Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography.

Journal: American journal of respiratory and critical care medicine

PMID: 28892454

RATIONALE: Deep learning is a powerful tool that may allow for improved outcome prediction.

Germán González

1 Sierra Research, Alicante, Spain.
Samuel Y Ash

3 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston Massachusetts.
Gonzalo Vegas-Sánchez-Ferrero

2 Applied Chest Imaging Laboratory, Department of Radiology, and.
Jorge Onieva Onieva

2 Applied Chest Imaging Laboratory, Department of Radiology, and.
Farbod N Rahaghi

3 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston Massachusetts.
James C Ross

2 Applied Chest Imaging Laboratory, Department of Radiology, and.
Alejandro Díaz

2 Applied Chest Imaging Laboratory, Department of Radiology, and.
Raúl San José Estépar

2 Applied Chest Imaging Laboratory, Department of Radiology, and.
George R Washko

3 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston Massachusetts.

Aged Databases, Factual Deep Learning Female Humans Logistic Models Male Middle Aged Predictive Value of Tests Prognosis Pulmonary Disease, Chronic Obstructive Respiratory Distress Syndrome Respiratory Function Tests Risk Assessment Severity of Illness Index Smoking Survival Rate Tomography, X-Ray Computed

View on PubMed Access via DOI PubMed (28892454)