Automatic Contrast Generation from Contrastless Computed Tomography.

The use of contrast-enhanced computed tomography (CTCA) for detection of coronary artery disease (CAD) exposes patients to the risks of iodine contrast-agents and excessive radiation, increases scanning time and healthcare costs. Deep learning generative models have the potential to artificially create a pseudo-enhanced image from non-contrast computed tomography (CT) scans.In this work, two specific models of generative adversarial networks (GANs) - the Pix2Pix-GAN and the Cycle-GAN - were tested with paired non-contrasted CT and CTCA scans from a private and public dataset. Furthermore, an exploratory analysis of the trade-off of using 2D and 3D inputs and architectures was performed. Using only the Structural Similarity Index Measure (SSIM) and the Peak Signal-to-Noise Ratio (PSNR), it could be concluded that the Pix2Pix-GAN using 2D data reached better results with 0.492 SSIM and 16.375 dB PSNR. However, visual analysis of the output shows significant blur in the generated images, which is not the case for the Cycle-GAN models. This behavior can be captured by the evaluation of the Fréchet Inception Distance (FID), that represents a fundamental performance metric that is usually not considered by related works in the literature.Clinical relevance- Contrast-enhanced computed tomography is the first line imaging modality to detect CAD resulting in unnecessary exposition to the risk of iodine contrast and radiation in particularly in young patients with no disease. This algorithm has the potential of being translated into clinical practice as a screening method for CAD in asymptomatic subjects or quick rule-out method of CAD in the acute setting or centres with no CTCA service. This strategy can eventually represent a reduction in the need for CTCA reducing its burden and associated costs.

Markov-Based Neural Networks for Heart Sound Segmentation: Using Domain Knowledge in a Principled Way.

This work considers the problem of segmenting heart sounds into their fundamental components. We unify statistical and data-driven solutions by introducing Markov-based Neural Networks (MNNs), a hybrid end-to-end framework that exploits Markov models as statistical inductive biases for an Artificial Neural Network (ANN) discriminator. We show that an MNN leveraging a simple one-dimensional Convolutional ANN significantly outperforms two recent purely data-driven solutions for this task in two publicly available datasets: PhysioNet 2016 (Sensitivity: 0.947 ±0.02; Positive Predictive Value : 0.937 ±0.025) and the CirCor DigiScope 2022 (Sensitivity: 0.950 ±0.008; Positive Predictive Value: 0.943 ±0.012). We also propose a novel gradient-based unsupervised learning algorithm that effectively makes the MNN adaptive to unseen datum sampled from unknown distributions. We perform a cross dataset analysis and show that an MNN pre-trained in the CirCor DigiScope 2022 can benefit from an average improvement of 3.90% Positive Predictive Value on unseen observations from the PhysioNet 2016 dataset using this method.

Inequality in health insurance coverage before and after the Affordable Care Act.

This study examines how the Affordable Care Act (ACA) affected income related inequality in health insurance coverage in the United States. Analyzing data from the American Community Survey (ACS) from 2010 through 2018, we apply difference-in-differences, and triple-differences estimation to the Recentered Influence Function OLS estimation. We find that the ACA reduced inequality in health insurance coverage in the United States. Most of this reduction was a result of the Medicaid expansion. Additional decomposition analysis shows there was little change in inequality of coverage through an employer plan, and a decrease in inequality for coverage through direct purchase of health insurance. These results indicate that the insurance exchanges also contributed to declining inequality in health insurance coverage.

On instabilities of deep learning in image reconstruction and the potential costs of AI.

Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction. The instabilities usually occur in several forms: 1) Certain tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction; 2) a small structural change, for example, a tumor, may not be captured in the reconstructed image; and 3) (a counterintuitive type of instability) more samples may yield poorer performance. Our stability test with algorithms and easy-to-use software detects the instability phenomena. The test is aimed at researchers, to test their networks for instabilities, and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.

Deep Convolutional Neural Networks for Heart Sound Segmentation.

This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. Different temporal modeling schemes are applied to the output of the proposed neural network, which induce the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). In particular, convolutional neural networks are used in conjunction with underlying hidden Markov models and hidden semi-Markov models to infer emission distributions. The proposed approaches are tested on heart sound signals from the publicly available PhysioNet dataset, and they are shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.9% and an average positive predictive value of 94% in detecting S1 and S2 sounds.

Assessment of Sound Features for Needle Perforation Event Detection.

