A multi-modal deep learning solution for precise pneumonia diagnosis: the PneumoFusion-Net model.

Area of Science:

• Medical Imaging and Artificial Intelligence
• Computational Pathology
• Diagnostic Medicine

Background:

• Pneumonia remains a leading cause of global morbidity and mortality.
• Distinguishing between bacterial and viral pneumonia is clinically challenging due to overlapping symptoms.
• Current diagnostic methods, relying on imaging and clinical experience, can be inefficient and inconsistent.

Purpose of the Study:

• To develop and evaluate a deep learning-based multimodal framework for enhanced pneumonia classification.
• To integrate diverse data sources, including CT images, clinical text, lab results, and radiology reports, for improved diagnostic accuracy.

Main Methods:

• Introduction of PneumoFusion-Net, a novel deep learning multimodal framework.
• Utilized a dataset of 10,095 pneumonia CT images with associated clinical data.
• Employed five-fold cross-validation to assess model performance using accuracy and F1-Score metrics.

Main Results:

• PneumoFusion-Net achieved 98.96% classification accuracy and a 98% F1-score on a held-out test set.
• The model demonstrated high effectiveness in differentiating bacterial from viral pneumonia.
• Results indicate reduced misdiagnosis and improved data homogeneity across patient datasets.

Conclusions:

• PneumoFusion-Net provides an effective and efficient method for pneumonia classification through multimodal data integration.
• The framework offers high diagnostic accuracy, with potential for significant clinical integration.
• PneumoFusion-Net can serve as a robust, automated diagnostic tool for radiologists and clinicians, reducing diagnostic burden.