Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department.

Importance: Multimodal generative artificial intelligence (AI) methodologies have the potential to optimize emergency department care by producing draft radiology reports from input images. Objective: To evaluate the accuracy and quality of AI-generated chest radiograph interpretations in the emergency department setting. Design, Setting, and Participants: This was a retrospective diagnostic study of 500 randomly sampled emergency department encounters at a tertiary care institution including chest radiographs interpreted by both a teleradiology service and on-site attending radiologist from January 2022 to January 2023. An AI interpretation was generated for each radiograph. The 3 radiograph interpretations were each rated in duplicate by 6 emergency department physicians using a 5-point Likert scale. Main Outcomes and Measures: The primary outcome was any difference in Likert scores between radiologist, AI, and teleradiology reports, using a cumulative link mixed model. Secondary analyses compared the probability of each report type containing no clinically significant discrepancy with further stratification by finding presence, using a logistic mixed-effects model. Physician comments on discrepancies were recorded. Results: A total of 500 ED studies were included from 500 unique patients with a mean (SD) age of 53.3 (21.6) years; 282 patients (56.4%) were female. There was a significant association of report type with ratings, with post hoc tests revealing significantly greater scores for AI (mean [SE] score, 3.22 [0.34]; P < .001) and radiologist (mean [SE] score, 3.34 [0.34]; P < .001) reports compared with teleradiology (mean [SE] score, 2.74 [0.34]) reports. AI and radiologist reports were not significantly different. On secondary analysis, there was no difference in the probability of no clinically significant discrepancy between the 3 report types. Further stratification of reports by presence of cardiomegaly, pulmonary edema, pleural effusion, infiltrate, pneumothorax, and support devices also yielded no difference in the probability of containing no clinically significant discrepancy between the report types. Conclusions and Relevance: In a representative sample of emergency department chest radiographs, results suggest that the generative AI model produced reports of similar clinical accuracy and textual quality to radiologist reports while providing higher textual quality than teleradiologist reports. Implementation of the model in the clinical workflow could enable timely alerts to life-threatening pathology while aiding imaging interpretation and documentation.

Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study.

OBJECTIVE: Tracking changes in hemodynamic congestion and the consequent proactive readjustment of treatment has shown efficacy in reducing hospitalizations for patients with heart failure (HF). However, the cost-prohibitive nature of these invasive sensing systems precludes their usage in the large patient population affected by HF. The objective of this research is to estimate the changes in pulmonary artery mean pressure (PAM) and pulmonary capillary wedge pressure (PCWP) following vasodilator infusion during right heart catheterization (RHC), using changes in simultaneously recorded wearable seismocardiogram (SCG) signals captured with a small wearable patch. METHODS: A total of 20 patients with HF (20% women, median age 55 (interquartile range (IQR), 44-64) years, ejection fraction 24 (IQR, 16-43)) were fitted with a wearable sensing patch and underwent RHC with vasodilator challenge. We divided the dataset randomly into a training-testing set (n = 15) and a separate validation set (n = 5). We developed globalized (population) regression models to estimate changes in PAM and PCWP from the changes in simultaneously recorded SCG. RESULTS: The regression model estimated both pressures with good accuracies: root-mean-square-error (RMSE) of 2.5 mmHg and R2 of 0.83 for estimating changes in PAM, and RMSE of 1.9 mmHg and R2 of 0.93 for estimating changes in PCWP for the training-testing set, and RMSE of 2.7 mmHg and R2 of 0.81 for estimating changes in PAM, and RMSE of 2.9 mmHg and R2 of 0.95 for estimating changes in PCWP for the validation set respectively. CONCLUSION: Changes in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF. SIGNIFICANCE: This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.

Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum.

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.

Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning.

Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

Wearable Cuff-Less Blood Pressure Estimation at Home via Pulse Transit Time.

