Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers.

Background: Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.

Methods: We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.

Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans.

Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans.

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality.

Purpose: Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs).

Methods: Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed.

Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer.

Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans developed in cardiovascular radiology can optimize treatment planning in radiation oncology. The system was trained using multi-center data (n = 858) with manual heart segmentations provided by cardiovascular radiologists.

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT.

Small whole heart volume predicts cardiovascular events in patients with stable chest pain: insights from the PROMISE trial.

Objectives: The size of the heart may predict major cardiovascular events (MACE) in patients with stable chest pain. We aimed to evaluate the prognostic value of 3D whole heart volume (WHV) derived from non-contrast cardiac computed tomography (CT).

Methods: Among participants randomized to the CT arm of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE), we used deep learning to extract WHV, defined as the volume of the pericardial sac.

Radiomics of Coronary Artery Calcium in the Framingham Heart Study.

Purpose: To extract radiomic features from coronary artery calcium (CAC) on CT images and to determine whether this approach could improve the ability to identify individuals at risk for a composite endpoint of clinical events.

Materials And Methods: Participants from the Offspring and Third Generation cohorts of the community-based Framingham Heart Study underwent noncontrast cardiac CT (2002-2005) and were followed for more than a median of 9.1 years for composite major events.

The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer.

Introduction: Limited stage small cell lung cancer (LS-SCLC) has a poor prognosis. Additional prognostic markers are needed for risk-stratification and treatment intensification. This study compares quantitative CT-based volumetric tumor measurements versus International Association for the Study of Lung Cancer (IASLC) TNM staging to predict outcomes.

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging.

Purpose: Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking lesions over space and time may be trivial, the development of clinically relevant, automated radiomics methods that incorporate serial imaging data is far more challenging. In this study, we evaluated deep learning networks for predicting clinical outcomes through analyzing time series CT images of patients with locally advanced non-small cell lung cancer (NSCLC).

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

Background: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.

Methods And Findings: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.

PanoramicData: Data Analysis through Pen & Touch.

Interactively exploring multidimensional datasets requires frequent switching among a range of distinct but inter-related tasks (e.g., producing different visuals based on different column sets, calculating new variables, and observing the interactions between sets of data).

A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer.

We present a practical technique for using a writer-independent recognition engine to improve the accuracy and speed while reducing the training requirements of a writer-dependent symbol recognizer. Our writer-dependent recognizer uses a set of binary classifiers based on the AdaBoost learning algorithm, one for each possible pairwise symbol comparison. Each classifier consists of a set of weak learners, one of which is based on a writer-independent handwriting recognizer.

Drawing on air: input techniques for controlled 3D line illustration.

We present Drawing on Air, a haptic-aided input technique for drawing controlled 3D curves through space. Drawing on Air addresses a control problem with current 3D modeling approaches based on sweeping movement of the hands through the air. While artists praise the immediacy and intuitiveness of these systems, a lack of control makes it nearly impossible to create 3D form beyond quick design sketches or gesture drawings.

An office-based orthotic system in treatment of the arthritic foot.

Patients with inflammatory arthritic disease of the feet invariably require conservative office management. The simplest method of obtaining relief has been to use commercially available extra-depth shoes combined with custommade, removable, closed-celled polyethylene foam arch supports. These supports are soft, light, and can be accurately adjusted to each patient's pathology.

The yale cervical orthosis: an evaluation of its effectiveness in restricting cervical motion in normal subjects and a comparison with other cervical orthoses.

The Yale cervical orthosis is a lightweight polyethylene foam Philadelphia collar with molded fiberglass extensions over the thorax. This orthosis was studied on 17 normal subjects in the extremes of the ranges of flexion, extension, rotation, and lateral bending using roentgenograms and axial photographs to assess how effectively it limited motion of the neck. Overall, it satisfactorily controlled cervical motion and was similar to the most effective rigid cervical orthoses.