StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.

Heart sound segmentation has been shown to improve the performance of artificial intelligence (AI)-based auscultation decision support systems increasingly viewed as a solution to compensate for eroding auscultatory skills and the associated subjectivity. Various segmentation approaches with demonstrated performance can be utilized for this task, but their robustness can suffer in the presence of noise. A noise-robust heart sound segmentation algorithm was developed and its accuracy was tested using two datasets: the CirCor DigiScope Phonocardiogram dataset and an in-house dataset - a heart murmur library collected at the Children's National Hospital (CNH). On the CirCor dataset, our segmentation algorithm marked the boundaries of the primary heart sounds S1 and S2 with an accuracy of 0.28 ms and 0.29 ms, respectively, and correctly identified the actual positive segments with a sensitivity of 97.44%. The algorithm also executed four times faster than a logistic regression hidden semi-Markov model. On the CNH dataset, the algorithm succeeded in 87.4% cases, achieving a 6% increase in segmentation success rate demonstrated by our original Shannon energy-based algorithm. Accurate heart sound segmentation is critical to supporting and accelerating AI research in cardiovascular diseases. The proposed algorithm increases the robustness of heart sound segmentation to noise and viability for clinical use.

Effects of adaptive duration to salinity in drinking water on behavior, weight gain and blood biochemical parameters in growing goats.

This experiment aimed to determine the effect of adaptive duration to saline water on behaviors, weight gain and blood biochemical parameters in growing goats. The experiment was arranged in a completely randomized design, which included four treatments with five animals per group. The goats were administered either fresh water (control) or seawater with a salinity of 1.5%, with varying durations of adaptation to seawater. The adaptive durations included an abrupt change (A0) from fresh water to seawater with a salinity of 1.5% or stepwise adaptation either 4 (A4) or 7 (A7) days of increasing saline concentrations. The results showed that dry matter intake in the non-adapted goats (A0 group) was lower than that of the control group or the adapted goats throughout the experiment (p<0.05). In contrast, water intake from drinking saline water was greater than that in the control group (p<0.05). Body weigh did not differ among the treatments; however, non-adapted goats exhibited a lower weight gain than the adapted goats (p<0.05). The goats in the A0 and A4 groups exhibited increased plasma levels of urea, AST, and ALT compared with the control and A7 groups. However, blood electrolyte levels remained unchanged and were within the normal range for goats. Therefore, it is concluded that the stepwise adaptation to seawater with a salinity of 1.5% for 21 days has no influence on productivity and health status of goats.

Augmented reality for point-of-care ultrasound-guided vascular access in pediatric patients using Microsoft HoloLens 2: a preliminary evaluation.

Significance: Conventional ultrasound-guided vascular access procedures are challenging due to the need for anatomical understanding, precise needle manipulation, and hand-eye coordination. Recently, augmented reality (AR)-based guidance has emerged as an aid to improve procedural efficiency and potential outcomes. However, its application in pediatric vascular access has not been comprehensively evaluated. Aim: We developed an AR ultrasound application, HoloUS, using the Microsoft HoloLens 2 to display live ultrasound images directly in the proceduralist's field of view. We presented our evaluation of the effect of using the Microsoft HoloLens 2 for point-of-care ultrasound (POCUS)-guided vascular access in 30 pediatric patients. Approach: A custom software module was developed on a tablet capable of capturing the moving ultrasound image from any ultrasound machine's screen. The captured image was compressed and sent to the HoloLens 2 via a hotspot without needing Internet access. On the HoloLens 2, we developed a custom software module to receive, decompress, and display the live ultrasound image. Hand gesture and voice command features were implemented for the user to reposition, resize, and change the gain and the contrast of the image. We evaluated 30 (15 successful control and 12 successful interventional) cases completed in a single-center, prospective, randomized study. Results: The mean overall rendering latency and the rendering frame rate of the HoloUS application were 139.30 ms ( σ = 32.02 ms ) and 30 frames per second, respectively. The average procedure completion time was 17.3% shorter using AR guidance. The numbers of puncture attempts and needle redirections were similar between the two groups, and the number of head adjustments was minimal in the interventional group. Conclusion: We presented our evaluation of the results from the first study using the Microsoft HoloLens 2 that investigates AR-based POCUS-guided vascular access in pediatric patients. Our evaluation confirmed clinical feasibility and potential improvement in procedural efficiency.

Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation.

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

Assessing the Effect of Augmented Reality on Procedural Outcomes During Ultrasound-Guided Vascular Access.

OBJECTIVE: Augmented reality devices are increasingly accepted in health care, though most applications involve education and pre-operative planning. A novel augmented reality ultrasound application, HoloUS, was developed for the Microsoft HoloLens 2 to project real-time ultrasound images directly into the user's field of view. In this work, we assessed the effect of using HoloUS on vascular access procedural outcomes. METHODS: A single-center user study was completed with participants with (N = 22) and without (N = 12) experience performing ultrasound-guided vascular access. Users completed a venipuncture and aspiration task a total of four times: three times on study day 1, and once on study day 2 between 2 and 4 weeks later. Users were randomized to use conventional ultrasound during either their first or second task and the HoloUS application at all other times. Task completion time, numbers of needle re-directions, head adjustments and needle visualization rates were recorded. RESULTS: For expert users, task completion time was significantly faster using HoloUS (11.5 s, interquartile range [IQR] = 6.5-23.5 s vs. 18.5 s, IQR = 11.0-36.5 s; p = 0.04). The number of head adjustments was significantly lower using the HoloUS app (1.0, IQR = 0.0-1.0 vs. 3.0, IQR = 1.0-5.0; p < 0.0001). No significant differences were identified in other measured outcomes. CONCLUSION: This is the first investigation of augmented reality-based ultrasound-guided vascular access using the second-generation HoloLens. It demonstrates equivalent procedural efficiency and accuracy, with favorable usability, ergonomics and user independence when compared with traditional ultrasound techniques.

Calibrated Frequency-Division Distorted Born Iterative Tomography for Real-Life Head Imaging.

The clinical use of microwave tomography (MT) requires addressing the significant mismatch between simulated environment, which is used in the forward solver, and real-life system. To alleviate this mismatch, a calibrated tomography, which uses two homogeneous calibration phantoms and a modified distorted Born iterative method (DBIM), is presented. The two phantoms are used to derive a linear model that matches the forward solver to real-life measurements. Moreover, experimental observations indicate that signal quality at different frequencies varies between different antennas due to inevitably inconsistent manufacturing tolerance and variances in radio-frequency chains. An optimum frequency, at which the simulated and measured signals of the antenna present maximum similarity when irradiating the calibrated phantoms, is thus calculated for each antenna. A frequency-division DBIM (FD-DBIM), in which different antennas in the array transmit their corresponding optimum frequencies, is subsequently developed. A clinical brain scanner is then used to assess performance of the algorithm in lab and healthy volunteers' tests. The linear calibration model is first used to calibrate the measured data. After that FD-DBIM is used to solve the problem and map the dielectric properties of the imaged domain. The simulated and experimental results confirm validity of the presented approach and its superiority to other tomographic method.

Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver.

Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl4. The ground truth of the liver state was based on lipid liver percents estimated via the Folch assay and hydroxyproline concentration to quantify fibrosis. Rabbits were scanned ultrasonically in vivo using a SonixOne scanner and an L9-4/38 linear array. Liver fat percentage was classified based on the ultrasonic backscattered radiofrequency (RF) signals from the livers using either QUS or a 1-D convolutional neural network (CNN). Use of QUS parameters with linear regression and canonical correlation analysis demonstrated that the QUS parameters could differentiate between livers with lipid levels above or below 5%. However, the QUS parameters were not sensitive to fibrosis. The CNN was implemented by analyzing raw RF ultrasound signals without using separate reference data. The CNN outputs the classification of liver as either above or below a threshold of 5% fat level in the liver. The CNN outperformed the classification utilizing the QUS parameters combined with a support vector machine in differentiating between low and high lipid liver levels (i.e., accuracies of 74% versus 59% on the testing data). Therefore, although the CNN did not provide a physical interpretation of the tissue properties (e.g., attenuation of the medium or scatterer properties) the CNN had much higher accuracy in predicting fatty liver state and did not require an external reference scan.

