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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A review of imaging techniques for systems biology.

This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and molecular probes such as quantum dots and nanoshells in systems biology.