• Germán González, Samuel Y Ash, Gonzalo Vegas-Sánchez-Ferrero, Onieva OnievaJorgeJ2 Applied Chest Imaging Laboratory, Department of Radiology, and., Farbod N Rahaghi, James C Ross, Alejandro Díaz, San José EstéparRaúlR0000-0002-3677-19962 Applied Chest Imaging Laboratory, Department of Radiology, and., George R Washko, and COPDGene and ECLIPSE Investigators.
• 1 Sierra Research, Alicante, Spain.
• Am. J. Respir. Crit. Care Med. 2018 Jan 15; 197 (2): 193203193-203.

RationaleDeep learning is a powerful tool that may allow for improved outcome prediction.ObjectivesTo determine if deep learning, specifically convolutional neural network (CNN) analysis, could detect and stage chronic obstructive pulmonary disease (COPD) and predict acute respiratory disease (ARD) events and mortality in smokers.MethodsA CNN was trained using computed tomography scans from 7,983 COPDGene participants and evaluated using 1,000 nonoverlapping COPDGene participants and 1,672 ECLIPSE participants. Logistic regression (C statistic and the Hosmer-Lemeshow test) was used to assess COPD diagnosis and ARD prediction. Cox regression (C index and the Greenwood-Nam-D'Agnostino test) was used to assess mortality.Measurements And Main ResultsIn COPDGene, the C statistic for the detection of COPD was 0.856. A total of 51.1% of participants in COPDGene were accurately staged and 74.95% were within one stage. In ECLIPSE, 29.4% were accurately staged and 74.6% were within one stage. In COPDGene and ECLIPSE, the C statistics for ARD events were 0.64 and 0.55, respectively, and the Hosmer-Lemeshow P values were 0.502 and 0.380, respectively, suggesting no evidence of poor calibration. In COPDGene and ECLIPSE, CNN predicted mortality with fair discrimination (C indices, 0.72 and 0.60, respectively), and without evidence of poor calibration (Greenwood-Nam-D'Agnostino P values, 0.307 and 0.331, respectively).ConclusionsA deep-learning approach that uses only computed tomography imaging data can identify those smokers who have COPD and predict who are most likely to have ARD events and those with the highest mortality. At a population level CNN analysis may be a powerful tool for risk assessment.

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