Shivaram, Suganti

Abstract Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Introduction: Heart failure with preserved ejection fraction (HFpEF) is an increasingly common clinical syndrome with diagnostic challenges and no effective treatment. The high heterogeneity of HFpEF clinical syndrome demands novel technologies to distinguish from heart failure with reduced ejection Fraction (HFrEF). Currently, invasive hemodynamic exercise testing is performed to evaluate for HFpEF with right heart catheterization. Hypothesis: We hypothesize that a deep learning model can noninvasively detect patients with HFpEF using phonocardiogram (PCG). Methods: Eligible patients undergoing right heart catheterization were recruited after IRB approval. Eko duo stethoscope was used to record simultaneous ECG & PCG at the mitral area on the patients prior to right heart catheterization at baseline. Each recording was 30 seconds in length with a frequency range between 250 - 5000 Hz. R-wave peak detection was performed & beat to beat PCG annotations were performed. The annotated PCG signals were converted into Mel-frequency cepstral coefficients (MFCC) and padded to account for the difference in lengths. Deep Learning model was developed using a Sequential Neural Network with 80% data to train and validate the model and 20% hold out data was used for testing accuracy of the model. Receiver operating curve (ROC) was obtained to estimate area under the curve (AUC) for performance analysis. Results: 42 patients were enrolled with 25 patients diagnosed as HFpEF by right heart catheterization and 17 were reported as controls. 2544 & 1771 individual PCG annotations for HFpEF and controls were used for model training/validation and testing. The model performed reasonably well with accuracy of 0.89, F1 score of 0.88, precision of 0.80 and recall of 0.97 on the test data. ROC was obtained with AUC of 0.94. Conclusions: The results demonstrate the ability of the deep learning model to noninvasively detect HFpEF using PCG. Larger study is needed to validate these findings.

Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care.

Abstract Phonocardiogram (PCG) signals are electrical recording of heart sounds containing vital information of diagnostic importance. Several signal processing methods exist to characterize PCG, however suffers in terms of sensitivity and specificity in accurately discriminating normal and abnormal heart sounds. Recently, a multiscale frequency (MSF) analysis of normal PCG was reported to characterize subtle frequency content changes in PCG which can aid in differentiating normal and abnormal heart sounds. In this work, it was hypothesized that MSF can discriminate normal PCG signal compared to an artifact, PCG with extra systolic heart sounds and murmur based on their varying frequency content. Various samples of PCG with normal and abnormal heart sounds were obtained from Peter Bentley Heart Sounds Database sampled at 44.1 kHz for analysis. The signal was filtered using a 4th order Butterworth lowpass filter with cutoff frequency at 200 Hz to remove higher frequency noise and MSF estimation was performed on the filtered dataset using custom MATLAB software. Mann-Whitney test was performed for statistical significance at p < 0.05. Results indicate that MSF successfully discriminated normal and abnormal heart sounds, which can aid in PCG classification with more sophisticated analysis. Validation of this technique with larger dataset is required.

Phonocardiogram (PCG) signals contain very important information regarding the heart condition. Recently, several automatic detection algorithms have been explored to profile the characteristics of heart sounds to aid in disease diagnosis. However, many of these methods has been demonstrated only on clean signals with limited test data and variety of PCG signals that can accurately provide information of diagnostic importance with higher sensitivity and specificity. In this work, we propose to characterize the multiscale frequency state of the normal PCG signals that can aid in accurate profiling of PCG to discriminate from pathological conditions.

Sleep apnea is characterized by abnormal interruptions in breathing during sleep due to partial or complete airway obstructions affecting middle-aged men and women on an estimated ∼4% of the population [1]. While the disorder is clinically manageable to relieve patients, the challenge occurs with diagnosis, with many patients going undiagnosed leading to further complications such as ischemic heart diseases, stroke etc. Sleep apnea also significantly affects the quality of day to day life causing sleepiness and fatigue. Polysomnography (PSG) technique is currently a used for detecting sleep apnea which is a comprehensive sleep test to diagnose sleep disorders by recording brain waves, the oxygen level in the blood, heart rate, breathing, eye and leg movements during the study. However, PSG test is very expensive, requires patients to stay overnight and is known to cause inconvenience to the patients.