OBJECTIVE: We developed a wearable watch-based device to provide noninvasive, cuff-less blood pressure (BP) estimation in an at-home setting. METHODS: The watch measures single-lead electrocardiogram (ECG), tri-axial seismocardiogram (SCG), and multi-wavelength photoplethysmogram (PPG) signals to compute the pulse transit time (PTT), allowing for BP estimation. We sent our custom watch device and an oscillometric BP cuff home with 21 healthy subjects, and captured the natural variability in BP over the course of a 24-hour period. RESULTS: After calibration, our Pearson correlation coefficient (PCC) of 0.69 and root-mean-square-error (RMSE) of 2.72 mmHg suggest that noninvasive PTT measurements correlate with around-the-clock BP. Using a novel two-point calibration method, we achieved a RMSE of 3.86 mmHg. We further demonstrated the potential of a semi-globalized adaptive model to reduce calibration requirements. CONCLUSION: This is, to the best of our knowledge, the first time that BP has been comprehensively estimated noninvasively using PTT in an at-home setting. We showed a more convenient method for obtaining ambulatory BP than through the use of the standard oscillometric cuff. We presented new calibration methods for BP estimation using fewer calibration points that are more practical for a real-world scenario. SIGNIFICANCE: A custom watch (SeismoWatch) capable of taking multiple BP measurements enables reliable remote monitoring of daily BP and paves the way towards convenient hypertension screening and management, which can potentially reduce hospitalizations and improve quality of life.

Non-Invasive Wearable Patch Utilizing Seismocardiography for Peri-Operative Use in Surgical Patients.

OBJECTIVE: Optimizing peri-operative fluid management has been shown to improve patient outcomes and the use of stroke volume (SV) measurement has become an accepted tool to guide fluid therapy. The Transesophageal Doppler (TED) is a validated, minimally invasive device that allows clinical assessment of SV. Unfortunately, the use of the TED is restricted to the intra-operative setting in anesthetized patients and requires constant supervision and periodic adjustment for accurate signal quality. However, post-operative fluid management is also vital for improved outcomes. Currently, there is no device regularly used in clinics that can track patient's SV continuously and non-invasively both during and after surgery. METHODS: In this paper, we propose the use of a wearable patch mounted on the mid-sternum, which captures the seismocardiogram (SCG) and electrocardiogram (ECG) signals continuously to predict SV in patients undergoing major surgery. In a study of 12 patients, hemodynamic data was recorded simultaneously using the TED and wearable patch. Signal processing and regression techniques were used to derive SV from the signals (SCG and ECG) captured by the wearable patch and compare it to values obtained by the TED. RESULTS: The results showed that the combination of SCG and ECG contains substantial information regarding SV, resulting in a correlation and median absolute error between the predicted and reference SV values of 0.81 and 7.56 mL, respectively. SIGNIFICANCE: This work shows promise for the proposed wearable-based methodology to be used as an alternative to TED for continuous patient monitoring and guiding peri-operative fluid management.

Cardiac Function Monitoring for Patients Undergoing Cancer Treatments Using Wearable Seismocardiography: A Proof-of-Concept Study.

Advances in cancer therapeutics have dramatically improved the survival rate and quality of life in patients affected by various cancers, but have been accompanied by treatment-related cardiotoxicity, e.g. left ventricular (LV) dysfunction and/or overt heart failure (HF). Cardiologists thus need to assess cancer treatment-related cardiotoxic risks and have close followups for cancer survivors and patients undergoing cancer treatments using serial echocardiography exams and cardiovascular biomarkers testing. Unfortunately, the cost-prohibitive nature of echocardiography has made these routine follow-ups difficult and not accessible to the growing number of cancer survivors and patients undergoing cancer treatments. There is thus a need to develop a wearable system that can yield similar information at a minimal cost and can be used for remote monitoring of these patients. In this proof-of-concept study, we have investigated the use of wearable seismocardiography (SCG) to monitor LV function non-invasively for patients undergoing cancer treatment. A total of 12 subjects (six with normal LV relaxation, five with impaired relaxation and one with pseudo-normal relaxation) underwent routine echocardiography followed by a standard six-minute walk test. Wearable SCG and electrocardiogram signals were collected during the six-minute walk test and, later, the signal features were compared between subjects with normal and impaired LV relaxation. Pre-ejection period (PEP) from SCG decreased significantly (p < 0.05) during exercise for the subjects with impaired relaxation compared to the subjects with normal relaxation, and changes in PEP/LV ejection time (LVET) were also significantly different between these two groups (p < 0.05). These results suggest that wearable SCG may enable monitoring of patients undergoing cancer treatments by assessing cardiotoxicity.

Wearable Patch-Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients With Heart Failure.

