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

• Published in: European radiology 2024-08, Vol.34 (8), p.5066-5076
• Main Authors: Liu, Tong, Zhang, Zheng-hua, Zhou, Qi-hao, Cheng, Qing-zhao, Yang, Yue, Li, Jia-shu, Zhang, Xue-mei, Zhang, Jian-qing
• Format: Article
• Language: English
• Subjects: Accuracy; Adult; Aged; Antibiotics; Aspergillus; Communications systems; Computed tomography; Deep Learning; Diagnosis; Diagnosis, Differential; Diagnostic Radiology; Diagnostic systems; Female; Hospitals; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Male; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Neural networks; Neuroradiology; Patients; Pneumonia; Pneumonia, Staphylococcal - diagnostic imaging; Pneumonia, Staphylococcal - microbiology; Pulmonary Aspergillosis - diagnostic imaging; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Staphylococcus aureus; Staphylococcus aureus - isolation & purification; Thorax; Tomography, X-Ray Computed - methods; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10578-3

Objective To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between Staphylococcus aureus pneumonia (SAP) and Aspergillus pneumonia (ASP) and to evaluate the significant correlation of each clinical feature in determining these two types of pneumonia using a random forest dichotomous diagnosis model. This will enhance diagnostic accuracy and efficiency in distinguishing between SAP and ASP. Methods In this study, 60 patients with clinically confirmed SAP and ASP, who were admitted to four large tertiary hospitals in Kunming, China, were included. Thoracic high-resolution CT lung windows of all patients were extracted from the picture archiving and communication system, and the corresponding clinical data of each patient were collected. Results The MI-DenseCFNet diagnosis model demonstrates an internal validation set with an area under the curve (AUC) of 0.92. Its external validation set demonstrates an AUC of 0.83. The model requires only 10.24s to generate a categorical diagnosis and produce results from 20 cases of data. Compared with high-, mid-, and low-ranking radiologists, the model achieves accuracies of 78% vs. 75% vs. 60% vs. 40%. Eleven significant clinical features were screened by the random forest dichotomous diagnosis model. Conclusion The MI-DenseCFNet multimodal diagnosis model can effectively diagnose SAP and ASP, and its diagnostic performance significantly exceeds that of junior radiologists. The 11 important clinical features were screened in the constructed random forest dichotomous diagnostic model, providing a reference for clinicians. Clinical relevance statement MI-DenseCFNet could provide diagnostic assistance for primary hospitals that do not have advanced radiologists, enabling patients with suspected infections like Staphylococcus aureus pneumonia or Aspergillus pneumonia to receive a quicker diagnosis and cut down on the abuse of antibiotics. Key points • MI-DenseCFNet combines deep learning neural networks with crucial clinical features to discern between Staphylococcus aureus pneumonia and Aspergillus pneumonia. • The comprehensive group had an area under the curve of 0.92, surpassing the proficiency of junior radiologists. • This model can enhance a primary radiologist’s diagnostic capacity.