Exploring the Application Efficacy of Artificial Intelligence (AI) Diagnostic Tools in Medical Imaging (MI) of Respiratory(R) Infectious (I) Disease (D)
Artificial Intelligence-based medical imaging interpretation
Artificial Intelligence-based medical imaging interpretation: 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.
Study summary
The early identification and severe warning of acute respiratory infectious diseases are of paramount importance. Utilizing effective means to make correct diagnoses of the source of infection at an early stage is the premise of all effective measures. AI-MID is a research initiative that uses artificial intelligence tools to assist in the clinical medical imaging diagnosis of respiratory diseases, aiming to reduce the time doctors spend reviewing images, increase work efficiency, and enhance the sensitivity and specificity of pneumonia detection, thereby improving the detection rate of pneumonia at the grassroots level. This approach facilitates precise prevention, accurate diagnosis, and precise treatment.
Eligibility
Sex
ALL
Min age
1 Year
Max age
90 Years
Healthy volunteers
Accepted
Inclusion Criteria:
1. 1-90 years old, gender not specified.
2. Exhibits symptoms of respiratory tract infection
3. Must have etiological examination results
4. Must have imaging data;
Exclusion Criteria:
1. Severe artifacts in medical images
2. Clinical diagnosis indicates concurrent pulmonary edema
3. Dual review results in unclear diagnosis or potential misdiagnosis
4. Other situations that may cause difficulties in reading the films, or as determined by the researcher, the study participant is deemed unsuitable for enrollment.
Primary outcome measure(s)
Evaluating the Diagnostic Efficacy of Artificial Intelligence Diagnostic Tools in Medical Imaging of Respiratory Infectious Diseases — 2 years 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.
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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