BACKGROUND: To estimate oxygen uptake (VO2) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch. CPX is an important risk stratification tool for patients with heart failure (HF) owing to the prognostic value of the features derived from the gas exchange variables such as VO2. However, CPX requires specialized equipment, as well as trained professionals to conduct the study. METHODS AND RESULTS: We have conducted a total of 68 CPX tests on 59 patients with HF with reduced ejection fraction (31% women, mean age 55 ± 13 years, ejection fraction 0.27 ± 0.11, 79% stage C). The patients were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing set (n = 44) and a separate validation set (n = 24). We developed globalized (population) regression models to estimate VO2 from the SCG and ECG signals measured continuously with the patch. We further classified the patients as stage D or C using the SCG and ECG features to assess the ability to detect clinical state from the wearable patch measurements alone. We developed the regression and classification model with cross-validation on the training-testing set and validated the models on the validation set. The regression model to estimate VO2 from the wearable features yielded a moderate correlation (R2 of 0.64) with a root mean square error of 2.51 ± 1.12 mL · kg-1 · min-1 on the training-testing set, whereas R2 and root mean square error on the validation set were 0.76 and 2.28 ± 0.93 mL · kg-1 · min-1, respectively. Furthermore, the classification of clinical state yielded accuracy, sensitivity, specificity, and an area under the receiver operating characteristic curve values of 0.84, 0.91, 0.64, and 0.74, respectively, for the training-testing set, and 0.83, 0.86, 0.67, and 0.92, respectively, for the validation set. CONCLUSIONS: Wearable SCG and ECG can assess CPX VO2 and thereby classify clinical status for patients with HF. These methods may provide value in the risk stratification of patients with HF by tracking cardiopulmonary parameters and clinical status outside of specialized settings, potentially allowing for more frequent assessments to be performed during longitudinal monitoring and treatment.

Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI.

Cardiac MRI (CMR) techniques offer non-invasive visualizations of cardiac morphology and function. However, imaging can be time-consuming and complex. Seismocardiography (SCG) measures physical vibrations transmitted through the chest from the beating heart and pulsatile blood flow. SCG signals can be acquired quickly and easily, with inexpensive electronics. This study investigates relationships between CMR metrics of function and SCG signal features. Same-day CMR and SCG data were collected from 28 healthy adults and 6 subjects with aortic valve disease history. Correlation testing and statistical median/decile calculations were performed with data from the healthy cohort. MR-quantified flow and function parameters in the healthy cohort correlated with particular SCG energy levels, such as peak aortic velocity with low-frequency SCG (coefficient 0.43, significance 0.02) and peak flow with high-frequency SCG (coefficient 0.40, significance 0.03). Valve disease-induced flow abnormalities in patients were visualized with MRI, and corresponding abnormalities in SCG signals were identified. This investigation found significant cross-modality correlations in cardiac function metrics and SCG signals features from healthy subjects. Additionally, through comparison to normative ranges from healthy subjects, it observed correspondences between pathological flow and abnormal SCG. This may support development of an easy clinical test used to identify potential aortic flow abnormalities.

Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients.

BACKGROUND: Remote monitoring of patients with heart failure (HF) using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise. METHODS AND RESULTS: Patients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable ECG and seismocardiogram sensing patch. Patients stood at rest for an initial recording, performed a 6-minute walk test, and then stood at rest for 5 minutes of recovery. The protocol was performed at the time of outpatient visit or at 2 time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of seismocardiogram signals after exercise compared with rest using graph mining (graph similarity score). We found that graph similarity score can assess HF patient state and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the graph similarity score metric (44.4±4.9 [decompensated HF] versus 35.2±10.5 [compensated HF]; P<0.001). In the 6 decompensated patients with longitudinal data, we found a significant change in graph similarity score from admission (decompensated) to discharge (compensated; 44±4.1 [admitted] versus 35±3.9 [discharged]; P<0.05). CONCLUSIONS: Wearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to submaximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health.

GOAL: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds. METHODS: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. RESULTS: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. CONCLUSION: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. SIGNIFICANCE: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.

A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables.

We present a low power multi-modal patch designed for measuring activity, altitude (based on high-resolution barometric pressure), a single-lead electrocardiogram, and a tri-axial seismocardiogram (SCG). Enabled by a novel embedded systems design methodology, this patch offers a powerful means of monitoring the physiology for both patients with chronic cardiovascular diseases, and the general population interested in personal health and fitness measures. Specifically, to the best of our knowledge, this patch represents the first demonstration of combined activity, environmental context, and hemodynamics monitoring, all on the same hardware, capable of operating for longer than 48 hours at a time with continuous recording. The three-channels of SCG and one-lead ECG are all sampled at 500 Hz with high signal-to-noise ratio, the pressure sensor is sampled at 10 Hz, and all signals are stored to a microSD card with an average current consumption of less than 2 mA from a 3.7 V coin cell (LIR2450) battery. In addition to electronic characterization, proof-of-concept exercise recovery studies were performed with this patch, suggesting the ability to discriminate between hemodynamic and electrophysiology response to light, moderate, and heavy exercise.