Estimation of Backscatter Coefficients Using an In Situ Calibration Source.

The objective of this article is to demonstrate the feasibility of estimating the backscatter coefficient (BSC) using an in situ calibration source. Traditional methods of estimating the BSC in vivo using a reference phantom technique do not account for transmission losses due to intervening layers between the ultrasonic source and the tissue region to be interrogated, leading to increases in bias and variance of BSC-based estimates. To account for transmission losses, an in situ calibration approach is proposed. The in situ calibration technique employs a titanium sphere that is well-characterized ultrasonically, biocompatible, and embedded inside the sample. A set of experiments was conducted to evaluate the embedded titanium spheres as in situ calibration targets for BSC estimation. The first experiment quantified the backscattered signal strength from titanium spheres of three sizes: 0.5, 1, and 2 mm in diameter. The second set of experiments assessed the repeatability of BSC estimates from the titanium spheres and compared these BSCs to theory. The third set of experiments quantified the ability of the titanium bead to provide an in situ reference spectrum in the presence of a lossy layer on top of the sample. The final set of experiments quantified the ability of the bead to provide a calibration spectrum over multiple depths in the sample. All experiments were conducted using an L9-4/38 linear array connected to a SonixOne system. The strongest signal was observed from the 2-mm titanium bead with the signal-to-noise ratio (SNR) of 11.6 dB with respect to the background speckle. Using an analysis bandwidth of 2.5-5.5 MHz, the mean differences between the experimentally derived BSCs and BSCs derived from the Faran theory were 0.54 and 0.76 dB using the array and a single-element transducer, respectively. The BSCs estimated using the in situ calibration approach without the layer and with the layer and using the reference phantom approach with the layer were compared to the reference phantom approach without the layer present. The mean differences in BSCs were 0.15, 0.73, and -9.69 dB, respectively. The mean differences of the BSCs calculated from data blocks located at depths that were either 30 pulse lengths above or below the actual bead depth compared to the BSC calculated at bead depth were -1.55 and -1.48 dB, respectively. The results indicate that an in situ calibration target can account for overlaying tissue losses, thereby improving the robustness of BSC-based estimates.

Visualization of the Intensity Field of a Focused Ultrasound Source In Situ.

In an increasing number of applications of focused ultrasound (FUS) therapy, such as opening of the blood-brain barrier or collapsing microbubbles in a tumor, elevation of tissue temperature is not involved. In these cases, real-time visualization of the field distribution of the FUS source would allow localization of the FUS beam within the targeted tissue and allow repositioning of the FUS beam during tissue motion. In this paper, in order to visualize the FUS beam in situ, a 6-MHz single-element transducer ( f /2) was used as the FUS source and aligned perpendicular to a linear array which passively received scattered ultrasound from the sample. An image of the reconstructed intensity field pattern of the FUS source using bistatic beamforming was then superimposed on a registered B-mode image of the sample acquired using the same linear array. The superimposed image is used to provide anatomical context of the FUS beam in the sample being treated. The intensity field pattern reconstructed from a homogeneous scattering phantom was compared with the field characteristics of the FUS source characterized by the wire technique. The beamwidth estimates at the FUS focus using the in situ reconstruction technique and the wire technique were 1.5 and 1.2 mm, respectively. The depth-of-field estimates for the in situ reconstruction technique and the wire technique were 11.8 and 16.8 mm, respectively. The FUS beams were also visualized in a two-layer phantom and a chicken breast. The novel reconstruction technique was able to accurately visualize the field of an FUS source in the context of the interrogated medium.

Characterizing Fatty Liver in vivo in Rabbits, Using Quantitative Ultrasound.

