Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem impacts Generative Adversarial Networks' capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, both varieties of the mode collapse problem are investigated, and their subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization with the Deep Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse problems. Synthetically generated images are utilized for data augmentation and training a Vision Transformer model. The classification performance of the model is evaluated using accuracy, recall, and precision scores. Results demonstrate that the DCGAN and the ACGAN with adaptive input-image normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as evidenced by the superior diversity scores and classification scores.Comment: Submitted to the Elsevier Journa

Classification of Stress via Ambulatory ECG and GSR Data

In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights into mental health and behavioural states through psychophysiological analysis. However, automated analysis is required to provide timely results due to the quantity of data collected. Machine learning has shown efficacy in providing an automated classification of physiological data for health applications in controlled laboratory environments. Ambulatory uncontrolled environments, however, provide additional challenges requiring further modelling to overcome. This work empirically assesses several approaches utilising machine learning classifiers to detect stress using physiological data recorded in an ambulatory setting with self-reported stress annotations. A subset of the training portion SMILE dataset enables the evaluation of approaches before submission. The optimal stress detection approach achieves 90.77% classification accuracy, 91.24 F1-Score, 90.42 Sensitivity and 91.08 Specificity, utilising an ExtraTrees classifier and feature imputation methods. Meanwhile, accuracy on the challenge data is much lower at 59.23% (submission #54 from BEaTS-MTU, username ZacDair). The cause of the performance disparity is explored in this work.Comment: Associated Code to enable reproducible experimental work - https://github.com/ZacDair/EMBC_Release SMILE dataset provided by Computational Wellbeing Group (COMPWELL) https://compwell.rice.edu/workshops/embc2022/dataset - https://compwell.rice.edu

Efficacy of hypertonic saline versus isotonic saline among children with cystic fibrosis: A systematic review and meta-analysis

Background: Inhaled hypertonic saline (HS) reduces pulmonary exacerbations in patients with cystic fibrosis (CF) aged 6 or more years. However, the effectiveness of HS in improving clinical outcomes in younger children aged 6 or less years is not established. This study examines the efficacy of HS in younger CF patients. Methods: Searches were conducted across three databases (Medline, Cochrane Central and EMBASE) from inception through July 2022. Randomized controlled trials assessing the impact of HS in younger CF patients were included. Trials involving only patients greater than 6 years or control group other than isotonic saline (IS) were excluded. Outcomes measured included lung clearance index (LCI), cystic fibrosis questionnaire (CFQ-R) score, spirometry measures, oxygen saturation, respiratory rate, height and weight. Outcomes were reported as mean differences (MDs) with 95% confidence intervals. Results: Seven studies (n = 390 patients) were included in this review. HS significantly reduced the LCI (MD: -0.67; 95%CI, -1.05 to 0.29, P = 0.0006) compared to IS. In addition, HS was associated with significant improvements in height (MD: 2.23; 95%CI, -0.00 to 4.46, P = 0.05) and CFQ-R (MD: 4.30; 95%CI, 0.65–7.95, P = 0.02), but not in oxygen saturation (MD: -0.15; 95%CI, -0.54 to 0.25, P = 0.47), respiratory rate (MD: -0.21; 95%CI, -2.19 to 1.77, P = 0.83) or weight (MD: 0.70; 95%CI, -0.47 to 1.87, P = 0.24). Furthermore, HS did not significantly improve spirometry measures, including FEV1 (MD: -0.11; 95%CI, -0.21 to 0.43, P = 0.51) and forced vital capacity (MD: 0.27; 95%CI, -0.49 to 1.04, P = 0.48), but significantly improved FEF25-75 (MD: 0.12; 95% CI, 0.05–0.20; P = 0.002). Discussion: Treatment with HS in younger children with CF improves lung clearance, symptoms and quality of life. FEF25-75 may prove a more sensitive measure for assessing intervention related improvements in pediatric CF trials. Conclusion: The findings support HS as a therapeutic method in CF-affected children

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.Comment: Submitted to the Journa

Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery

Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as the severity of the DR stage increases, affecting the classifiers' diagnostic capacity. The imbalance can be addressed using Generative Adversarial Networks (GANs) to augment the datasets with synthetic images. Generating synthetic images is advantageous if high-quality and diversified images are produced. To evaluate the quality and diversity of synthetic images, several evaluation metrics, such as Multi-Scale Structural Similarity Index (MS-SSIM), Cosine Distance (CD), and Fr\'echet Inception Distance (FID) are used. Understanding the effectiveness of each metric in evaluating the quality and diversity of GAN-based synthetic images is critical to select images for augmentation. To date, there has been limited analysis of the appropriateness of these metrics in the context of biomedical imagery. This work contributes an empirical assessment of these evaluation metrics as applied to synthetic Proliferative DR imagery generated by a Deep Convolutional GAN (DCGAN). Furthermore, the metrics' capacity to indicate the quality and diversity of synthetic images and a correlation with classifier performance is undertaken. This enables a quantitative selection of synthetic imagery and an informed augmentation strategy. Results indicate that FID is suitable for evaluating the quality, while MS-SSIM and CD are suitable for evaluating the diversity of synthetic imagery. Furthermore, the superior performance of Convolutional Neural Network (CNN) and EfficientNet classifiers, as indicated by the F1 and AUC scores, for the augmented datasets demonstrates the efficacy of synthetic imagery to augment the imbalanced dataset.Comment: 29 Pages, 8 Figures, submitted to MEDAL23: Advances in Deep Generative Models for Medical Artificial Intelligence (Springer Nature series

Current trends in complete denture education in undergraduate dental colleges of Pakistan

Objective: To determine the current trends in complete denture education in undergraduate dental colleges. Method: The survey-based study was conducted in April and May 2020 at undergraduate dental colleges of Pakistan, and comprised heads of the Prosthodontics Department at all dental colleges across Pakistan having at least one batch of final year dental students. Data was collected using an online predesigned questionnaire that explored theoretical and practical teaching patterns of complete denture prosthodontics in the undergraduate years, and the materials and practices of students when constructing complete dentures in the clinics. The participants were given the option of choosing more than one option where needed. Data was analysed using SPSS 23. Results: Of the 49 subjects approached, 40(81.6%) returned the forms duly filled; 11(27.5%) from public-sector institutions and 29(72.5%) from the private sector. There were 26(65%) institutions which required that their undergraduate students fabricate 2-4 conventional complete dentures. In all 40(100%) colleges, faculty gave live clinical demonstrations before students fabricated conventional complete dentures in the outpatient departments. Teaching strategy included small group discussions in 25(62.5%) institutions. Green stick 40(100%), zinc oxide eugenol 40(100%) and impression compound 39(97.2%) were the materials of choice for various steps of impression making. In all the 40(100%) institutions, students fabricated conventional complete dentures during their prosthodontics rotation. Immediate, copy and overdentures were constructed by students in 8(20%), 3(7.5%) and 8(20%) institutions, respectively. Conclusions: Majority of the dental schools used similar impression materials and techniques for fabricating conventional complete dentures. Didactic teaching of conventional and unconventional complete dentures was being carried out at a huge majority of the dental institutions studied. Key Words: Current trends, Complete dentures, Undergraduate dental education

Regenerative Potential of Enamel Matrix Protein Derivative and Acellular Dermal Matrix for Gingival Recession: A Systematic Review and Meta-Analysis

Objective: The purpose of this study was to assess the clinical effectiveness of using a combination of enamel matrix protein derivative and acellular dermal matrix in comparison to acellular dermal matrix alone for treating gingival recessions. Methods: The Cochrane Library (Wiley), PubMed by Medline (NLM), Medline (EBSCO), and Embase (Ovid) databases were searched for entries up to April 2020. Only clinical trials were included. Primary outcomes were root coverage (%), changes in keratinized tissue width and recession (mm). Meta-analysis was conducted for root coverage, changes in keratinized tissue width, recession, clinical attachment level and probing depth. Results: Four studies were selected for the analysis. In primary outcomes, root coverage, change in keratinized tissue width and recession analysis showed a mean difference of 4.99% (p = 0.11), 0.20 mm (p = 0.14) and 0.13 mm (p = 0.23) respectively between the two groups. Secondary outcomes analysis also exhibited a statistically insignificant difference between the test and control group with mean difference of 0.11 mm (p = 0.32) in clinical attachment level gain and -0.03 mm (p = 0.29) in probing depth reduction analysis. Conclusions: Within the limits of this study, enamel matrix protein derivative combined with acellular dermal matrix used for treating gingival recession defects resulted in no beneficial effect clinically than acellular dermal matrix only

Outcomes after anti-thymocyte globulin vs Basiliximab induction before deceased donor kidney transplants

Background: Deceased donor kidney transplants represent an important source of renal replacement for the 100 000 patients initiating hemodialysis annually. We compared the association of induction therapy, anti-thymocyte globulin [rabbit] (rATG) or basiliximab, with posttransplant rejection, graft and patient survival.Methods: Using the United Network for Organ Sharing (UNOS) database, we identified patients that received deceased donor kidney transplants. The outcomes analyzed were 6- month rejection, 1-year rejection, patient survival and graft survival. Multivariate logistic regression models were constructed to understand the association of induction therapy and rejection. Cox-proportional hazards models were constructed to ascertain the association of choice of induction therapy with both patient and graft survival.Results: Of 45 339 patients, 33 906 patients received rATG induction therapy and 11 433 patients received basiliximab induction therapy. The rATG group were younger (53.44 years vs 55.28 years, P \u3c 0.001), more frequently female (58.74% male vs 66.08%, P \u3c 0.001) and more frequently Black (34.78% vs 25.66%, p \u3c 0.001) compared with patients in the basiliximab group. Rejection was more likely with basiliximab compared with rATG at 6 months(OR = 1.64, P \u3c 0.001; 7.81% Basiliximab vs 5.23% rATG)and at 12 months (OR = 1.56, P \u3c 0.001; 8.81% Basiliximab vs 6.31% rATG). Basiliximab induction therapy was associated with worse patient survival, (HR = 1.05, P = 0.017). Basiliximab induction therapy was associated with worse graft survival, (HR = 1.03, P = 0.037).Conclusion: The analysis of the national experience demonstrated favorable rejection, patient survival, and graft survival with rATG usage. Further prospective data are necessary to provide treatment recommendations

HLA mismatch is important for 20-year graft survival in kidney transplant patients

Background: Human leukocyte antigens (HLA) matching is gradually being omitted from clinical practice in evaluation for renal allograft transplant. While such practices may yield shorter wait times and adequate short-term outcomes, graft longevity in HLA mismatched patients remains unclear. This study aims to demonstrate that HLA matching may still play an important role in long-term graft survival. Methods: We identified patients undergoing an index kidney transplant in the United Network for Organ Sharing (UNOS) data from 1990 to 1999, with one-year graft survival. The primary outcome of the analysis was graft survival beyond 10 years. We explored the long-lasting impact of HLA mismatches by landmarking the analysis at established time points. |Results: We identified 76,530 patients receiving renal transplants in the time frame, 23,914 from living donors and 52,616 from deceased donors. On multivariate analysis, more HLA mismatches were associated with worse graft survival beyond 10 years for both living and deceased donor allografts. HLA mismatch continued to remain an essential factor in the long term. Conclusions: A greater number of HLA mismatches was associated with progressively worse long-term graft survival for patients. Our analysis reinforces the importance of HLA matching in the preoperative evaluation of renal allograft

Parents’ behaviour toward antibiotic self-medication in children and incidence of resistance: a cross-sectional study from Punjab, Pakistan

Background. Antibiotic resistance is mostly brought about through antibiotic self-medication, which is a common issue in impoverished countries. The most at-risk group is children, while there is no protection evidence released for them. Due to a lack of proper knowledge, parents often inappropriately administer antibiotics to their children. Objectives. The current study aims to evaluate the parents’ knowledge of antibiotic use and their knowledge of the medical conditions for which self-medication is used. Material and methods. A cross-sectional descriptive study was carried out in parents. Parents’ direct interviews and self-administered questionnaires were used to gather the data. Descriptive analysis and chi-square tests were performed to determine the significance of these findings using IBM SPSS Statistics version 22. Results. There were 1,034 individuals who self-medicated their children in total. Male participants outnumbered female participants by a small margin. In the past 12 months, 88.6% of parents gave antibiotics to their children. Pharmacy advice and past prescriptions were the main causes of this behaviour, whilst cough, fever and tooth discomfort were the conditions for which antibiotics were prescribed. Throughout the course, 45.5% of patients changed antibiotics on their own. Conclusions. The findings of this study underscore the urgent need to address the issue of self-medication of antibiotics in children, emphasising the potential harm it can cause. Parents often resort to self-medication without a proper understanding of the underlying causes of their children’s illnesses, relying on antibiotics as a panacea. To mitigate this practice and protect the well-being of children, it is imperative to implement a multifaceted approach involving regulatory measures and educational initiatives beyond the scope of pharmacist interventions