Exploring the Application Efficacy of Artificial Intelligence (AI) Diagnostic Tools in Medical Imaging (MI) of Respiratory(R) Infectious (I) Disease (D) (AI-MIRID)

Fudan University

Status: Enrolling

Conditions: Artificial Intelligence; Respiratory Infectious Diseases; Medical Imaging

Treatments: Other: Artificial Intelligence-based medical imaging interpretation

Study type: Interventional

Funder types: Other

Identifiers

NCT06553911

AI-MIRID

Details and patient eligibility

About

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.

Enrollment: 2,000 estimated patients

Sex: All

Ages: 1 to 90 years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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2,000 participants in 2 patient groups

No Intervention

No Intervention group

Description:

Non-intervention

Artificial Intelligence-based medical imaging interpretation group

Experimental group

Description:

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.

Treatment:

Other: Artificial Intelligence-based medical imaging interpretation