Harnessing the power of AI: deep-learning model forecasts 30-day mortality in pneumonia cases AJR_Radiology AI artificalintelligence machinelearning deeplearning deeplearningmodel DL pneumonia
By Pooja Toshniwal PahariaJul 5 2023Reviewed by Lily Ramsey, LLM In a recent study published in the American Journal of Roentgenology, researchers developed a deep learning model to estimate 30.0-day mortality risk among community-acquired pneumonia patients using chest x-rays obtained for diagnosis as inputs.They also validated the model's performance among patients from different institutions and periods.
About the study In the present retrospective study, researchers developed and externally validated a DL-based model predictor of death within 30.0 days among CAP patients using initial chest radiographs. They also evaluated the model at two other institutions, i.e., the Seoul Metropolitan Government-Seoul National University Boramae Medical Center between January and March 2020, and Chung-Ang University Hospital between March 2019 and October 2021.
The deep learning model was devised with a 3.0: 1.0: 1.0 participant distribution to the training group, validation group, and internal testing group to estimate the 30.0-day any-cause death risk among CAP patients with their chest radiography scans analyzed at the time of diagnosis as inputs. Compared to CURB-65, the DL model had similar sensitivity but higher specificity, with a positive predictive value of 35% vs. 18% and a negative predictive value of 95% vs. 94%. The DL model exhibited acceptable calibration in the temporal test group but significantly overestimated the risk of 30-day mortality in external test cohorts A and B.
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