Ruddlesden-Popper 2D Perovskite-MoS2 Hybrid Heterojunction Photocathodes for Efficient and Scalable Photo-Rechargeable Li-Ion Batteries.

Photo-rechargeable batteries (PRBs) can provide a compact solution to power autonomous smart devices located at remote sites that cannot be connected with the grid. The study reports the Ruddlesden-Popper (RP) metal halide perovskite (MHP) and molybdenum disulfide (MoS2) hybrid heterojunction-based photocathodes for Li-ion photo-rechargeable battery (Li-PRB) applications. Hybrid Lithium-ion batteries (LIBs) have demonstrated an average discharge specific capacity of 144.46 and 129.17 mAhg-1 for 50 cycles when operating at 176 and 294 mAg-1, respectively compared to the pristine LIBs which have shown specific capacity of 37.48 and 25.60 mAhg-1 under similar conditions. Hybrid Li-PRB has achieved an average dark discharge specific capacities of 128.66 mAhg-1 (capacity retention: 96.56%) which enhanced to 180.67 mAhg-1 under illumination (capacity retention: 97.39%; photo-enhancement: 40.42%) at 64 mAg-1. Excellent performance of hybrid Li-PRB is attributed to the formation of type-II heterojunction that leads to improved crystallinity and film morphology. The PRB has demonstrated a high photo conversion and storage efficiency (PC-SE) of 0.52% under standard 1 Sun illumination, which outperforms other previously reported MHP based LIBs and PRBs. This work provides a novel approach of harnessing the potential of MHPs for PRBs and offers new avenues for MHP photocathodes for various applications beyond PRBs.

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages.

OBJECTIVE: A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed. METHODS: A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters. RESULTS: The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects. CONCLUSION: AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.

Infomarkers for transition to goals consistent with palliative care in dying patients.

OBJECTIVE: Electronic health records (EHRs) may contain infomarkers that identify patients near the end of life for whom it would be appropriate to shift care goals to palliative care. Discovery and use of such infomarkers could be used to conduct effectiveness research that ultimately could help to reduce the monumental cost of caring for the dying. The aim of our study was to identify changes in the plans of care that represent infomarkers, which signal a transition of care goals from nonpalliative care ones to those consistent with palliative care. METHOD: Using an existing electronic health record database generated during a two-year longitudinal study of nine diverse medical-surgical units from four Midwest hospitals and a known group approach, we evaluated patient care episodes for 901 patients who died (mean age = 74.5 ± 14.6 years). We used ANOVA and Tukey's post-hoc tests to compare patient groups. RESULTS: We identified 11 diagnoses, including Death Anxiety and Anticipatory Grieving, whose addition to the care plan, some of which also occurred with removal of nonpalliative care diagnoses, represent infomarkers of transition to palliative care goals. There were four categories of patients, those who had: no infomarkers on plans (n = 507), infomarkers added on the admission plan (n = 194), infomarkers added on a post-admission plan (minor transitions, n = 109), and infomarkers added and nonpalliative care diagnoses removed on a post-admission plan (major transition, n = 91). Age, length of stay, and pain outcomes differed significantly for these four categories of patients. SIGNIFICANCE OF RESULTS: EHRs contain pertinent infomarkers that if confirmed in future studies could be used for timely referral to palliative care for improved focus on comfort outcomes and to identify palliative care subjects from data repositories in order to conduct big-data research, comparative effectiveness studies, and health-services research.

Lycopene alleviates BCG-induced depressive phenotypes in mice by disrupting 5-HT3 receptor - IDO1 interplay in the brain.

The 5-HT3 receptor and indoleamine 2,3-dioxygenase 1 (IDO1) enzyme play a crucial role in the pathogenesis of depression as their activation reduces serotonin contents in the brain. Since molecular docking analysis revealed lycopene as a potent 5-HT3 receptor antagonist and IDO1 inhibitor, we hypothesized that lycopene might disrupt the interplay between the 5-HT3 receptor and IDO1 to mitigate depression. In mice, the depression-like phenotypes were induced by inoculating Bacillus Calmette-Guerin (BCG). Lycopene (intraperitoneal; i.p.) was administered alone or in combination with 5-HT3 receptor antagonist ondansetron (i.p.) or IDO1 inhibitor minocycline (i.p.), and the behavioral screening was performed by the sucrose preference test, open field test, tail suspension test, and splash test which are based on the different principles. Further, the brains were subjected to the biochemical analysis of serotonin and its precursor tryptophan by the HPLC. The results showed depression-like behavior in BCG-inoculated mice, which was reversed by lycopene administration. Moreover, prior treatment with ondansetron or minocycline potentiated the antidepressant action of lycopene. Minocycline pretreatment also enhanced the antidepressant effect of ondansetron indicating the regulation of IDO1 activity by 5-HT3 receptor-triggered signaling. Biochemical analysis of brain samples revealed a drastic reduction in the levels of tryptophan and serotonin in depressed animals, which were restored following treatment with lycopene and its combination with ondansetron or minocycline. Taken together, the data from molecular docking, behavioral experiments, and biochemical estimation suggest that lycopene might block the 5-HT3 receptor and consequently inhibit the activity of IDO1 to ameliorate BCG-induced depression in mice.

A fully automated classification of third molar development stages using deep learning.

Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators. The dataset includes images from both Q3 (lower jaw left side) and Q4 (lower right side) regions extracted from panoramic images, resulting in a total of 6624 images for analysis. Following data collection, the methodology employs region of interest extraction, pre-filtering, and extensive data augmentation techniques to enhance classification accuracy. The deep neural network model, including architectures such as EfficientNet, EfficientNetV2, MobileNet Large, MobileNet Small, ResNet18, and ShuffleNet, is optimized for this task. Our findings indicate that EfficientNet achieved the highest classification accuracy at 83.7%. Other architectures achieved accuracies ranging from 71.57 to 82.03%. The variation in performance across architectures highlights the influence of model complexity and task-specific features on classification accuracy. This research introduces a novel machine learning model designed to accurately estimate the development stages of lower wisdom teeth in OPG images, contributing to the fields of dental diagnostics and treatment planning.

Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes.

The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study is to assess self-management practices among patients with type 2 diabetes in rural areas of Pakistan using Artificial intelligence and machine learning algorithms. Of particular note is the assessment of the factors associated with poor self-management activities, such as non-adhering to medications, poor eating habits, lack of physical activities, and poor glycemic control (HbA1c %). The sample of 200 participants was purposefully recruited from the medical clinics in rural areas of Pakistan. The artificial neural network algorithm and logistic regression classification algorithms were used to assess diabetes self-management activities. The diabetes dataset was split 80:20 between training and testing; 80% (160) instances were used for training purposes and 20% (40) instances were used for testing purposes, while the algorithms' overall performance was measured using a confusion matrix. The current study found that self-management efforts and glycemic control were poor among diabetes patients in rural areas of Pakistan. The logistic regression model performance was evaluated based on the confusion matrix. The accuracy of the training set was 98%, while the test set's accuracy was 97.5%; each set had a recall rate of 79% and 75%, respectively. The output of the confusion matrix showed that only 11 out of 200 patients were correctly assessed/classified as meeting diabetes self-management targets based on the values of HbA1c < 7%. We added a wide range of neurons (32 to 128) in the hidden layers to train the artificial neural network models. The results showed that the model with three hidden layers and Adam's optimisation function achieved 98% accuracy on the validation set. This study has assessed the factors associated with poor self-management activities among patients with type 2 diabetes in rural areas of Pakistan. The use of a wide range of neurons in the hidden layers to train the artificial neural network models improved outcomes, confirming the model's effectiveness and efficiency in assessing diabetes self-management activities from the required data attributes.

Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning.

Patient pain can be detected highly reliably from facial expressions using a set of facial muscle-based action units (AUs) defined by the Facial Action Coding System (FACS). A key characteristic of facial expression of pain is the simultaneous occurrence of pain-related AU combinations, whose automated detection would be highly beneficial for efficient and practical pain monitoring. Existing general Automated Facial Expression Recognition (AFER) systems prove inadequate when applied specifically for detecting pain as they either focus on detecting individual pain-related AUs but not on combinations or they seek to bypass AU detection by training a binary pain classifier directly on pain intensity data but are limited by lack of enough labeled data for satisfactory training. In this paper, we propose a new approach that mimics the strategy of human coders of decoupling pain detection into two consecutive tasks: one performed at the individual video-frame level and the other at video-sequence level. Using state-of-the-art AFER tools to detect single AUs at the frame level, we propose two novel data structures to encode AU combinations from single AU scores. Two weakly supervised learning frameworks namely multiple instance learning (MIL) and multiple clustered instance learning (MCIL) are employed corresponding to each data structure to learn pain from video sequences. Experimental results show an 87% pain recognition accuracy with 0.94 AUC (Area Under Curve) on the UNBC-McMaster Shoulder Pain Expression dataset. Tests on long videos in a lung cancer patient video dataset demonstrates the potential value of the proposed system for pain monitoring in clinical settings.

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters.

INTRODUCTION: We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images. METHODS: A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were cropped to extract the cervical vertebrae using an Aggregate Channel Features (ACF) object detector. The resulting images were used to train four different Deep Learning (DL) models: our proposed CNN, MobileNetV2, ResNet101, and Xception, together with a set of tunable directional edge enhancers. When using MobileNetV2, ResNet101 and Xception, data augmentation is adopted to allow adequate network complexity while avoiding overfitting. The performance of our CNN model was compared with that of MobileNetV2, ResNet101 and Xception with and without the use of directional filters. For validation and performance assessment, k-fold cross-validation, ROC curves, and p-values were used. RESULTS: The proposed innovative model that uses a CNN preceded with a layer of tunable directional filters achieved a validation accuracy of 84.63%84.63% in CVM stage classification into five classes, exceeding the accuracy achieved with the other DL models investigated. MobileNetV2, ResNet101 and Xception used with directional filters attained accuracies of 78.54%, 74.10%, and 80.86%, respectively. The custom-designed CNN method also achieves 75.11% in six-class CVM stage classification. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If the custom-designed CNN is used without the directional filters, the test accuracy decreases to 80.75%. In the Xception model without the directional filters, the testing accuracy drops slightly to 79.42% in the five-class CVM stage classification. CONCLUSION: The proposed model of a custom-designed CNN together with the tunable Directional Filters (CNNDF) is observed to provide higher accuracy than the commonly used pre-trained network models that we investigated in the fully automated determination of the CVM stages.

Ruddlesden-Popper 2D perovskites of type (C6H9C2H4NH3)2(CH3NH3)n-1PbnI3n+1 (n = 1-4) for optoelectronic applications.

Ruddlesden-Popper (RP) phase metal halide organo perovskites are being extensively studied due to their quasi-two dimensional (2D) nature which makes them an excellent material for several optoelectronic device applications such as solar cells, photo-detectors, light emitting diodes (LEDs), lasers etc. While most of reports show use of linear carbon chain based organic moiety, such as n-Butylamine, as organic spacer in RP perovskite crystal structure, here we report a new series of quasi 2D perovskites with a ring type cyclic carbon group as organic spacer forming RP perovskite of type (CH)2(MA)n-1PbnI3n+1; CH = 2-(1-Cyclohexenyl)ethylamine; MA = Methylamine). This work highlights the synthesis, structural, thermal, optical and optoelectronic characterizations for the new RP perovskite series n = 1-4. The demonstrated RP perovskite of type for n = 1-4 have shown formation of highly crystalline thin films with alternate stacking of organic and inorganic layers, where the order of PbI6 octahedron layering are controlled by n-value, and shown uniform direct bandgap tunable from 2.51 eV (n = 1) to 1.92 eV (n = 4). The PL lifetime measurements supported the fact that lifetime of charge carriers increase with n-value of RP perovskites [154 ps (n = 1) to 336 ps (n = 4)]. Thermogravimetric analysis (TGA) showed highly stable nature of reported RP perovskites with linear increase in phase transition temperatures from 257 °C (n = 1) to 270 °C (n = 4). Scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDAX) are used to investigate the surface morphology and elemental compositions of thin films. In addition, the photodetectors fabricated for the series using (CH)2(MA)n-1PbnI3n+1 RP perovskite as active absorbing layer and without any charge transport layers, shown sharp photocurrent response from 17 nA/cm2 for n = 1 to 70 nA/cm2 for n = 4, under zero bias and low power illumination conditions (470 nm LED, 1.5 mW/cm2). Furthermore, for lowest bandgap RP perovskite n = 4, (CH)2MA3Pb4I13 the photodetector showed maximum photocurrent density of ~ 508 nA/cm2 at 3 V under similar illumination condition, thus giving fairly large responsivity (46.65 mA/W). Our investigations show that 2-(1-Cyclohexenyl)ethylamine based RP perovskites can be potential solution processed semiconducting materials for optoelectronic applications such as photo-detectors, solar cells, LEDs, photobatteries etc.

Identifying the predictors of Covid-19 infection outcomes and development of prediction models.

BACKGROUND: The infection of Corona Virus Disease (Covid-19) is challenging health problems worldwide. COVID-19 pandemic is spreading all over the world with the number of infected cases increased to 54.4 million with 1.32 million deaths. Different types of statistical models have been developed to predict viral infection and multiple studies have compared the performance of these predictive models, but results were not consistent. This study aimed to develop and provide easy to use model to predict the Covid-19 infection severity in the patients and to help understanding the patient's condition. METHODS: This study analyzed simulated data obtained from the large database for 340 patients with an active Covid-19 infection. The study identified predictors of Covid-19 outcomes that may be measured in two different ways: the total T-cell levels in the blood with T-cell subsets and number of cells in the blood infected with virus. All measures are relatively unobtrusive as they only require a blood sample, however there is a significant laboratory cost implications for measuring the number of cells infected with virus. This study used methodological approach using two different methods showing how multiple regression and logistic regression can be used in the context of Covid-19 longitudinal data to develop the prediction models. RESULTS: This study has identified the predictors of Covid-19 infection outcomes and developed prediction models. In the regression model of Total_T Cell, the predictors BMI, comorbidity and Total_Tcell were all associated with increased levels of infection severity (p < 0.001). For BMI, the mean % of unhealthy cells increased by 0.42 (95% CI 0.24 to 0.60) and comorbidity predictor has on average 8.3% more unhealthy liver cells than without comorbidity (95% CI - 2.9%-1.29%). The results of multivariate logistic regression model predicting the Covid-19 Infection severity were promising. The significant predictors were observed such as Age (OR 0.95, p = 0.02, 95% CI: 0.91-0.99), Helper T_cells (OR O.93, p = 0.03, 95% CI: 0.87-0.99), Basic_Tcell (OR 1.11, p = 0.001, 95% CI: 1.06-1.71) and Comorbidity (OR 0.41, p = 0.05, 95% CI: 0.16-1.07). CONCLUSIONS: In this study recommendation has been provided to clinical researchers on the best way to use the various Covid-19 infections measures along with identifying other possible predictors of Covid-19 infection. It is imperative to monitor closely the T-cell subsets using prediction models that might provide valuable information about the patient's condition during the treatment process.

Applications of a Chronic Care Model for Self-Management of Type 2 Diabetes: A Qualitative Analysis.

The main aim of this study was to explore the suitability, practicality, and acceptability of the self-management support and delivery system design components of the Chronic Care Model (CCM) in type 2 diabetes self-management in primary care settings in rural Pakistan. Thirty patients living with type 2 diabetes and 20 healthcare professionals (10 general practitioners and 10 nurses) were recruited from Al-Rehman Hospital at Abbottabad, Pakistan. The study data were collected using semi-structured interviews and analyzed using thematic analysis. The self-management element of the CCM played an important role in managing type 2 diabetes, and self-efficacy in relation to diet and diabetes management were the most effective strategies. Surprisingly, considering the local culture around diabetes, patient care reflecting their cultural background was identified as an important factor by patients not healthcare professionals. The delivery system design element of the CCM promoted multidisciplinary teamwork. Our findings suggest that the self-management support and delivery system design components of the CCM provided an effective framework for supporting diabetes self-management education and support in rural areas.

Healthcare Professionals' Perspectives of Patients' Experiences of the Self-Management of Type 2 Diabetes in the Rural Areas of Pakistan: A Qualitative Analysis.

The main objective of this research work was to explore the healthcare professionals' perspectives of type 2 diabetes patients' experiences of self-management of diabetes in the rural area of Pakistan. In this study, we have carried out a methodological approach to use a self-management framework to direct the interview guide for healthcare professionals to examine their perceptions and expectations of their diabetes patients' adherence to the medications prescribed. Twenty healthcare professionals were recruited in this study consisting of ten general practitioners and ten nurses from various clinics (medical centres) of Al-Rehman Hospital at Abbottabad, Pakistan. This qualitative study explored the feelings and opinions of general practitioners on patients' compliance and adherence by using the semi-structured interview guide using a methodological framework. All interviews of participants were audiotaped and transcribed for content analysis. Six major themes were identified: patient-doctor relationship; patient's non-adherence to diet and exercise; conflicts with the patients; low self-efficacy and feeling of "resignation with poor care"; the influence of culture on patients' self-management activities and lack of support for patients by health care providers, patients, and their families. We have derived relevant solutions from qualitative studies and considered that communication, tailored, and shared care is the best approach for patient adherence to treatment. GPs felt that a structured consultation and follow-up in a multidisciplinary team might help to increase adherence. The results of this qualitative health research highlighted the challenges healthcare professionals are facing in rural Pakistan in managing patients with type 2 diabetes and supporting their management activities. Healthcare professionals and patients may benefit by adopting a methodological framework approach to ensure meaningful participation and adjusting the patient-doctor relationship, and setting up achievable management and self-management goals.

Application of structural equation modeling in assessing the self-management activities of patients with type 2 diabetes.

OBJECTIVE: This study aimed at assessing the self-management activities of type 2 diabetes patients using Structural Equation Modeling (SEM) which measures and analyzes the correlations between observed and latent variables. This statistical modeling technique explored the linear causal relationships among the variables and accounted for the measurement errors. METHODS: A sample of 200 patients was recruited from the middle-aged population of rural areas of Pakistan to explore the self-management activities of type 2 diabetes patients using the validated version of the Urdu Summary of Diabetes Self-care Activities (U-SDSCA) instrument. The structural modeling equations of self-management of diabetes were developed and used to analyze the variation in glycemic control (HbA1c). RESULTS: The validated version of U-SDSCA instrument showed acceptable psychometric properties throughout a consecutive reliability and validity evaluation including: split-half reliability coefficient 0.90, test-retest reliability (r = 0.918, P ≤ .001), intra-class coefficient (0.912) and Cronbach's alpha (0.79). The results of the analysis were statistically significant (α = 0.05, P-value < .001), and showed that the model was very well fitted with the data, satisfying all the parameters of the model related to confirmatory factor analysis with chi-squared = 48.9, CFI = 0.94, TLI = 0.95, RMSEA = 0.065, SPMR = 0.068. The model was further improved once the items related to special diet were removed from the analysis, chi-squared value (30.895), model fit indices (CFI = 0.98, TLI = 0.989, RMSEA = 0.045, SPMR = 0.048). A negative correlation was observed between diabetes self-management and the variable HbA1c (r = -0.47; P < .001). CONCLUSIONS: The Urdu Summary of Diabetes Self-Care Activities (U-SDSCA) instrument was used for the patients of type 2 diabetes to assess their diabetes self-management activities. The structural equation models of self-management showed a very good fit to the data and provided excellent results which may be used in future for clinical assessments of patients with suboptimal diabetes outcomes or research on factors affecting the associations between self-management activities and glycemic control.

