AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients
This addresses the challenge of rapid, noninvasive sepsis detection for intensive care unit patients, representing a domain-specific incremental advance.
The researchers tackled the problem of early sepsis diagnosis and mortality prediction in critically ill patients by using deep learning on hyperspectral imaging data, achieving AUROCs of 0.80 for sepsis and 0.72 for mortality with imaging alone, and improving to 0.94 and 0.83 with additional clinical data.
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcirculatory alterations, we propose a deep learning approach to automated sepsis diagnosis and mortality prediction using a single HSI cube acquired within seconds. In a prospective observational study, we collected HSI data from the palms and fingers of more than 480 intensive care unit patients. Neural networks applied to HSI measurements predicted sepsis and mortality with areas under the receiver operating characteristic curve (AUROCs) of 0.80 and 0.72, respectively. Performance improved substantially with additional clinical data, reaching AUROCs of 0.94 for sepsis and 0.83 for mortality. We conclude that deep learning-based HSI analysis enables rapid and noninvasive prediction of sepsis and mortality, with a potential clinical value for enhancing diagnosis and treatment.