Assessment of uncertainty estimation for measurement of ethanol concentration in COVID-19 hand sanitizer using FTIR spectroscopy

Analysis of uncertainty estimation for measurement of type and concentration of alcohol in hand sanitisers is a matter of urgency in the COVID-19 situation. FTIR spectroscopy was used to investigate hand sanitisers made in our laboratory and commercial products. An internal standard addition method was used to control the measurement quality. The absorption spectra of ethanol were found to be at 1086 and 1044 cm-1, corresponding to C-O stretching. The area under the C-O adsorptions is used to create a calibration curve, which is then used to calculate the ethanol percentage. Additional standard sample and quality control sample showed calibration curves with slopes of 0.1267 and 0.1285, respectively. The regression coefficients and residual variance of 0.0057 showed a 'best fit' with the predicted value. These parameters were used to estimate the uncertainty of six commercial products. The ethanol concentration of commercial products is measured between 71.38 and 81.54% v/v, with an estimated uncertainty of 1.14% v/v. The results showed that the ethanol content of all products differed from the label but could be used to kill bacteria and viruses. This entire process was established as a SOP for measuring alcohol concentration in hand sanitizer.

From Prediction to Prescription: Evolutionary Optimization of Nonpharmaceutical Interventions in the COVID-19 Pandemic.

Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with nonpharmaceutical interventions, such as social distancing restrictions and school and business closures. This article demonstrates how evolutionary AI can be used to facilitate the next step, i.e., determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription, it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. Early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. They also demonstrate that results of lifting restrictions can be unreliable, and suggest creative ways in which restrictions can be implemented softly, e.g., by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.

Reducing False-Positive Detections Using the Distance Between Activation Distributions in Individual Channels

We propose a novel method to estimate the confidence of outputted predictions of a convolutional neural network. We show that different channels in one layer can be treated as an ensemble and extract the confidence of a prediction from a single channel. To achieve this, we compute statistical distances between activation distributions located at the predicted mask and its surrounding area and aggregate it across all channels in a deep layer of a network. Research on a segmentation network of lung cancer nodules from 3d computer tomography images has shown growth of precision compared to the thresholding output network values. The more layers used to compute confidence, the better performance obtained, allowing for up to 18% fewer false-positives detections on the source Cancer dataset and up to 54% fewer false-positives detections on an unseen Covid dataset. Analyzing channel activations doesn't require any changes in the training procedure with a negligible amount of additional computations at the inference time. © 2022 IEEE.

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.

Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.

Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification.

Deep learning (DL) has shown great success in the field of medical image analysis. In the wake of the current pandemic situation of SARS-CoV-2, a few pioneering works based on DL have made significant progress in automated screening of COVID-19 disease from the chest X-ray (CXR) images. But these DL models have no inherent way of expressing uncertainty associated with the model's prediction, which is very important in medical image analysis. Therefore, in this paper, we develop an uncertainty-aware convolutional neural network model, named UA-ConvNet, for the automated detection of COVID-19 disease from CXR images, with an estimation of associated uncertainty in the model's predictions. The proposed approach utilizes the EfficientNet-B3 model and Monte Carlo (MC) dropout, where an EfficientNet-B3 model has been fine-tuned on the CXR images. During inference, MC dropout has been applied for M forward passes to obtain the posterior predictive distribution. After that mean and entropy have been calculated on the obtained predictive distribution to get the mean prediction and model uncertainty. The proposed method is evaluated on the three different datasets of chest X-ray images, namely the COVID19CXr, X-ray image, and Kaggle datasets. The proposed UA-ConvNet model achieves a G-mean of 98.02% (with a Confidence Interval (CI) of 97.99-98.07) and sensitivity of 98.15% for the multi-class classification task on the COVID19CXr dataset. For binary classification, the proposed model achieves a G-mean of 99.16% (with a CI of 98.81-99.19) and a sensitivity of 99.30% on the X-ray Image dataset. Our proposed approach shows its superiority over the existing methods for diagnosing the COVID-19 cases from the CXR images.

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images.

In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method.