The Summary of an Urdu Version of Diabetes Self-Care Activities Measure: Psychometric Evaluation and Validation.

Objective: The English version of the Summary of Diabetes Self-Care Activities (SDSCA) measure is the most frequently used self-reporting instrument assessing diabetes self-management. This study is aimed at translating English SDSCA into the Urdu version and validating and evaluating its psychometric properties. Methods: The Urdu version of SDSCA was developed based on the guidelines provided by the World Health Organization for translation and adaptation of instruments. The panel of experts examined the content validity, reliability, and internal consistency of the instrument. The translation process from the English version to the Urdu version revealed excellent results at all the stages. Results: The instrument showed promising and acceptable results. Of particular mention are the results related to split-half reliability coefficient 0.90, test-retest reliability (r = 0.918, P < .001), intraclass coefficient (0.912), and Cronbach's alpha (.79). The factor analysis (exploratory and confirmatory) was not performed in this study due to the small sample size (n = 30) as the objective was to validate the Urdu version of the SDSCA instrument. Conclusions: This study provided evidence for the reliability and validity of the Urdu Summary of Diabetes Self-Care Activities (U-SDSCA) instrument, which may be used in the future for the patients of diabetes in order to assess type 2 diabetes self-management activities in the rural area of Pakistan and other Urdu-speaking countries.

Predicting Hospital Re-admissions from Nursing Care Data of Hospitalized Patients.

Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission.However, most of the EHRs are high dimensional and sparsely populated, and analyzing such data sets is a Big Data challenge. The effect of some of the well-known dimension reduction techniques is minimized due to presence of non-linear variables. We use association mining as a dimension reduction method and the results are used to develop models, using data from an existing nursing EHR system, for predicting risk of re-admission to the hospitals. These models can help in determining effective treatments for patients to minimize the possibility of re-admission, bringing down the cost and increasing the quality of care provided to the patients. Results from the models show significantly accurate predictions of patient re-admission.

Framework for Mining and Analysis of Standardized Nursing Care Plan Data.

Despite an unprecedented amount of health-related data being amassed from various technological innovations, our ability to process this data and extract hidden knowledge has yet to catch up with this explosive growth. Although nursing care plans can be an effective tool to support the achievement of desired patient outcomes, their online collection, storage, and processing is lagging far behind. As a result, the impact of nursing care is not well understood from qualitative as well as quantitative perspectives. In this article, we first outline a complete life cycle of nursing care data, and present a knowledge discovery and analysis framework for such data sets. We also highlight Big Data issues pertaining to the analysis of nursing care data. Using an exemplar data set, we demonstrate the broad applicability of the proposed framework by showing knowledge discovery results for different outcomes related to patients, nursing staff, and administrators.

Automated Real-Time Conjunctival Microvasculature Image Stabilization.

The bulbar conjunctiva is a thin, vascularized membrane covering the sclera of the eye. Non-invasive imaging techniques have been utilized to assess the conjunctival vasculature as a means of studying microcirculatory hemodynamics. However, eye motion often confounds quantification of these hemodynamic properties. In the current study, we present a novel optical imaging system for automated stabilization of conjunctival microvasculature images by real-time eye motion tracking and realignment of the optical path. The ability of the system to stabilize conjunctival images acquired over time by reducing image displacements and maintaining the imaging area was demonstrated.

Feasibility of level-set analysis of enface OCT retinal images in diabetic retinopathy.

Pathology segmentation in retinal images of patients with diabetic retinopathy is important to help better understand disease processes. We propose an automated level-set method with Fourier descriptor-based shape priors. A cost function measures the difference between the current and expected output. We applied our method to enface images generated for seven retinal layers and determined correspondence of pathologies between retinal layers. We compared our method to a distance-regularized level set method and show the advantages of using well-defined shape priors. Results obtained allow us to observe pathologies across multiple layers and to obtain metrics that measure the co-localization of pathologies in different layers.