Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms.

Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. Results: In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breaths/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). Conclusions: These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI).

The application of machine learning and artificial intelligence to clinical settings for prevention, diagnosis, treatment, and the improvement of clinical care have been demonstrably cost-effective. However, current clinical AI (cAI) support tools are predominantly created by non-domain experts and algorithms available in the market have been criticized for the lack of transparency behind their creation. To combat these challenges, the Massachusetts Institute of Technology Critical Data (MIT-CD) consortium, an affiliation of research labs, organizations, and individuals that contribute to research in and around data that has a critical impact on human health, has iteratively developed the "Ecosystem as a Service (EaaS)" approach, providing a transparent education and accountability platform for clinical and technical experts to collaborate and advance cAI. The EaaS approach provides a range of resources, from open-source databases and specialized human resources to networking and collaborative opportunities. While mass deployment of the ecosystem still faces several hurdles, here we discuss our initial implementation efforts. We hope this will promote further exploration and expansion of the EaaS approach, while also informing or realizing policies that will accelerate multinational, multidisciplinary, and multisectoral collaborations in cAI research and development, and provide localized clinical best practices for equitable healthcare access.

Customization scenarios for de-identification of clinical notes.

BACKGROUND: Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about their performance when applied to new datasets. OBJECTIVE: We present practical options for clinical note de-identification, assessing performance of machine learning systems ranging from off-the-shelf to fully customized. METHODS: We implement a state-of-the-art machine learning de-identification system, training and testing on pairs of datasets that match the deployment scenarios. We use clinical notes from two i2b2 competition corpora, the Physionet Gold Standard corpus, and parts of the MIMIC-III dataset. RESULTS: Fully customized systems remove 97-99% of personally identifying information. Performance of off-the-shelf systems varies by dataset, with performance mostly above 90%. Providing a small labeled dataset or large unlabeled dataset allows for fine-tuning that improves performance over off-the-shelf systems. CONCLUSION: Health organizations should be aware of the levels of customization available when selecting a de-identification deployment solution, in order to choose the one that best matches their resources and target performance level.

Very low intravenous contrast volume protocol for computed tomography angiography providing comprehensive cardiac and vascular assessment prior to transcatheter aortic valve replacement in patients with chronic kidney disease.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is a lifesaving procedure for many patients high risk for surgical aortic valve replacement. The prevalence of chronic kidney disease (CKD) is high in this population, and thus a very low contrast volume (VLCV) computed tomography angiography (CTA) protocol providing comprehensive cardiac and vascular imaging would be valuable. METHODS: 52 patients with severe, symptomatic aortic valve disease, undergoing pre-TAVR CTA assessment from 2013-4 at Columbia University Medical Center were studied, including all 26 patients with CKD (eGFR<30 mL/min) who underwent a novel VLCV protocol (20 mL of iohexol at 2.5 mL/s), and 26 standard-contrast-volume (SCV) protocol patients. Using a 320-slice volumetric scanner, the protocol included ECG-gated volume scanning of the aortic root followed by medium-pitch helical vascular scanning through the femoral arteries. Two experienced cardiologists performed aortic annulus and root measurements. Vascular image quality was assessed by two radiologists using a 4-point scale. RESULTS: VLCV patients had mean (±SD) age 86 ± 6.5, BMI 23.9 ± 3.4 kg/m(2) with 54% men; SCV patients age 83 ± 8.8, BMI 28.7 ± 5.3 kg/m(2), 65% men. There was excellent intra- and inter-observer agreement for annular and root measurements, and excellent agreement with 3D-transesophageal echocardiographic measurements. Both radiologists found diagnostic-quality vascular imaging in 96% of VLCV and 100% of SCV cases, with excellent inter-observer agreement. CONCLUSIONS: This study is the first of its kind to report the feasibility and reproducibility of measurements for a VLCV protocol for comprehensive pre-TAVR CTA. There was excellent agreement of cardiac measurements and almost all studies were diagnostic quality for vascular access assessment.

Pulmonary Microvascular Blood Flow in Mild Chronic Obstructive Pulmonary Disease and Emphysema. The MESA COPD Study.

RATIONALE: Smoking-related microvascular loss causes end-organ damage in the kidneys, heart, and brain. Basic research suggests a similar process in the lungs, but no large studies have assessed pulmonary microvascular blood flow (PMBF) in early chronic lung disease. OBJECTIVES: To investigate whether PMBF is reduced in mild as well as more severe chronic obstructive pulmonary disease (COPD) and emphysema. METHODS: PMBF was measured using gadolinium-enhanced magnetic resonance imaging (MRI) among smokers with COPD and control subjects age 50 to 79 years without clinical cardiovascular disease. COPD severity was defined by standard criteria. Emphysema on computed tomography (CT) was defined by the percentage of lung regions below -950 Hounsfield units (-950 HU) and by radiologists using a standard protocol. We adjusted for potential confounders, including smoking, oxygenation, and left ventricular cardiac output. MEASUREMENTS AND MAIN RESULTS: Among 144 participants, PMBF was reduced by 30% in mild COPD, by 29% in moderate COPD, and by 52% in severe COPD (all P < 0.01 vs. control subjects). PMBF was reduced with greater percentage emphysema-950HU and radiologist-defined emphysema, particularly panlobular and centrilobular emphysema (all P ≤ 0.01). Registration of MRI and CT images revealed that PMBF was reduced in mild COPD in both nonemphysematous and emphysematous lung regions. Associations for PMBF were independent of measures of small airways disease on CT and gas trapping largely because emphysema and small airways disease occurred in different smokers. CONCLUSIONS: PMBF was reduced in mild COPD, including in regions of lung without frank emphysema, and may represent a distinct pathological process from small airways disease. PMBF may provide an imaging biomarker for therapeutic strategies targeting the pulmonary microvasculature.

