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Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain. Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.

High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process.

Identifying physiological impairments of swallowing is essential for determining accurate diagnosis and appropriate treatment for patients with dysphagia. The hyoid bone is an anatomical landmark commonly monitored during analysis of videofluoroscopic swallow studies (VFSSs). Its displacement is predictive of penetration/aspiration and is associated with other swallow kinematic events. However, VFSSs are not always readily available/feasible and expose patients to radiation. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from a microphone and tri-axial accelerometer, is under investigation as a non-invasive dysphagia screening method and potential adjunct to VFSS when it is unavailable or not feasible. We investigated the ability of HRCA to independently track hyoid bone displacement during swallowing with similar accuracy to VFSS, by analyzing vibratory signals from a tri-axial accelerometer using machine learning techniques. We hypothesized HRCA would track hyoid bone displacement with a high degree of accuracy compared to humans. Trained judges completed frame-by-frame analysis of hyoid bone displacement on 400 swallows from 114 patients and 48 swallows from 16 age-matched healthy adults. Extracted features from vibratory signals were used to train the predictive algorithm to generate a bounding box surrounding the hyoid body on each frame. A metric of relative overlapped percentage (ROP) compared human and machine ratings. The mean ROP for all swallows analyzed was 50.75%, indicating > 50% of the bounding box containing the hyoid bone was accurately predicted in every frame. This provides evidence of the feasibility of accurate, automated hyoid bone displacement tracking using HRCA signals without use of VFSS images.

Wireless energy transfer is a broad research area that has recently become applicable to implantable medical devices. Wireless powering of and communication with implanted devices is possible through wireless transcutaneous energy transfer. However, designing wireless transcutaneous systems is complicated due to the variability of the environment. The focus of this review is on strategies to sense and adapt to environmental variations in wireless transcutaneous systems. Adaptive systems provide the ability to maintain performance in the face of both unpredictability (variation from expected parameters) and variability (changes over time). Current strategies in adaptive (or tunable) systems include sensing relevant metrics to evaluate the function of the system in its environment and adjusting control parameters according to sensed values through the use of tunable components. Some challenges of applying adaptive designs to implantable devices are challenges common to all implantable devices, including size and power reduction on the implant, efficiency of power transfer and safety related to energy absorption in tissue. Challenges specifically associated with adaptation include choosing relevant and accessible parameters to sense and adjust, minimizing the tuning time and complexity of control, utilizing feedback from the implanted device and coordinating adaptation at the transmitter and receiver.

The displacement of the hyoid bone is one of the key components evaluated in the swallow study, as its motion during swallowing is related to overall swallowing integrity. In daily research settings, experts visually detect the hyoid bone in the video frames and manually plot hyoid bone position frame by frame. This study aims to develop an automatic method to localize the location of the hyoid bone in the video sequence. To automatically detect the location of the hyoid bone in a frame, we proposed a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location of the hyoid bone. We also evaluated the performance of two other state-of-art detection methods for comparison. The experimental results clearly showed that the single shot multibox detector can detect the hyoid bone with an average precision of 89.14% and outperform other auto-detection algorithms. We conclude that this automatic hyoid bone tracking system is accurate enough to be widely applied as a pre-processing step for image processing in dysphagia research, as well as a promising development that may be useful in the diagnosis of dysphagia.

Artificial intelligence is rapidly transforming healthcare, offering promising advancements in diagnosis, treatment, and patient outcomes. However, concerns regarding the regulatory oversight of artificial intelligence driven medical technologies have emerged, particularly with the U.S. Food and Drug Administration’s current approval processes. This paper critically examines the U.S. Food and Drug Administration’s regulatory framework for artificial intelligence powered healthcare products, highlighting gaps in safety evaluations, post-market surveillance, and ethical considerations. Artificial intelligence’s continuous learning capabilities introduce unique risks, as algorithms evolve beyond their initial validation, potentially leading to performance degradation and biased outcomes. Although the U.S. Food and Drug Administration has taken steps to address these challenges, such as artificial intelligence/machine learning-based software as a medical device action plan and proposed regulatory adjustments, significant weaknesses remain, particularly in real-time monitoring, transparency and bias mitigation. This paper argues for a more adaptive, community-engaged regulatory approach that mandates extensive post-market evaluations, requires artificial intelligence developers to disclose training data sources, and establishes enforceable standards for fairness, equity, and accountability. A patient-centered regulatory framework must also integrate diverse perspectives to ensure artificial intelligence technologies serve all populations equitably. By fostering an agile, transparent, and ethics-driven oversight system, the U.S. Food and Drug Administration can balance innovation with patient safety, ensuring that artificial intelligence-driven medical technologies enhance, rather than compromise, healthcare outcomes.

Sociodemographic factors are critical determinants of health outcomes and disparities, yet their documentation in electronic medical records is often sparse and confined to unstructured clinical text. This poses substantial challenges for automated extraction and integration into clinical decision-making. In this study, we systematically evaluate and compare 6 convolutional neural network architectures, including hybrid models that integrate traditional classifiers, for binary classification of multiple sociodemographic characteristics from EMR text using data from 4375 patients across 96 primary care clinics. The goal was to assess how model complexity and lexical diversity influence classification performance. Manual annotation achieved high inter-rater reliability (kappa: 0.98 for documentation status, 0.96 for documented information). We report performance using F1 score, precision, recall, area under the precision-recall curve, and Matthews correlation coefficient. Results showed that simpler architectures, particularly a single-layer CNN, consistently outperform deeper or hybrid models across most characteristics (F1 score: 90.99%), especially under conditions of data imbalance and varied documentation patterns. While hybrid models offered gains for well-documented factors like marital status, they were less effective for sparse or diverse characteristics. These findings provide a practical framework for developing efficient, interpretable clinical NLP pipelines and inform model selection strategies for real-world health equity and EMR research applications.

There is a growing need to document sociodemographic factors in electronic medical records to produce representative cohorts for medical research and to perform focused research for potentially vulnerable populations. The objective of this work was to assess the content of family physicians’ electronic medical records and characterize the quality of the documentation of sociodemographic characteristics. Descriptive statistics were reported for each sociodemographic characteristic. The association between the completeness rates of the sociodemographic data and the various clinics, electronic medical record vendors, and physician characteristics was analyzed. Supervised machine learning models were used to determine the absence or presence of each characteristic for all adult patients over the age of 18 in the database. Documentation of marital status (51.0%) and occupation (47.2%) were significantly higher compared to the rest of the variables. Race (1.4%), sexual orientation (2.5%), and gender identity (0.8%) had the lowest documentation rates with a 97.5% missingness rate or higher. The correlation analysis for vendor type demonstrated that there was significant variation in the availability of marital and occupation information between vendors ( χ 2 > 6.0, P < 0.05). Variability in documentation between clinics indicated that the majority of characteristics exhibited high variation in completeness rates with the highest variation for occupation (median: 47.2, interquartile range: 60.6%) and marital status (median: 45.6, interquartile: 59.7%). Finally, physician sex, years since a physician graduated, and whether a physician was a foreign vs a Canadian medical graduate were significantly associated with documentation rates of place of birth, citizenship status, occupation, and education in the electronic medical records. Our findings suggest a crucial need to implement better documentation strategies for sociodemographic information in the healthcare setting. To improve completeness rates, healthcare systems should monitor, encourage, enforce, or incentivize sociodemographic data collection standards.

Sparse signals are characterized by a few nonzero coefficients in one of their transformation domains. This was the main premise in designing signal compression algorithms. Compressive sensing as a new approach employs the sparsity property as a precondition for signal recovery. Sparse signals can be fully reconstructed from a reduced set of available measurements. The description and basic definitions of sparse signals, along with the conditions for their reconstruction, are discussed in the first part of this paper. The numerous algorithms developed for the sparse signals reconstruction are divided into three classes. The first one is based on the principle of matching components. Analysis of noise and nonsparsity influence on reconstruction performance is provided. The second class of reconstruction algorithms is based on the constrained convex form of problem formulation where linear programming and regression methods can be used to find a solution. The third class of recovery algorithms is based on the Bayesian approach. Applications of the considered approaches are demonstrated through various illustrative and signal processing examples, using common transformation and observation matrices. With pseudocodes of the presented algorithms and compressive sensing principles illustrated on simple signal processing examples, this tutorial provides an inductive way through this complex field to researchers and practitioners starting from the basics of sparse signal processing up to the most recent and up-to-date methods and signal processing applications.