Background Single pellet reaching is an established task for studying fine motor control in which rats reach for, grasp, and eat food pellets in a stereotyped sequence. Most incarnations of this task require constant attention, limiting the number of animals that can be tested and the number of trials per session. Automated versions allow more interventions in more animals, but must be robust and reproducible. New Method Our system automatically delivers single reward pellets for rats to grasp with their forepaw. Reaches are detected using real-time computer vision, which triggers video acquisition from multiple angles using mirrors. This allows us to record high-speed (>300 frames per second) video, and trigger interventions (e.g., optogenetics) with high temporal precision. Individual video frames are triggered by digital pulses that can be synchronized with behavior, experimental interventions, or recording devices (e.g., electrophysiology). The system is housed within a soundproof chamber with integrated lighting and ventilation, allowing multiple skilled reaching systems in one room. Results We show that rats acquire the automated task similarly to manual versions, that the task is robust, and can be synchronized with optogenetic interventions. Comparison with existing methods Existing skilled reaching protocols require high levels of investigator involvement, or, if ad libitum, do not allow for integration of high-speed, synchronized data collection. Conclusion This task will facilitate the study of motor learning and control by efficiently recording large numbers of skilled movements. It can be adapted for use with modern neurophysiology, which demands high temporal precision.
Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.
BackgroundStill's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists.ObjectivesTo develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral.MethodsThe study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm.ResultsA comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943.ConclusionsStill's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.
screen (from those with history of in-utero exposure) were significant predictors for onset of NAS requiring pharmacological intervention at >96 HOL (OR 0.21; p value 0.011). Conclusions The majority of infants who required pharmacological treatment for NAS during their postnatal observation period were diagnosed within the first 120 HOL. Those atrisk infants, born to mothers with a known history of exposure, who have a negative urine toxicology screen for both baby and mother, should be monitored beyond 5 days as they tend to have a later presentation.
Introduction: Still’s murmurs are difficult to distinguish from other heart murmurs at the primary care level. This leads to an estimated 400,000 children being referred to pediatric cardiologists each year for evaluation of heart murmurs in the United States. The murmur is ultimately diagnosed as innocent Still’s murmur in approximately 90% of these children. This diagnosis requires no specialty referral, cardiac testing, exercise restrictions or cardiology follow-up. These unnecessary referrals and associated tests waste healthcare resources, add to healthcare costs, and are a source of avoidable anxiety in children and families while waiting to see a pediatric cardiologist. Hypothesis: We have developed a novel mobile technology to discriminate Still’s murmur from all other murmurs. A smartphone application (app) that could quickly distinguish Still’s murmur from all other murmurs with high accuracy at the point of care could reassure pediatricians in their identification of Still’s murmur, significantly reducing the current rate of unnecessary referrals to cardiologists. Methods: Our current prototype is StethAid, a smartphone app which accepts heart sound recordings, and a cloud-based deep learning algorithm for discriminating Still’s murmur from other murmurs. The algorithm was independently developed and evaluated using archived heart sounds, recorded by one of the authors (RWD), made using an electronic stethoscope (3M Littmann Model 4100). Results: Our algorithm offers a sensitivity (Still’s murmurs correctly identified) of 90% with a specificity (Non-Still’s murmurs correctly identified) of 99% on 5-fold cross-validation over the Murmur Library. The area under the curve was 0.99446. The algorithm’s result is available in real time at the point-of-care (< 1 min). Conclusions: The described point-of-care mobile technology could automatically distinguish Still’s murmur from all other murmurs with high accuracy. The technology could lower the current high rate of specialist referrals associated with Still’s murmur and reduce the related financial and emotional costs. Future directions will include further improvement of the technology and validation through multi-center clinical trials.
Primary care physicians (PCPs) often lack the skills to distinguish the common innocent Still’s murmur from far less frequent but potentially serious pathological heart murmurs. This leads to approximately 800,000 children being referred to pediatric cardiologists each year for evaluation of heart murmurs in the United States [1–2]. The murmur is ultimately diagnosed as an innocent Still’s murmur in approximately 78% of these children (Children’s National Health System data). These unnecessary referrals and associated tests cost the healthcare system over half a billion annually, and are a source of avoidable anxiety for children and families while waiting to see a pediatric cardiologist.
Auscultation is a critical component of a cardiopulmonary examination; however, several studies have shown that many physicians lack proficiency in it. With the development of artificial intelligence (AI)-augmented auscultation tools, physicians could improve their auscultation skills and provide accurate diagnoses, even approaching the expertise of seasoned clinicians. Although a few AI auscultation platforms have been created, none have been adopted widely in clinical settings. Our goal is to develop a comprehensive digital auscultation platform, termed StethAid, to supplement the value of auscultation. StethAid is a comprehensive auscultation platform that comprises of an electronic stethoscope, mobile applications, and website portals. The StethAid stethoscope enables streaming and recording of heart and lungs sounds. It features 100 levels of sound amplification, digital filtering, active noise cancellation, and wireless connectivity. The StethAid stethoscope is similar to FDA-approved stethoscopes in its frequency response. Our mobile apps deliver various auscultation tools, as well as an AI suite and remote auscultation capabilities. The AI suite includes deep learning-based automated wheeze detection from auscultated lungs sounds. StethAid has been rigorously validated technically and clinically. StethAid could assist physicians in making more informed decisions, potentially leading to improved patient outcomes.
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