analyse the barriers to screening through an accessibility framework. We performed a systematic literature search in PubMed, Mendeley and Google Scholar to retrieve all English language studies (quantitative, qualitative and mixed-methods) that contained information on breast and cervical cancer screening in low- and middle-income countries. We only considered publications published between 1 January 2016 and 31 May 2021. The review was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension (PRISMA-S), an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. The search yielded a total of 67 articles from low- and middle-income countries in this review. We used a framework on accessibility known as the 5A framework, which distinguishes five aspects of access: approachability, acceptability, availability, affordability and appropriateness, to classify the screening barriers. We added two more aspects: awareness and angst, as they could explain other important barriers to screening. They confirmed how the lack of awareness, cost of the screening service and distance to the screening centre act as major impediments to screening. They also revealed how embarrassment and fear of screening and cultural factors such as lack of spousal or family support could be obstacles to screening. We conclude that more needs to be done by policymakers and governments to improve the confidence of the people in the health systems. Women should be made aware of the causes and risk factors of cancer through evidence-based strategies so that there is an increased adherence to screening.

Deep learning model using retinal vascular images for classifying schizophrenia.

Contemporary psychiatric diagnosis still relies on the subjective symptom report of the patient during a clinical interview by a psychiatrist. Given the significant variability in personal reporting and differences in the skill set of psychiatrists, it is desirable to have objective diagnostic markers that could help clinicians differentiate patients from healthy individuals. A few recent studies have reported retinal vascular abnormalities in patients with schizophrenia (SCZ) using retinal fundus images. The goal of this study was to use a trained convolution neural network (CNN) deep learning algorithm to detect SCZ using retinal fundus images. A total of 327 subjects [139 patients with Schizophrenia (SCZ) and 188 Healthy volunteers (HV)] were recruited, and retinal images were acquired using a fundus camera. The images were preprocessed and fed to a convolution neural network for the classification. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The CNN achieved an accuracy of 95% for classifying SCZ and HV with an AUC of 0.98. Findings from the current study suggest the potential utility of deep learning to classify patients with SCZ and assist clinicians in clinical settings. Future studies need to examine the utility of the deep learning model with retinal vascular images as biomarkers in schizophrenia with larger sample sizes.

Association between retinal vascular measures and brain white matter lesions in schizophrenia.

OBJECTIVE: Recent studies have examined retinal vascular abnormalities in schizophrenia as retinal vascular imaging is a non-invasive proxy to cerebral microvasculature. However, relation between retinal vascular abnormalities and brain structure is not well examined in schizophrenia. Hence in this study, for the first time, we examined the relationship between retinal vascular measures and brain white matter lesions in schizophrenia. We examined brain white matter lesions as they are considered a predictive marker for future adverse cerebrovascular event. METHODS: We acquired retinal vascular images of both eyes using a non-mydriatic camera and calculated retinal vascular diameter, tortuosity, trajectory and fractal dimension using validated methods. All patients underwent Magnetic Resonance Imaging of bran and we computed white matter hypo-intensities using Freesurfer software. We performed a linear regression analysis to examine the relationship between white matter hypo-intensities and retinal vascular measures controlling for age, sex, fasting blood sugar, creatinine, whole-brain volume, and antipsychotic dose. RESULTS: The regression model was significant in Schizophrenia patients (R=0.983;R2 =0.966;-F=10.849;p = 0.008) but not in healthy volunteers (R=0.828;R2 =0.686;F=0.182; p = 0.963). Among the retinal vascular measures, arterial tortuosity (ß = 0.963;p-0.002), tortuosity (ß = -1.002;p = 0.001) and fractal dimension (ß = -0.688;p = 0.014) were significant predictors of white matter lesions. DISCUSSION: The current study's findings support the conclusion that retinal vascular fractal dimension and tortuosity are associated with changes in cerebral white matter and may be considered proxy markers for cerebral microvasculature in schizophrenia. Considering the relationship between white matter lesions and stroke, these observations could have important clinical implications to screen schizophrenia patients for risk of adverse cerebrovascular event.

Association between retinal vascular caliber and brain structure in schizophrenia.