Biomechanics of milk extraction during breast-feeding.

How do infants extract milk during breast-feeding? We have resolved a century-long scientific controversy, whether it is sucking of the milk by subatmospheric pressure or mouthing of the nipple-areola complex to induce a peristaltic-like extraction mechanism. Breast-feeding is a dynamic process, which requires coupling between periodic motions of the infant's jaws, undulation of the tongue, and the breast milk ejection reflex. The physical mechanisms executed by the infant have been intriguing topics. We used an objective and dynamic analysis of ultrasound (US) movie clips acquired during breast-feeding to explore the tongue dynamic characteristics. Then, we developed a new 3D biophysical model of the breast and lactiferous tubes that enables the mimicking of dynamic characteristics observed in US imaging during breast-feeding, and thereby, exploration of the biomechanical aspects of breast-feeding. We have shown, for the first time to our knowledge, that latch-on to draw the nipple-areola complex into the infant mouth, as well as milk extraction during breast-feeding, require development of time-varying subatmospheric pressures within the infant's oral cavity. Analysis of the US movies clearly demonstrated that tongue motility during breast-feeding was fairly periodic. The anterior tongue, which is wedged between the nipple-areola complex and the lower lips, moves as a rigid body with the cycling motion of the mandible, while the posterior section of the tongue undulates in a pattern similar to a propagating peristaltic wave, which is essential for swallowing.

A momentum-based constraint on optical flow tracking of the endocardial surface.

Echocardiography is the standard of care for the evaluation of cardiac function in a variety of clinical scenarios. Despite the increasing availability of RT3D imaging, its utility remains limited due to a lack of tools available to analyze 3D+t datasets. In previous work, we have proposed and validated optical flow as an effective correlation-based technique to track myocardial motion and deformation in RT3D datasets. However, OF's ability to track small regions of tissue (e.g. the endocardial surface) is diminished in less optimal acquisitions. Our goal, therefore, is to develop additional constraints on OF-estimated motion in order to increase the robustness of endocardial surface tracking. We present several modifications to OF-based tracking including motion field smoothing and momentum correction that results in improved OF tracking.

Parameterization of real-time 3D speckle tracking framework for cardiac strain assessment.

Cross-correlation based 3D speckle tracking algorithm can be used to automatically track myocardial motion on three dimensional real-time (RT3D) echocardiography. The goal of this study was to experimentally investigate the effects of different parameters associated with such algorithm to ensure accurate cardiac strain measurements. The investigation was performed on 10 chronic obstructive pulmonary disease RT3DE cardiac ultrasound images. The following two parameters were investigated: 1) the gradient threshold of the anisotropic diffusion pre-filtering and 2) the window size of the cross correlation template matching in the speckle tracking. Results suggest that the optimal gradient threshold of the anisotropic filter depends on the average gradient of the background speckle noise, and that an optimal pair of template size and search window size can be identified determines the cross-correlation level and computational cost.

In-vivo clinical validation of cardiac deformation and strain measurements from 4D ultrasound.

An important goal in clinical cardiology is the non-invasive quantification of regional cardiac deformation. While many methods have been proposed for the estimation of 3D left ventricular deformation and strains derived from 4D ultrasound, currently there is a lack of in vivo clinical validation of these algorithms on humans. In this paper, we describe the experiments used in validating cardiac deformation and strain estimates of 4D ultrasound using correlation-based optical flow tracking on two different COPD patients with normal left ventricular function. Validation of the algorithm was done by 1) validation of cardiac volume across multiple scans of the same patient and 2) validation of the repeatability of cardiac displacement and strain results from multiple scan acquisitions of the same patient. The preliminary results are encouraging with our algorithm producing consistent cardiac volume and strain results across multiple acquisitions. Furthermore, our derived 4D cardiac strains showed qualitatively correct results. We also observed particularly interesting results in the radial displacements of the posterior and lateral walls of our COPD patients.

Leveraging genetic algorithm and neural network in automated protein crystal recognition.

We propose a classification framework combined with a multi-scale image processing method for recognizing protein crystals in high-throughput images. The main three points of the processing method are the multiple population genetic algorithm for region of interest detection, multi-scale Laplacian pyramid filters and histogram analysis techniques to find an effective feature vector. Using human (expert crystallographers) classified images as ground truth, the current experimental results gave 88% true positive and 99% true negative rates, resulting in an average true performance of approximately 93.5% validated on an image database which contained over 79,000 images.