Non-intervention

Using clinical information, imaging data, and corresponding etiological results of the study participants, an AI diagnostic tool is established to specifically recognize patients' chest medical imaging and construct corresponding diagnostic conclusions.

In the AI interpretation group, using clinical information, imaging data, and corresponding etiological results of the study participants, an AI diagnostic tool is established to specifically recognize patients' chest medical imaging and construct corresponding diagnostic conclusions.

To evaluate the diagnostic efficacy of computer-aided detection (CAD) software in the identification of pulmonary infections, the study will employ the following methods:

Imaging Criteria: Experienced radiologists will interpret the medical imaging of study participants, serving as the imaging standard.

Computer-Aided Detection: Concurrently, the CAD software will analyze the participants' medical imaging to generate diagnostic results.

Efficacy Assessment: The accuracy and consistency of the CAD software will be evaluated by comparing its interpretations with the diagnoses made by the radiologists.

By integrating the medical imaging of study participants with the corresponding respiratory pathogen detection results, these data will be used as the training set input into the AI diagnostic tool, enabling it to undergo deep learning. This process will establish an AI diagnostic tool based on pathogen imaging. After completing the data collection for both retrospective and prospective study sections, we plan to evaluate the disease progression and prognosis of the study participants based on survival analysis and predictive modeling. By integrating clinical data and imaging data, we aim to enhance the accuracy and precision of the prognostic assessment model. The model will be continuously optimized according to the changes in the conditions of study participants enrolled over different time periods.