Health

Background: Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected on careful review, potentially causing rescanning, and workflow delays. Although scan time operator screening for whole-slide OOF is feasible, manual screening for OOF affecting...

Purpose Use adjudication to quantify errors in diabetic retinopathy (DR) grading based on individual graders and majority decision, and to train an improved automated algorithm for DR grading. Design Retrospective analysis. Participants Retinal fundus images from DR screening programs. Methods Images were each graded by the algorithm, U.S. board-certified ophthalmologists, and retinal...

Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito popula- tions. The Sterile Insect Technique (SIT) is a promising alternative to pesticides; however, effective SIT relies on minimal releases of female insects. This paper describes a multi-objective convolutional...

GUIDELINE TITLE: Mechanical Ventilation in Adult Patients With Acute Respiratory Distress Syndrome DEVELOPER: American Thoracic Society (ATS)/European Society of Intensive Care Medicine (ESICM)/Society of Critical Care Medicine (SCCM) RELEASE DATE: May 1, 2017 TARGET POPULATION: Hospitalized adults with acute respiratory distress syndrome (ARDS). SELECTED MAJOR RECOMMENDATIONS: For all patients...

Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep...

Objective: Refractive error, one of the leading cause of visual impairment, can be corrected by simple interventions like prescribing eyeglasses, which often starts with autorefraction to estimate the refractive error. In this study, using deep learning, we trained a network to estimate refractive error from fundus photos only. Design: Retrospective analysis. Subjects, Participants, and/or...

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around...

We consider three hypotheses concerning the primate neocortex which have influenced computational neuroscience in recent years. Is the mind modular in terms of its being profitably described as a collection of relatively independent functional units? Does the regular structure of the cortex imply a single algorithm at work, operating on many different inputs in parallel? Can the cognitive...

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation...

Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as...

Imaging is a central method in life sciences, and the drive to extract information from microscopy approaches has led to methods to fluorescently label specific cellular constituents. However, the specificity of fluorescent labels varies, labeling can confound biological measurements, and spectral overlap limits the number of labels to a few that can be resolved simultaneously. Here, we...

The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (e.g. deleterious variants and disease). This review summarizes lessons learned from the large-scale analyses of genome and exome data sets, modeling of...

Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Evolutionary algorithms provide a technique to discover such networks automatically. Despite significant computational requirements, we show that evolving models that rival large, hand-designed architectures is possible today. We...

Vision loss from diabetic retinopathy should be unnecessary for patients with access to diabetic retinopathy screening, yet it still occurs at high rates and in varied contexts. Precisely because vision loss is only one of many late-stage complications of diabetes, interfering with the management of diabetes and making self-care more difficult, Vision Threatening Diabetic Retinopathy (VTDR) is...

Copy number variants (CNVs) are an important type of genetic variation and play a causal role in many diseases. However, they are also notoriously difficult to identify accurately from next-generation sequencing (NGS) data. For larger CNVs, genotyping arrays provide reasonable benchmark data, but NGS allows us to assay a far larger number of small (< 10kbp) CNVs that are poorly captured by...

In this paper we document our experiences with developing speech recognition for Medical Transcription -- a system that automatically transcribes notes from doctor-patient conversations. Towards this goal, we built a system along two different methodological lines -- a Connectionist Temporal Classification (CTC) phoneme based model and a Listen Attend and Spell (LAS) model. To train these...

Background: Novel telemedicine platforms have allowed critical retinal screening to expand into primary care settings, making retinal screening no longer confined to specialty eye care clinics. This enhanced access has contributed to improved retinal screening for diabetic patients, particularly those treated in Federally Qualified Health Centers (‘safety net clinics’). While the implementation...

Image-based screening is a powerful technique to reveal how chemical, genetic, and environmental perturbations affect cellular state. Its potential is restricted by the current analysis algorithms that target a small number of cellular phenotypes and rely on expert-engineered image features. Newer algorithms that learn how to represent an image are limited by the small amount of labeled data...

We present a simple and robust optimization algorithm related to genetic algorithms, and with analogies to the popular CMA-ES search algorithm, that serves as a cheap alternative to Bayesian Optimization. The algorithm is robust against both monotonic transforms of the objective function value and affine transformations of the feasible region. It is fast and easy to implement, and has...

This study was triggered by a recognition of remarkably low adherence to doctor's or physical therapist's instruction like 40-70% even among people who should be highly motivated. We wanted to understand how that was so. People often blame themselves and other for lack of willpower. This corresponds nicely with prevalent assumptions in a related field, that of fitness technology, where non-...

This paper presents a robust, small-footprint, far-field keyword spotting (KWS) algorithm, which was inspired by the human auditory system’s ability to achieve the so-called cocktail party effect in adverse acoustic environments. It introduces the idea of combining microphone-array speech enhancement with machine learning, by incorporating a feedback path from the neural network (NN) KWS...