OBJECTIVE: Several lines of research in the last decade have indicated the potential utility of retina as a window to the brain. Emerging evidence suggests abnormalities in retinal vascular caliber in schizophrenia. However, the relationship between retinal vascular measures and brain structure has not been examined in schizophrenia to date. Hence, we examined the relationship between retinal vasculature measured using fundus photography and brain structure measured using magnetic resonance imaging. METHOD: We recruited 17 healthy volunteers and 20 patients with schizophrenia. Using a non-mydriatic camera, we captured the images for left and right eyes separately and retinal vascular calibers were calculated using a semi-automated software package. Whole-brain anatomical T1 MPRAGE images were acquired using a 3-Tesla MRI scanner. Whole-brain and regional volume and cortical thickness were calculated using the Freesurfer software package. We used FreeSurfer's QDEC interface to compute vertex-by-vertex for analysis of the volume and cortical thickness. The relation between brain volume, cortical thickness, and retinal vascular caliber was examined using partial correlation and regression analysis. RESULTS: There was a significant negative correlation between average CRVE and global cortical mean thickness in schizophrenia but not in healthy. In schizophrenia patients, there was a significant negative correlation between average CRVE and cortical thickness in frontal regions - left rostral middle frontal, left superior frontal, and right caudal middle frontal gyri and posterior brain regions - left lateral occipital gyrus and left posterior cingulate cortex. DISCUSSION: The findings of the study suggest potential utility of retinal venular diameter as a proxy marker to abnormal neurodevelopment in schizophrenia.

An intelligent real-time scheduler for out-patient clinics: A multi-agent system model.

Scheduling of resources and patients are crucial in outpatient clinics, particularly when the patient demand is high and patient arrivals are random. Generally, outpatient clinic systems are push systems where scheduling is based on average demand prediction and is considered for long term (monthly or bimonthly). Often, planning and actual scenario vary due to uncertainty and variability in demand and this mismatch results in prolonged waiting times and under-utilization of resources. In this article, we model an outpatient clinics as a multi-agent system and propose an intelligent real-time scheduler that schedules patients and resources based on the actual status of departments. Two algorithms are implemented: one for resource scheduling that is based on predictive demand and the other is patient scheduling which performs path optimization depending on the actual status of departments. In order to match resources with stochastic demand, a coordination mechanism is developed that reschedules the resources in the outpatient clinics in real time through auction-bidding procedures. First, a simulation study of intelligent real-time scheduler is carried out followed by implementation of the same in an outpatient clinic of Aravind Eye Hospital, Madurai, India. This hospital has huge patient demand and the patient arrivals are random. The results show that the intelligent real-time scheduler improved the performance measures like waiting time, cycle time, and utilization significantly compared to scheduling of resources and patients in isolation. By scheduling resources and patients, based on system status and demand, the outpatient clinic system becomes a pull system. This scheduler transforms outpatient clinics from open loop system to closed-loop system.

EEG based multi-class seizure type classification using convolutional neural network and transfer learning.

Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal electroencephalogram (EEG) was existing, no attempts have been made towards the classification of variants of seizures. Therefore, this study attempts to classify seven variants of seizures with non-seizure EEG through the application of convolutional neural networks (CNN) and transfer learning by making use of the Temple University Hospital EEG corpus. The objective of our study is to perform a multi-class classification of epileptic seizure type, which includes simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. The 19 channels EEG time series was converted into a spectrogram stack before feeding as input to CNN. The following two different modalities were proposed using CNN: (1) Transfer learning using pretrained network, (2) Extract image features using pretrained network and classify using the support vector machine classifier. The following ten pretrained networks were used to identify the optimal network for the proposed study: Alexnet, Vgg16, Vgg19, Squeezenet, Googlenet, Inceptionv3, Densenet201, Resnet18, Resnet50, and Resnet101. The highest classification accuracy of 82.85% (using Googlenet) and 88.30% (using Inceptionv3) was achieved using transfer learning and extract image features approach respectively. Comparison results showed that CNN based approach outperformed conventional feature and clustering based approaches. It can be concluded that the EEG based classification of seizure type using CNN model could be used in pre-surgical evaluation for treating patients with epilepsy.

Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times.

This study addressed the problem of scheduling walk-in patients in real time. Outpatient clinics encounter uncertainty in patient demand. In addition, the disparate departments are locally (department-centric) organized, leading to prolonged waiting times for patients. The proposed integral patient scheduling model incorporates the status and information of all departments in the outpatient clinic along with all possible pathways to direct patients, on their arrival, to the optimal path. The developed hybrid ant agent algorithm identifies the optimal path to reduce the patient waiting time and cycle time (time from registration to exit). An outpatient clinic in Aravind Eye Hospital, Madurai, has a huge volume of walk-in patients and was selected for this study. The simulation study was performed for diverse scenarios followed by implementation study. The results indicate that integral patient scheduling reduced waiting time significantly. The path optimization in real time makes scheduling effective and efficient as it captures the changes in the outpatient clinic instantly.

Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction.

OBJECTIVE: In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS: Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS: Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS: We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE: To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.

Relation between retinal vascular abnormalities and working memory impairment in patients with schizophrenia and bipolar disorder.

Emerging evidence indicates abnormal retinal micro-vasculature in schizophrenia (SCZ) and bipolar disorder (BD) and its relation to cognitive functions. However, the association of these abnormalities with the cognitive deficits in these disorders has not been examined till date. Hence, we explored this aspect in patients with SCZ, BD, and healthy volunteers (HV). We examined 34 with SCZ, 39 with BD, and 45 HV. Retinal images were acquired using nonmydriatic fundus camera. The retinal images were analyzed, and average diameters of retinal arterioles and venules were calculated. Working memory was assessed using computerized one-back test from Cogstate® battery. There was significant difference between groups in retinal venules and arterioles caliber (p < 0.001). Both SCZ and BD patients had wider venules and narrower arterioles. They had significantly lower working memory accuracy (p = 0.008) and higher log mean speed (p < 0.001). There was significant positive correlation between one-back test accuracy and retinal arteriolar caliber (r = 0.22; p = 0.01) and between log mean speed score and retinal venular caliber (r = 0.20; p = 0.02). Findings suggest association between working memory and retinal vascular caliber, a potential pointer towards understanding the vascular pathology in cognitive deficits in SCZ and BD. Future studies need to examine whether retinal vascular could be a biomarker for SCZ and BD.

A convolutional neural network based framework for classification of seizure types.

Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures based on the characteristics of EEG and other parameters. Till now research has been conducted to classify EEG as seizure and non-seizures, but the classification of seizure types has not been explored. Thus, in this paper, we have proposed the 8-class classification problem in order to classify different seizure types using convolutional neural networks (CNN). This research study suggests a CNN based framework for classification of epileptic seizure types that include simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. EEG time series was converted into spectrogram stacks and used as input for CNN. To the best of authors knowledge, ours is the very first study that classified the seizures types using the computational algorithm. The four CNN models, namely AlexNet, VGG16, VGG19, and basic CNN model was applied to study the performance of 8-class classification problem. The proposed study showed a classification accuracy of 84.06%, 79.71%, 76.81%, and 82.14% using AlexNet, VGG16, VGG19 and basic CNN models respectively. The experimental results suggest that the proposed framework could be helpful to the neurology community for recognition of seizures types.

A Novel, Low-Cost, Wide-Field, Infant Retinal Camera, "Neo": Technical and Safety Report for the Use on Premature Infants.

PURPOSE: To report the technical aspects, systemic, and ocular safety of a novel, low-cost, wide-field, infant retinal camera for use on premature infants. METHODS: The device, the "3nethra Neo" (Neo) is a 120° portable, contact, wide-field, unibody camera, with a CMOS sensor (2040 × 2040 resolution) and a warm light-emitting diode (LED) illumination source. The Neo was used to image 140 awake, preterm infants between postmenstrual age (PMA) of 28 to 37 weeks, undergoing retinopathy of prematurity (ROP) screening. Baseline, 'during procedure', at 5 minutes, and for 60 minutes postprocedure, readings of oxygen saturation and heart rate were recorded. The device design, optics, illumination, and software specifications were compared with the RetCam 3. RESULTS: Study defined bradycardia (9 infants, 6.4%), tachycardia (3 infants, 2.1%), and hypoxia (2 infants, 1.4%) were observed but there were no clinically significant systemic changes that required intervention during or following any of the study time intervals. There was a transient increase in heart rate by 9.68 (7.53-11.83; P < 0.0001) and marginal decrease in oxygen saturation (-1.94 [-1.60 to -2.28], P < 0.0001), which started to return to baseline 5 minutes after the procedure. Transient redness was seen in two eyes (0.7%) of two infants. No other ocular adverse effects were observed. CONCLUSIONS: The Neo is easy to use in preterm infants and being compact was readily portable. There were no significant ocular or systemic adverse effects, potentially allowing it to be a viable low-cost device for ROP screening in low resource settings. TRANSLATIONAL RELEVANCE: The camera provides a safe and affordable alternative to image the retina of infants by using novel illumination and lens mechanics and has the potential of worldwide acceptance.

Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. Epilepsy is a brain disorder described by recurrent and unpredictable interruption of healthy brain function. Diagnosis of patients with epilepsy requires monitoring and visual inspection of long-term EEG by the neurologist, which is found to be a time-consuming procedure. Therefore, this study proposes an automated seizure detection model using a novel computationally efficient feature named sigmoid entropy derived from discrete wavelet transforms. The sigmoid entropy was estimated from the wavelet coefficients in each sub-band and classified using a non-linear support vector machine classifier with leave-one-subject-out cross-validation. The performance of the proposed method was tested with the Ramaiah Medical College and Hospital (RMCH) database, which consists of the 58 Hours of EEG from 115 subjects, the University of Bonn (UBonn), and CHB-MIT databases. Results showed that sigmoid entropy exhibits lower values for epileptic EEG in contrary to other existing entropy methods. We observe a seizure detection rate of 96.34%, a false detection rate of 0.5/h and a mean detection delay of 1.2 s for the RMCH database. The highest sensitivity of 100% and 94.21% were achieved for UBonn and CHB-MIT databases respectively. The performance comparison confirms that sigmoid entropy was found to be better and computationally efficient as compared to other entropy methods. It can be concluded that the proposed sigmoid entropy could be used as a potential biomarker for recognition and detection of epileptic seizures.

Retinal vascular tortuosity in schizophrenia and bipolar disorder.

The micro-vasculature of retina and brain share common morphological, physiological, and pathological properties. Retina being easily accessible, retinal vascular examination provides an indirect assessment of cerebral vasculature. Considering the high prevalence of vascular morbidity in SCZ and BD a few studies have examined retinal vascular caliber and have reported increased retinal venular caliber in schizophrenia (SCZ). Retinal vascular tortuosity could serve as a better structural measure than caliber as it is static and less susceptible to pulse period variations. However, to date, no study has examined retinal vascular tortuosity in SCZ and bipolar disorder (BD). Hence, we examined retinal vascular tortuosity in comparison with healthy volunteers (HV). We included 255 subjects (78 HV, 79 SCZ, and 86 BD) in the age range of 18 to 50 years. Trained personnel acquired images using a non-mydriatic fundus camera. To measure the average retinal arteriolar tortuosity index (RATI) and retinal venular tortuosity index (RVTI), we used a previously validated, semi-automatic algorithm. The results showed significant differences across the three groups in RATI but not in RVTI; both BD and SCZ had significantly increased RATI compared to HV. There was also a significant difference between SCZ and BD, with BD having higher RATI. If shown to be of predictive utility in future longitudinal studies, it has the potential to identify patients at risk of development of adverse vascular events. As retinal vascular imaging is non-invasive and inexpensive, it could serve as a proxy marker and window to cerebral vasculature.

Retinal vascular fractal dimension in bipolar disorder and schizophrenia.

BACKGROUND: Bipolar disorder (BD) and schizophrenia (SCZ), are associated with greater vascular co-morbidities and adverse vascular events. Owing to shared developmental origins and morphology, retinal vasculature is a proxy assessment measure of the cerebral vasculature. Although retinal vascular fractal dimension (Df), a measure of vascular geometry and complexity of branching, has been shown to be directly associated with cerebrovascular pathology, it has not been examined in SCZ and BD. METHODS: We studied 277 participants (92 healthy volunteers, 98 SCZ, and 87 BD) from 18 to 50 years of age. Images were acquired by trained personnel using a non-mydriatic fundus camera and the retinal vascular Df was calculated by the box-counting method using an automated algorithm. The average Df across the left and right eyes were calculated. RESULTS: Both SCZ and BD had significantly increased Df compared to HV despite controlling for possible confounding factors. However, there was no significant difference between SCZ and BD. These findings suggest abnormal retinal vascular Df in psychoses. LIMITATIONS: The study design was cross-sectional, and patients were on medications. Confound of lifestyle factors such as diet and exercise, if any, was not controlled. Sub-group analysis between BD-I and BD-II was not performed in view of the small sample. CONCLUSIONS: Considering the easy accessibility, affordability, and non-invasive nature of the examination, retinal vascular Df could serve as a surrogate marker for cerebral vascular abnormality and could potentially identify BD and SCZ patients at risk of developing adverse vascular events.