摘要
Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide.Accurate diagnosis and early intervention are crucial to improve patient outcomes.Artificial intelligence(AI)has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis,enabling more precise investigation and individualized clinical management.In this study,we successfully developed a multimodal integration(MMI)pipeline to differentiate among bacterial,fungal,and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 patients.The area under the curve(AUC)of the MMI system comprising clinical text and computed tomography(CT)image scans yielded 0.910(95%confidence interval[CI]:0.904–0.916)and 0.887(95%CI:0.867–0.909)in the internal and external testing datasets respectively,which were comparable to those of experienced physicians.Furthermore,the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822(95%CI:0.805–0.837)and bacterial subtypes with a mean AUC of 0.803(95%CI:0.775–0.830).Here,the MMI system harbors the potential to guide tailored medication recommendations,thus mitigating the risk of antibiotic misuse.Additionally,the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness,contributing to more informed clinical decision-making.To revolutionize medical care,embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.
基金
supported by the National Natural Science Foundation of China(82341083,82100119)
the Science and Technology Project of Sichuan(2020YFG0473,2022ZDZX0018)
the Beijing Municipal Science and Technology Planning Project(Z211100003521009)
Hong Kong Research Grants Council through General Research Fund(Grant 17207722)
the Sichuan University from“0”to“1”Innovation Project(2023SCUH0051)
the 1.3.5 Project for Disciplines Excellence of West China Hospital,Sichuan University(ZYYC23027)。