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and can often lead to fibrosis, cirrhosis, cancer and complete liver failure. Liver biopsy is the current standard of care to quantify hepatic steatosis, but it comes with increased patient risk and only samples a small portion of the liver. Imaging approaches to assess NAFLD include proton density fat fraction estimated via magnetic resonance imaging (MRI) and shear wave elastography. However, MRI is expensive and shear wave elastography is not proven to be sensitive to fat content of the liver (Kramer et al. 2016). On the other hand, ultrasonic attenuation and the backscatter coefficient (BSC) have been observed to be sensitive to levels of fat in the liver (Lin et al. 2015; Paige et al. 2017). In this study, we assessed the use of attenuation and the BSC to quantify hepatic steatosis in vivo in a rabbit model of fatty liver. Rabbits were maintained on a high-fat diet for 0, 1, 2, 3 or 6 wk, with 3 rabbits per diet group (total N = 15). An array transducer (L9-4) with a center frequency of 4.5 MHz connected to a SonixOne scanner was used to gather radio frequency (RF) backscattered data in vivo from rabbits. The RF signals were used to estimate an average attenuation and BSC for each rabbit. Two approaches were used to parameterize the BSC (i.e., the effective scatterer diameter and effective acoustic concentration using a spherical Gaussian model and a model-free approach using a principal component analysis [PCA]). The 2 major components of the PCA from the BSCs, which captured 96% of the variance of the transformed data, were used to generate input features to a support vector machine for classification. Rabbits were separated into two liver fat-level classes, such that approximately half of the rabbits were in the low-lipid class (≤9% lipid liver level) and half of the rabbits in the high-lipid class (>9% lipid liver level). The slope and the midband fit of the attenuation coefficient provided statistically significant differences (p value = 0.00014 and p value = 0.007, using a two-sample t test) between low and high-lipid fat classes. The proposed model-free and model-based parameterization of the BSC and attenuation coefficient parameters yielded classification accuracies of 84.11 %, 82.93 % and 78.91 % for differentiating low-lipid versus high-lipid classes, respectively. The results suggest that attenuation and BSC analysis can differentiate low-fat versus high-fat livers in a rabbit model of fatty liver disease.

A comparative investigation of coarticulation in fricatives: electropalatographic, electromagnetic, and acoustic data.

The principal aim of this investigation was to compare coarticulatory effects at different levels of the speech production system, in order to gain insight into the relations between the different levels. To this end, the relative magnitudes of carryover and anticipatory coarticulation with adjacent vowels were measured at the midpoints of the two lingual fricatives /s/ and /integral of/ in two speakers each of English, French, and German. Linguopalatal contact patterns derived from electropalatographic recordings were compared with an analysis of the acoustic output. The results indicated, firstly, that mismatches between articulatory and acoustic results are not uncommon. Secondly, and more surprisingly, while there was no difference in the overall magnitude of coarticulatory effects for /s/ and /integral of/, not all speakers showed a predominance of the same coarticulatory direction on both fricatives; this complicated the observed tendency for the predominance of carryover coarticulation to be greater in German and English than in French. Two speakers were retested using comparative analyses of electropalatography and electromagnetic articulography. These two procedures gave a closely parallel picture of lingual coarticulatory regularities (while complementing each other in terms of characterizing articulation). The implications of these results for identifying language-specific coarticulatory regularities are discussed.

Maternal ECG-Guided Neural Network for Improved Fetal Electrocardiogram Extraction.

Historically, acquiring a reliable and accurate non-invasive fetal electrocardiogram has several significant challenges in both data acquisition and attenuation of maternal signals. These barriers include maternal physical/physiological parameters, hardware sensitivity, and the effectiveness of signal processing algorithms in separating maternal and fetal electrocardiograms. In this paper, we focus on the evaluation of signal-processing algorithms. Here, we propose a learning-based method based on the integration of maternal electrocardiogram acquired as guidance for transabdominal fetal electrocardiogram signal extraction. The results demonstrate that incorporating the maternal electrocardiogram signal as input for training the neural network outperforms the network solely trained using information from the abdominal electrocardiogram. This indicates that leveraging the maternal electrocardiogram serves as a suitable prior for effectively attenuating maternal electrocardiogram from the abdominal electrocardiogram.