Work

Prediction of chronic obstructive pulmonary disease based on multimodal data and deep learning

Year: 2025

Type: article

Abstract: To address the issues of insufficient utilization of multimodal information, modal heterogeneity, and data gaps leading to poor model generalization in early prediction of chronic obstructive pulmonary disease (COPD), a deep learning-based multimodal dynamic fusion network (MMDF-Net) is proposed. This model integrates chest CT images, pulmonary function indicators, and environmental exposure data. It aligns image and non-image features via a dual-tower cross-modal contrastive learning module, mitigating semantic differences across modalities. A conditional generative adversarial network is used to generate high-fidelity environmental exposure data, reducing reliance on data completeness. A dynamic gating fusion mechanism adaptively adjusts multimodal weights based on patient smoking history, age, and other attributes to suppress noise. On the COPD Gene dataset, MMDF-Net achieves an area under the curve (AUC) of 0.92, a sensitivity of 92.3%, and a specificity of 88.7%, significantly outperforming single-modal models and dynamically adjusting weights according to disease stage. These results demonstrate that this multimodal dynamic fusion strategy can effectively address data heterogeneity and individual differences, providing technical support for precise early intervention in COPD.

Source: APL Bioengineering

DOI: 10.1063/5.0289414

Authors: Haoran Deng, Xuchun Ding, Shiping Zhu, Xue Song, Yufei Guo

Institutions: Zhejiang Chinese Medical University, Hangzhou Hospital of Traditional Chinese Medicine

Language: English

Topic: Chronic Obstructive Pulmonary Disease (COPD) Research

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,200