Machine Learning Approaches for Automated Glaucoma Detection using Clinical Data and Optical Coherence Tomography Images

Glaucoma is a multi-factorial, progressive blinding optic-neuropathy. A variety of factors, including genetics, vasculature, anatomy, and immune factors, are involved. Worldwide more than 80 million people are affected by glaucoma, and around 300,000 in Australia, where 50% remain undiagnosed. Untreated glaucoma can lead to blindness. Early detection by Artificial intelligence (AI) is crucial to accelerate the diagnosis process and can prevent further vision loss. Many proposed AI systems have shown promising performance for automated glaucoma detection using two-dimensional (2D) data. However, only a few studies had optimistic outcomes for glaucoma detection and staging. Moreover, the automated AI system still faces challenges in diagnosing at the clinicians’ level due to the lack of interpretability of the ML algorithms and integration of multiple clinical data. AI technology would be welcomed by doctors and patients if the "black box" notion is overcome by developing an explainable, transparent AI system with similar pathological markers used by clinicians as the sign of early detection and progression of glaucomatous damage. Therefore, the thesis aimed to develop a comprehensive AI model to detect and stage glaucoma by incorporating a variety of clinical data and utilising advanced data analysis and machine learning (ML) techniques. The research first focuses on optimising glaucoma diagnostic features by combining structural, functional, demographic, risk factor, and optical coherence tomography (OCT) features. The significant features were evaluated using statistical analysis and trained in ML algorithms to observe the detection performance. Three crucial structural ONH OCT features: cross-sectional 2D radial B-scan, 3D vascular angiography and temporal-superior-nasal-inferior-temporal (TSNIT) B-scan, were analysed and trained in explainable deep learning (DL) models for automated glaucoma prediction. The explanation behind the decision making of DL models were successfully demonstrated using the feature visualisation. The structural features or distinguished affected regions of TSNIT OCT scans were precisely localised for glaucoma patients. This is consistent with the concept of explainable DL, which refers to the idea of making the decision-making processes of DL models transparent and interpretable to humans. However, artifacts and speckle noise often result in misinterpretation of the TSNIT OCT scans. This research also developed an automated DL model to remove the artifacts and noise from the OCT scans, facilitating error-free retinal layers segmentation, accurate tissue thickness estimation and image interpretation. Moreover, to monitor and grade glaucoma severity, the visual field (VF) test is commonly followed by clinicians for treatment and management. Therefore, this research uses the functional features extracted from VF images to train ML algorithms for staging glaucoma from early to advanced/severe stages. Finally, the selected significant features were used to design and develop a comprehensive AI model to detect and grade glaucoma stages based on the data quantity and availability. In the first stage, a DL model was trained with TSNIT OCT scans, and its output was combined with significant structural and functional features and trained in ML models. The best-performed ML model achieved an area under the curve (AUC): 0.98, an accuracy of 97.2%, a sensitivity of 97.9%, and a specificity of 96.4% for detecting glaucoma. The model achieved an overall accuracy of 90.7% and an F1 score of 84.0% for classifying normal, early, moderate, and advanced-stage glaucoma. In conclusion, this thesis developed and proposed a comprehensive, evidence-based AI model that will solve the screening problem for large populations and relieve experts from manually analysing a slew of patient data and associated misinterpretation problems. Moreover, this thesis demonstrated three structural OCT features that could be added as excellent diagnostic markers for precise glaucoma diagnosis

Difference-Masking: Choosing What to Mask in Continued Pretraining

The self-supervised objective of masking-and-predicting has led to promising performance gains on a variety of downstream tasks. However, while most approaches randomly mask tokens, there is strong intuition that deciding what to mask can substantially improve learning outcomes. We investigate this in continued pretraining setting in which pretrained models continue to pretrain on domain-specific data before performing some downstream task. We introduce Difference-Masking, a masking strategy that automatically chooses what to mask during continued pretraining by considering what makes a task domain different from the pretraining domain. Empirically, we find that Difference-Masking outperforms baselines on continued pretraining settings across four diverse language-only and multimodal video tasks

The structural basis for high affinity binding of α1-acid glycoprotein to the potent antitumor compound UCN-01

The α1-acid glycoprotein (AGP) is an abundant blood plasma protein with important immunomodulatory functions coupled to endogenous and exogenous ligand-binding properties. Its affinity for many drug-like structures, however, means AGP can have a significant effect on the pharmokinetics and pharmacodynamics of numerous small molecule therapeutics. Staurosporine, and its hydroxylated forms UCN-01 and UCN-02, are kinase inhibitors that have been investigated at length as antitumour compounds. Despite their potency, these compounds display poor pharmokinetics due to binding to both AGP variants, AGP1 and AGP2. The recent renewed interest in UCN-01 as a cytostatic protective agent prompted us to solve the structure of the AGP2–UCN-01 complex by X-ray crystallography, revealing for the first time the precise binding mode of UCN-01. The solution NMR suggests AGP2 undergoes a significant conformational change upon ligand binding, but also that it uses a common set of sidechains with which it captures key groups of UCN-01 and other small molecule ligands. We anticipate that this structure and the supporting NMR data will facilitate rational redesign of small molecules that could evade AGP and therefore improve tissue distribution

COVID-19 related anxiety and its associated factors : a cross-sectional study on older adults in Bangladesh

Background: The COVID-19 pandemic has resulted in serious mental health conditions, particularly among older adults. This research explored the prevalence of COVID-19-related anxiety and its associated factors among older adults residing in Bangladesh. Methods: This cross-sectional study was conducted among 1,045 older Bangladeshi adults aged≥60 years through telephone interviews in September 2021. A semi-structured interview schedule was used to collect data on participants’ characteristics and COVID-19-related anxiety. The anxiety level was measured using the Bengali version of the five-point Coronavirus Anxiety Scale (CAS). A linear regression model explored the factors associated with COVID-19-related anxiety. Results: Overall, the prevalence of COVID-19-related anxiety was 23.2%. The regression analysis revealed that the average COVID-19-related anxiety score was significantly higher among females (β: 0.43, 95% CI: 0.05 to 0.81), and among those who faced difficulty getting medicine (β: 0.57, 95% CI: 0.16 to 0.97), felt isolated (β: 0.60, 95% CI: 0.24 to 0.95), and felt requiring additional care during the pandemic (β: 0.53, 95% CI: 0.16 to 0.91). Alternatively, the average COVID-19-related anxiety score was significantly lower among those who were widowed (β: -0.46, 95% CI: -0.87 to -0.04) and living distant from the health centre (β: -0.48, 95% CI: -0.79 to -0.17). Conclusion: The findings of the present study suggest providing immediate psychosocial support package to the older adults, particularly females and those who are vulnerable to receive health and social care support during the COVID-19 pandemic in Bangladesh

Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution

In this work, the financial data of 377 stocks of Standard & Poor’s 500 Index (S&P 500) from the years 1998–2012 with a 250-day time window were investigated by measuring realized stock returns and realized volatility. We examined the normal distribution and frequency distribution for both daily stock returns and volatility. We also determined the beta-coefficient and correlation among the stocks for 15 years and found that, during the crisis period, the beta-coefficient between the market index and stock’s prices and correlation among stock’s prices increased remarkably and decreased during the non-crisis period. We compared the stock volatility and stock returns for specific time periods i.e., non-crisis, before crisis and during crisis year in detail and found that the distribution behaviors of stock return prices has a better long-term effect that allows predictions of near-future market behavior than realized volatility of stock returns. Our detailed statistical analysis provides a valuable guideline for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods