Deep Learning to Predict Mortality After Cardiothoracic Surgery Using Preoperative Chest Radiographs.

ABSTRACT:

Background

The Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) estimates mortality risk only for certain common procedures (eg, coronary artery bypass or valve surgery) and is cumbersome, requiring greater than 60 inputs. We hypothesized that deep learning can estimate postoperative mortality risk based on a preoperative chest radiograph for cardiac surgeries in which STS-PROM scores were available (STS index procedures) or unavailable (non-STS index procedures).

Methods

We developed a deep learning model (CXR-CTSurgery) to predict postoperative mortality based on preoperative chest radiographs in 9283 patients at Massachusetts General Hospital (MGH) having cardiac surgery before April 8, 2014. CXR-CTSurgery was tested on 3615 different MGH patients and e

SUBMITTER: Raghu VK 

PROVIDER: S-EPMC11373441 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature