2024-01-16 10:11:23
Research team led by Professor Kyeong-Soo Jeong, Department of Respiratory Medicine, Yonsei University Severance Hospital
Entered 2024.01.16 19:10 Views 26 Entered 2024.01.16 19:10 Modified 2024.01.16 15:34 Views 26
An artificial intelligence (AI) model has been developed that can accurately and quickly diagnose sepsis and even predict prognosis. [사진=클립아트코리아]An artificial intelligence (AI) model has been developed that can accurately and quickly diagnose sepsis and even predict prognosis. Sepsis is a disease that causes damage to major organs due to an abnormal body response to microbial infection. The mortality rate due to severe sepsis increases to 35%, and up to 60% when accompanied by septic shock.
The research team of Professor Yu-Rang Park of the Department of Biomedical Systems Information at Yonsei University College of Medicine and Professor Gyeong-Soo Jeong of the Department of Respiratory Medicine at Severance Hospital developed an AI model that can diagnose sepsis and predict prognosis using 3D image data of CD8 T cells that kill virus-infected cells or tumor cells. developed. The accuracy is 99%.
Because the immune response to sepsis is complex and varies from patient to patient, early diagnosis and prompt action are important. Because it quickly affects multiple organs, the likelihood of death increases if treatment is delayed.
The representative biomarkers currently used to diagnose sepsis, such as C-reactive protein (CRP) and procalcitonin (PCT), have delayed diagnosis, resulting in delayed responses. Additionally, biomarkers such as interleukin-6 (IL-6), an inflammatory marker, lacked standardization, making it difficult to interpret diagnostic results. Therefore, there was a need to discover new biomarkers.
The research team examined whether the diagnosis and prognosis of sepsis could be predicted using CD8 T cell image data and AI models. CD8 T cells were isolated from blood samples of eight people in the sepsis recovery group (including those who died) and images were taken. Filming was conducted at three time points: when septic shock was diagnosed, when septic shock was resolved, and before discharge, and a holotomography microscope was used. Holotomography can quickly and reliably obtain 3D images of living immune cells without a staining process that affects changes in cell structure.
Images taken at each time point were compared and analyzed with images of 20 healthy control subjects using an AI classification model. At this time, the images obtained when diagnosing septic shock were used to evaluate the possibility of diagnosing septic shock, and the images obtained when diagnosing septic shock in the surviving and deceased patient groups were used to predict the prognosis of septic shock.
The prediction performance of the AI model was analyzed using the receiver operating characteristic curve (AUROC) index. AUROC means ‘area under the ROC curve’. This is a statistical technique that indicates the diagnostic accuracy of a specific testing tool and is used as a performance evaluation indicator for AI models. Typically, the closer the area is to 1, the better the performance, and if the area is 0.8 or higher, it is evaluated as a high-performance model.
As a result of the analysis, when only one CD8 T cell image was used to diagnose sepsis, the AI model’s prediction accuracy (AUROC) was 0.96 (96%), and when two cell images were used, performance was higher than 0.99 (99%). It seemed. The prognosis prediction model also showed an accuracy of 0.98 (98%) using a single cell image, and showed high performance of over 0.99 (99%) when using two cell images.
Professor Gyeongsoo Jeong, who led the research, said, “Through this study, we were able to identify the role of 3D images of CD8 T cells as a biomarker for sepsis.” He added, “By quickly and accurately diagnosing and predicting prognosis for sepsis patients through an AI model, we “We expect that it will be able to help make appropriate treatment decisions for individual patients,” he said.
The results of this study were published in the latest issue of the international academic journal Light: Science&Application.
Reporter Lim Jong-eon
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