Development and Validation of a Prognostic Prediction Model for Long COVID Cough Based on Multimodal Deep Learning Fusio

基于多模态深度学习融合的长新冠咳嗽预后预测模型构建与验证

50,000

Long COVID cough (LCC) is one of the most common respiratory sequelae after SARS-CoV-2 infection, yet studies exploring its underlying mechanisms and prognosis remain scarce. RNA-seq analysis of peripheral blood mononuclear cells collected at patients’ baseline enrollment demonstrates that chromatin structure is closely correlated with improved LCC prognosis. Nevertheless, accurate tools for LCC prognostic assessment are currently unavailable. Relying on bilateral collaboration, this project intends to develop a multimodal deep learning model for precise prognostic prediction of LCC by integrating clinical indicators, chest CT scans, pulmonary function test data and chromatin structural images. The model will support early identification of patients with refractory LCC and provide references for clinical treatment formulation.