Chest x-rays using a deep learning-based model.
By Vijay Kumar MalesuReviewed by Lily Ramsey, LLMJul 11 2024 In a recent study published in The Lancet Digital Health, a group of researchers estimated forced vital capacity and forced expiratory volume in 1 second from chest x-rays using a deep learning -based model.
Chest x-rays, widely used and correlated with pulmonary function, offer an alternative approach. Further research is needed to improve methods for estimating pulmonary function in diverse clinical settings and patient populations. Performance was evaluated by calculating Pearson correlation coefficient , intraclass correlation coefficient , Root Mean Square Error , Mean Square Error , and Mean Absolute Error between predicted and actual spirometry values.
The training dataset included 108,366 x-rays from 61,009 patients , while the validation dataset included 13,180 x-rays from 7,381 patients . The internal test dataset had 12,761 x-rays from 7,378 patients . For FEV1 determination, institution D had an r value of 0.91 and institution E also had an r value of 0.91. ICC values were 0.90 for both institutions, MSE values were 0.13 L² and 0.11 L², RMSE values were 0.37 L and 0.33 L, and MAE values were 0.28 L and 0.25 L, respectively.
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