This paper studies the use of non-invasive acoustic emission recordings for clinical device tracking. In particular, audio signals recorded at the proximal end of a needle are used to detect perforation events that occur when the needle tip crosses internal tissue layers.A comparative study is performed to assess the capacity of different features and envelopes in detecting perforation events. The results obtained from the considered experimental setup show a statistically significant correlation between the extracted envelopes and the perforation events, thus leading the way for future development of perforation detection algorithms.

Adaptive Sojourn Time HSMM for Heart Sound Segmentation.

Heart sounds are difficult to interpret due to events with very short temporal onset between them (tens of milliseconds) and dominant frequencies that are out of the human audible spectrum. Computer-assisted decision systems may help but they require robust signal processing algorithms. In this paper, we propose a new algorithm for heart sound segmentation using a hidden semi-Markov model. The proposed algorithm infers more suitable sojourn time parameters than those currently suggested by the state of the art, through a maximum likelihood approach. We test our approach over three different datasets, including the publicly available PhysioNet and Pascal datasets. We also release a pediatric dataset composed of 29 heart sounds. In contrast with any other dataset available online, the annotations of the heart sounds in the released dataset contain information about the beginning and the ending of each heart sound event. Annotations were made by two cardiopulmonologists. The proposed algorithm is compared with the current state of the art. The results show a significant increase in segmentation performance, regardless the dataset or the methodology presented. For example, when using the PhysioNet dataset to train and to evaluate the HSMMs, our algorithm achieved average an F-score of [Formula: see text] compared to [Formula: see text] achieved by the algorithm described in [D.B. Springer, L. Tarassenko, and G. D. Clifford, "Logistic regressionHSMM-based heart sound segmentation," IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 822-832, 2016]. In this sense, the proposed approach to adapt sojourn time parameters represents an effective solution for heart sound segmentation problems, even when the training data does not perfectly express the variability of the testing data.

Out of Pocket Costs and Health Insurance Take-Up Rates.

BACKGROUND: Over the first ten years of this century, the share of the US population covered by employer-sponsored health insurance plans experienced a significant decline. A decrease in the take-up rate accounts for about a quarter of this decline. Usually, the increasing share of the premium that is paid by workers is used to explain the decline in the take-up rate. However, in recent years the increase in copayments, deductible and coinsurance rate has far outpaced the increase in worker contribution. OBJECTIVE: In this study we analyze the impact of out-of-pocket (OOP) costs, which consist of both workers' contribution toward the premium and expected expenditures, on the take-up rate for firms that offer multiple plan types. METHODS: Using data from the Employer Health Benefits Survey we estimated a pooled ordinary least squares and a fixed effects model. Since we have information about different types of health insurance plans offered by the firm, we derive the cross-price elasticity of coverage. RESULTS: Our fixed effects estimations suggest that workers respond to an increase in the out-of-pocket contributions for Health Maintenance Organization (HMO) plans by switching to PPO plans without impacting the overall take-up rate, while workers respond to increases in the out-of-pocket contribution for Preferred Provider Organization (PPO) plans by switching to HMO plans or dropping out of the group coverage. CONCLUSION: In general, we found that the estimated elasticities are too small to explain the overall drop in take-up rates even in light of the large increases in required worker contributions and expected expenditures. Still, we highlight the growing importance of expected expenditures in explaining take-up rates.

Plan choice, health insurance cost and premium sharing.

We develop a model of premium sharing for firms that offer multiple insurance plans. We assume that firms offer one low quality plan and one high quality plan. Under the assumption of wage rigidities we found that the employee's contribution to each plan is an increasing function of that plan's premium. The effect of the other plan's premium is ambiguous. We test our hypothesis using data from the Employer Health Benefit Survey. Restricting the analysis to firms that offer both HMO and PPO plans, we measure the amount of the premium passed on to employees in response to a change in both premiums. We find evidence of large and positive effects of the increase in the plan's premium on the amount of the premium passed on to employees. The effect of the alternative plan's premium is negative but statistically significant only for the PPO plans.

The economic cost of teen drinking: late graduation and lowered earnings.

This paper analyzes the effect that binge drinking has on the probability of graduating on time from high school and on future earnings. The analysis is conducted on students in their senior year of high school using data from the National Longitudinal Survey of Youth 1979. Importantly, the usual instruments used to correct for the endogeneity of the drinking variable are found to be robust only for women. This paper finds that heavy drinking decreases the probability of graduating on time. Binge drinking does not have a direct impact on adults' labor earnings, but graduating late results in lower labor income. Because of a late graduation, young men who binge in high school will face an earnings penalty of 1.5-1.84 percentage points. Women also face a penalty, but this seems mostly due to the fact that women who graduate late work in industries and occupations that pay less.