PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines

Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. Developing tools for automated assessment of ECG sequences can improve veterinary care by providing clinicians real-time results and decision support tools. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal. ECG records are converted into 8 second Lead II sequences and classified as either normal (no evidence of cardiac abnormalities) or abnormal (presence of one or more cardiac abnormalities). For training ECG sequences are randomly augmented using RandomAugmentECG, a new augmentation library implemented specifically for this project. Each chunk is then is converted using a continuous wavelet transform into a 2D scalogram. The 2D scalogram are then classified as either normal or abnormal by a binary CNN classifier. Experimental results are validated against three boarded veterinary cardiologists achieving an AUC-ROC score of 0.9506 on test dataset matching human level performance. Additionally, we describe model deployment to Microsoft Azure using an MLOps approach. To our knowledge, this work is one of the first attempts to implement a deep learning model to automatically classify ECG sequences for canines.Implementing automated ECG classification will enhance veterinary care through improved diagnostic performance and increased clinic efficiency

Linear low-dose extrapolation for noncancer health effects is the exception, not the rule

The nature of the exposure-response relationship has a profound influence on risk analyses. Several arguments have been proffered as to why all exposure-response relationships for both cancer and noncarcinogenic end-points should be assumed to be linear at low doses. We focused on three arguments that have been put forth for noncarcinogens. First, the general “additivity-to-background” argument proposes that if an agent enhances an already existing disease-causing process, then even small exposures increase disease incidence in a linear manner. This only holds if it is related to a specific mode of action that has nonuniversal properties—properties that would not be expected for most noncancer effects. Second, the “heterogeneity in the population” argument states that variations in sensitivity among members ofthe target population tend to “flatten out and linearize” the exposure-response curve, but this actually only tends to broaden, not linearize, the dose-response relationship. Third, it has been argued that a review of epidemiological evidence shows linear or no-threshold effects at low exposures in humans, despite nonlinear exposure-response in the experimental dose range in animal testing for similar endpoints. It is more likely that this is attributable to exposure measurement error rather than a true non-threshold association. Assuming that every chemical is toxic at high exposures and linear at low exposures does not comport to modern-day scientific knowledge of biology. There is no compelling evidence-based justification for a general low-exposure linearity; rather, case-specific mechanistic arguments are needed

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

The importance of problem formulations in risk assessment: A case study involving dioxin-contaminated soil

AbstractThe need to remediate contaminated soils is typically accomplished by applying standard risk assessment methods followed by risk management to select remedial options. These human health risk assessments (HHRAs) have been largely conducted in a formulaic manner that relies heavily on standard deterministic exposure, toxicity assumptions and fixed mathematical formulas. The HHRA approach, with its traditional formulaic practice, does not take advantage of problem formulation in the same manner as is done in ecological risk assessment, and historically, has generally failed to emphasize incorporation of site-specific information. In response to these challenges, the National Academy of Sciences recently made several recommendations regarding the conduct of HHRAs, one of which was to begin all such assessments with problem formulation. These recommendations have since been extended to dose response assessment. In accordance with these recommendations, a group of experts presented and discussed findings that highlighted the importance and impact of including problem formulation when determining the need for remediation of dioxin contamination in soils, focusing in particular on exposure assessment is described

Assessing Civic Engagement and Community Development through the Kentucky League of Cities\u27 NewCities Institute

The Institute for Regional Analysis and Public Policy (IRAPP) at Morehead State University has partnered with the NewCities Institute and the NewCity Morehead Project to develop an assessment instrument to evaluate the progress that Morehead is making toward its NewCity goals. While this assessment instrument is being piloted in Morehead, IRAPP and the NewCities Institute seek to generalize it to be applicable to all cities that undertake the NewCities initiative. This poster presents the preliminary findings of this research and discusses those findings in light of the overall NewCities\u27 objectives of civic engagement and community development