MI-DenseCFNet: deep learning–based multimodal diagnosis models for Aureus and Aspergillus pneumonia

Category	Detail
Condition	Pneumonia caused by Staphylococcus aureus (SAP) and Aspergillus (ASP)
Key Mechanisms	Multimodal deep learning model combining CT imaging features and clinical data for pathogen-specific pneumonia diagnosis
Target Population	Patients with clinically confirmed SAP or ASP pneumonia
Care Setting	Tertiary hospital settings with access to CT imaging and clinical data

Pneumonia pathogens SAP and ASP have overlapping and distinct CT imaging features, complicating diagnosis by inexperienced physicians.
MI-DenseCFNet integrates DenseNet-201 extracted CT image features with clinical data via deep neural networks to improve diagnostic accuracy.
Early and accurate pathogen identification aids targeted treatment decisions and improves prognosis compared to empirical therapy.

Use American Thoracic Society guidelines for clinical diagnosis of pneumonia.
Confirm pathogen identification by deep sputum culture, blood culture, bronchoalveolar lavage culture, histopathology, or macrogenomic sequencing as gold standard.
Utilize high-resolution chest CT imaging with standardized acquisition parameters for lesion characterization.
Apply multimodal diagnostic models combining imaging and clinical features to improve early pathogen differentiation.

Tailor antimicrobial therapy based on early and accurate identification of SAP versus ASP to avoid empirical treatment delays.
Consider clinical features and imaging signs such as air crescent sign, air sacs, and halo sign for pathogen-specific management.

Monitor lesion size and lung involvement severity via serial CT imaging to assess treatment response.
Use clinical and laboratory data in conjunction with imaging for ongoing patient assessment.

Delayed pathogen identification may lead to unfavorable outcomes due to inappropriate empirical drug use.
Misinterpretation of imaging features by inexperienced clinicians can result in diagnostic errors.

Patients with confirmed SAP or ASP pneumonia from tertiary hospitals in Kunming, China

Early multimodal diagnosis supports targeted antimicrobial therapy, potentially improving clinical outcomes and reducing empirical treatment risks.

Exclude patients with confounding conditions such as heart failure, pulmonary surgery history, diffuse interstitial lung disease, connective tissue disease, pulmonary malignancy, active tuberculosis, or pulmonary embolism to ensure diagnostic accuracy.
Standardize CT imaging acquisition and preprocessing including normalization and data augmentation to optimize deep learning model performance.
Combine imaging features extracted by DenseNet-201 with clinical data processed by deep neural networks for comprehensive diagnostic modeling.
Validate diagnostic models on well-characterized patient cohorts with confirmed pathogen identification.

Clinical Scorecard: MI-DenseCFNet: A Deep Learning Approach for Multimodal Diagnosis of Pneumonia Caused by Aureus and Aspergillus