Rapid and low-cost prototyping of medical devices using 3D printed molds for liquid injection molding.

Biologically inert elastomers such as silicone are favorable materials for medical device fabrication, but forming and curing these elastomers using traditional liquid injection molding processes can be an expensive process due to tooling and equipment costs. As a result, it has traditionally been impractical to use liquid injection molding for low-cost, rapid prototyping applications. We have devised a method for rapid and low-cost production of liquid elastomer injection molded devices that utilizes fused deposition modeling 3D printers for mold design and a modified desiccator as an injection system. Low costs and rapid turnaround time in this technique lower the barrier to iteratively designing and prototyping complex elastomer devices. Furthermore, CAD models developed in this process can be later adapted for metal mold tooling design, enabling an easy transition to a traditional injection molding process. We have used this technique to manufacture intravaginal probes involving complex geometries, as well as overmolding over metal parts, using tools commonly available within an academic research laboratory. However, this technique can be easily adapted to create liquid injection molded devices for many other applications.

Tracking clinical status for heart failure patients using ballistocardiography and electrocardiography signal features.

Heart failure (HF) is an escalating public health problem, with few effective methods for home monitoring. In HF management, the important clinical factors to monitor include symptoms, fluid status, cardiac output, and blood pressure--based on these factors, inotrope and diuretic dosages are adjusted day-by-day to control the disorder and improve the patient's status towards a successful discharge. Previously, the ballistocardiogram (BCG) measured on a weighing scale has been shown to be capable of detecting changes in cardiac output and contractility for healthy subjects. In this study, we investigated whether the BCG and electrocardiogram (ECG) signals measured on a wireless modified scale could accurately track the clinical status of HF patients during their hospital stay. Using logistic regression, we found that the root-mean-square (RMS) power of the BCG provided a good fit for clinical status, as determined based on clinical measurements and symptoms, for the 85 patient days studied from 10 patients (p < 0.01). These results provide a promising foundation for future studies aimed at using the BCG/ECG scale at home to track HF patient status remotely.

Novel device to trend impedance and fluorescence of the cervix for preterm birth detection.

Preterm birth is the leading cause of worldwide neonatal mortality. It follows a pathologically accelerated form of the normal processes that govern cervical softening and dilation. Softening and dilation occur due to changes in cervical collagen crosslinking, which can be measured non-invasively by changes in tissue fluorescence and impedance. We present a novel device designed specifically to take fluorescence and impedance measurements throughout pregnancy, with the end goal of fusing and trending these measurements to form an early diagnosis of preterm labor.

Towards BirthAlert--A Clinical Device Intended for Early Preterm Birth Detection.

Preterm birth causes 1 million infant deaths worldwide every year, making it the leading cause of infant mortality. Existing diagnostic tests such as transvaginal ultrasound or fetal fibronectin either cannot determine if preterm birth will occur in the future or can only predict the occurrence once cervical shortening has begun, at which point it is too late to reverse the accelerated parturition process. Using iterative and rapid prototyping techniques, we have developed an intravaginal proof-of-concept device that measures both cervical bioimpedance and cervical fluorescence to characterize microstructural changes in a pregnant woman's cervix in hopes of detecting preterm birth before macroscopic changes manifest in the tissue. If successful, such an early alert during this "silent phase" of the preterm birth syndrome may open a new window of opportunity for interventions that may reverse and avoid preterm birth altogether.

Implantable ultra-low pulmonary pressure monitoring system for fetal surgery.

Congenital pulmonary hypoplasia is a devastating condition affecting fetal and newborn pulmonary physiology, resulting in great morbidity and mortality. The fetal lung develops in a fluid-filled environment. In this work, we describe a novel, implantable pressure sensing and recording device which we use to study the pressures present in the fetal pulmonary tree throughout gestation. The system achieves 0.18 cm H2O resolution and can record for twenty one days continuously at 256 Hz. Sample tracings of in vivo fetal lamb recordings are shown.