Deep Learning to Assess Long-term Mortality From Chest Radiographs.

ABSTRACT:

Importance

Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis.

Objective

To develop and test a convolutional neural network (CNN) (named CXR-risk) to predict long-term mortality, including noncancer death, from chest radiographs.

Design, setting, and participants

In this prognostic study, CXR-risk CNN development (n = 41 856) and testing (n = 10 464) used data from the screening radiography arm of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) (n = 52 320), a community cohort of asymptomatic nonsmokers and smokers (aged 55-74 years) enrolled at 10 US sites from November 8, 1993, through July 2, 2001. External testing used data from the screening radiography

SUBMITTER: Lu MT 

PROVIDER: S-EPMC6646994 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature