Infrastructure Engineering - Research Publications
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ItemApplication of Machine Learning to Ranking Predictors of Anti-VEGF Response(MDPI, 2022-11)Age-related macular degeneration (AMD) is a heterogeneous disease affecting the macula of individuals and is a cause of irreversible vision loss. Patients with neovascular AMD (nAMD) are candidates for the anti-vascular endothelial growth factor (anti-VEGF) treatment, designed to regress the growth of abnormal blood vessels in the eye. Some patients fail to maintain vision despite treatment. This study aimed to develop a prediction model based on features weighted in order of importance with respect to their impact on visual acuity (VA). Evaluations included an assessment of clinical, lifestyle, and demographic factors from patients that were treated over a period of two years. The methods included mixed-effects and relative importance modelling, and models were tested against model selection criteria, diagnostic and assumption checks, and forecasting errors. The most important predictors of an anti-VEGF response were the baseline VA of the treated eye, the time (in weeks), treatment quantity, and the treated eye. The model also ranked the impact of other variables, such as intra-retinal fluid, haemorrhage, pigment epithelium detachment, treatment drug, baseline VA of the untreated eye, and various lifestyle and demographic factors. The results identified variables that could be targeted for further investigation in support of personalised treatments based on patient data.
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ItemEpistemic Uncertainties in the Assessment of Regional Soil Acidification(MDPI AG, 2022-08-01)The increasing acidification of soil due to pollution and agricultural management practices is a growing problem worldwide, where food production is already under threat by climate change, more frequent droughts, and soil nutrient depletion. Soil acidification is quantified by pH measurements and is a primary metric for soil health. High soil acidity is a constraint on the production of grains and other crops because it decreases the bioavailability of important plant nutrients while increasing soil toxicity arising from an imbalance of essential soil elements. Field pH can be estimated by colour test kits which are very cost-effective and particularly suitable for developing countries where laboratory services are not available or fail to provide timely results. Because the pH test kit is based on visual colour matching between a colour card scale and a soil sample in solution, there are epistemic uncertainties, such as variability in expert opinion, differences in colour vision, measurement error, instrumentation, and changes in daylight spectral content. In this study, expert human observers were compared in experiments conducted using a standard pH test kit under a range of environmental conditions. A significant difference in uncertainty in colour discrimination was evident between male and female experts, whereas changes in daylight conditions had lower impact on the variance of pH estimates. In a group of subject matter experts, the male standard error (0.35 pH) was 57% higher on average over the range of pH values (pH = 4 → 10) compared to females (0.22 pH). This error was largest (70%) in the low pH 4 to 6.5 range, which is a critical range for successful amelioration of soil acidification. The results suggest that historical database measurements may have hitherto unrecognised uncertainties that affect confidence intervals for experimental data that in turn will have an impact on predictive models and policy development.
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ItemDeep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images.(Association for Research in Vision and Ophthalmology (ARVO), 2021-07-01)PURPOSE: This study describes the development of a deep learning algorithm based on the U-Net architecture for automated segmentation of geographic atrophy (GA) lesions in fundus autofluorescence (FAF) images. METHODS: Image preprocessing and normalization by modified adaptive histogram equalization were used for image standardization to improve effectiveness of deep learning. A U-Net-based deep learning algorithm was developed and trained and tested by fivefold cross-validation using FAF images from clinical datasets. The following metrics were used for evaluating the performance for lesion segmentation in GA: dice similarity coefficient (DSC), DSC loss, sensitivity, specificity, mean absolute error (MAE), accuracy, recall, and precision. RESULTS: In total, 702 FAF images from 51 patients were analyzed. After fivefold cross-validation for lesion segmentation, the average training and validation scores were found for the most important metric, DSC (0.9874 and 0.9779), for accuracy (0.9912 and 0.9815), for sensitivity (0.9955 and 0.9928), and for specificity (0.8686 and 0.7261). Scores for testing were all similar to the validation scores. The algorithm segmented GA lesions six times more quickly than human performance. CONCLUSIONS: The deep learning algorithm can be implemented using clinical data with a very high level of performance for lesion segmentation. Automation of diagnostics for GA assessment has the potential to provide savings with respect to patient visit duration, operational cost and measurement reliability in routine GA assessments. TRANSLATIONAL RELEVANCE: A deep learning algorithm based on the U-Net architecture and image preprocessing appears to be suitable for automated segmentation of GA lesions on clinical data, producing fast and accurate results.
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ItemArtificial Intelligence and Telehealth may Provide Early Warning of Epidemics(FRONTIERS MEDIA SA, 2021)The COVID-19 pandemic produced a very sudden and serious impact on public health around the world, greatly adding to the burden of overloaded professionals and national medical systems. Recent medical research has demonstrated the value of using online systems to predict emerging spatial distributions of transmittable diseases. Concerned internet users often resort to online sources in an effort to explain their medical symptoms. This raises the prospect that incidence of COVID-19 may be tracked online by search queries and social media posts analyzed by advanced methods in data science, such as Artificial Intelligence. Online queries can provide early warning of an impending epidemic, which is valuable information needed to support planning timely interventions. Identification of the location of clusters geographically helps to support containment measures by providing information for decision-making and modeling.
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ItemProgression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning(ASSOC RESEARCH VISION OPHTHALMOLOGY INC, 2021-11)PURPOSE: The purpose of this study was to identify a taxonomy of epistemic uncertainties that affect results for geographic atrophy (GA) assessment and progression. METHODS: An important source of variability is called "epistemic uncertainty," which is due to incomplete system knowledge (i.e. limitations in measurement devices, artifacts, and human subjective evaluation, including annotation errors). In this study, different epistemic uncertainties affecting the analysis of GA were identified and organized into a taxonomy. The uncertainties were discussed and analyzed, and an example was provided in the case of model structure uncertainty by characterizing progression of GA by mathematical modelling and machine learning. It was hypothesized that GA growth follows a logistic (sigmoidal) function. Using case studies, the GA growth data were used to test the sigmoidal hypothesis. RESULTS: Epistemic uncertainties were identified, including measurement error (imperfect outcomes from measuring tools), subjective judgment (grading affected by grader's vision and experience), model input uncertainties (data corruption or entry errors), and model structure uncertainties (elucidating the right progression pattern). Using GA growth data from case studies, it was demonstrated that GA growth can be represented by a sigmoidal function, where growth eventually approaches an upper limit. CONCLUSION: Epistemic uncertainties contribute to errors in study results and are reducible if identified and addressed. By prior identification of epistemic uncertainties, it is possible to (a) quantify uncertainty not accounted for by natural statistical variability, and (b) reduce the presence of these uncertainties in future studies. TRANSLATIONAL RELEVANCE: Lowering epistemic uncertainty will reduce experimental error, improve consistency and reproducibility, and increase confidence in diagnostics.
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ItemNo Preview AvailableDevelopment of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content(Elsevier, 2020-05-01)The pedotransfer function is a mathematical model used to convert direct soil measurements into known and unknown soil properties. It provides information for modelling and simulation in soil research, hydrology, environmental science and climate change impacts, including investigating the carbon cycle and the exchange of carbon between soils and the atmosphere to support carbon farming. In particular, the pedotransfer function can provide input parameters for landscape design, soil quality assessment and economic optimisation. The objective of the study was to investigate the feasibility of using a generalised pedotransfer function derived with a machine learning method to predict soil electrical conductivity (EC) and soil organic carbon content (OC) for different regional locations in the state of Victoria, Australia. This strategy supports a unified approach to the interpolation and population of a single regional soils database, in contrast to a range of pedotransfer functions derived from local databases with measurement sets that may have limited transferability. The pedotransfer function generation was based on a machine learning algorithm incorporating the Generalized Linear Mixed Model with interactions and nested terms, with Residual Maximum Likelihood estimation, and a predictor-frequency ranking system with step-wise reduction of predictors to evaluate the predictive errors in reduced models. The source of the data was the Victorian Soil Information System (VSIS), which is a database administered for soil information and mapping purposes. The database contains soil measurements and information from locations across Victoria and is a repository of historical data, including monitoring studies. In total, data from 93 projects were available for inputs to modelling and analysis, with 5158 samples used to derive predictors for EC and 1954 samples used to derive predictors for OC. Over 500 models were tested by systematically reducing the number of predictors from the full model. Five-fold cross-validation was used for estimation of model mean-squared prediction error (MSPE) and mean-absolute percentage error (MAPE). The results were statistically significant with only a gradual reduction in error for the top-ranked 50 models. The prediction errors (MSPE and MAPE) of the top ranked model for EC are 0.686 and 0.635, and 0.413 and 0.474 for OC respectively. The four most frequently occurring predictors both for EC and OC prediction across the full set of models were found to be soil depth, pH, particle size distribution and geomorphological mapping unit. The possible advantages and disadvantages of this approach were discussed with respect to other machine learning approaches.
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ItemRadiofrequency electromagnetic field exposure assessment: a pilot study on mobile phone signal strength and transmitted power levels(Nature Publishing Group, 2021-02-01)In many epidemiological studies mobile phone use has been used as an exposure proxy for radiofrequency electromagnetic field (RF-EMF) exposure. However, RF-EMF exposure assessment from mobile phone use is prone to measurement errors limiting epidemiological research. An often-overlooked aspect is received signal strength levels from base stations and its correlation with mobile phone transmit (Tx) power. The Qualipoc android phone is a tool that provides information on both signal strength and Tx power. The phone produces simultaneous measurements of Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), Received Signal Code Power (RSCP), and Tx power on the 3G and 4G networks. Measurements taken in the greater Melbourne area found a wide range of signal strength levels. The correlations between multiple signal strength indicators and Tx power were assessed with strong negative correlations found for 3G and 4G data technologies (3G RSSI −0.93, RSCP −0.93; 4G RSSI −0.85, RSRP −0.87). Variations in Tx power over categorical levels of signal strength were quantified and showed large increases in Tx power as signal level decreased. Future epidemiological studies should control for signal strength or factors influencing signal strength to reduce RF-EMF exposure measurement error.
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ItemIntegrating crop modelling and production economics to investigate multiple nutrient deficiencies and yield gaps(Wiley, 2020-07)A method is described for integrating crop modelling and production economics to quantify optimum applications of multiple nutrients and yield gaps. The method is demonstrated for crop production in the high‐rainfall zone of southern Australia. Data from a biophysical crop model were used to overcome the persistent problem of inadequate experimental data. The Mitscherlich function was expanded to accommodate four variable inputs – nitrogen, phosphorus, potassium and sulphur – and the expansion path was used to determine the economic optimum application of all four nutrients. Modelling revealed the state‐contingent yield potential and the extent to which unrealised yield could be explained by profit‐maximising behaviour and risk‐aversion by growers. If growers and their advisors were guided by the methods described, they would be better equipped to assess crop nutrient demands and limitations, predict yield potential, additional profit and the risks associated with high input systems in a variable climate. If scientists were more aware of the extra profits and the risks involved (as well as the quantitative relationships between inputs and outputs) when thinking about what to produce and how to do so, they would be more circumspect about the net benefits to be obtained from closing yield gaps.