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

NCT06553911 | Recruiting DMC

• 1 other identifier: AI-MIRID
• Study Type: interventional
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Respiratory Infectious Diseases; Artificial Intelligence; Medical Imaging

Outcome Measures

Primary Outcomes (1)

• Evaluating the Diagnostic Efficacy of Artificial Intelligence Diagnostic Tools in Medical Imaging of Respiratory Infectious Diseases

  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.

  2 years

Secondary Outcomes (1)

• Utilizing artificial intelligence tools for early identification and severe warning of respiratory infectious diseases

  2 years

Study Arms (2)

No Intervention

NO INTERVENTION

Non-intervention

Artificial Intelligence-based medical imaging interpretation group

EXPERIMENTAL

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.

Other: Artificial Intelligence-based medical imaging interpretation

Interventions

Artificial Intelligence-based medical imaging interpretation OTHER

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.

Artificial Intelligence-based medical imaging interpretation group

Eligibility Criteria

• Age: 1 Year - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• years old, gender not specified.
• Exhibits symptoms of respiratory tract infection
• Must have etiological examination results
• Must have imaging data;

You may not qualify if:

• Severe artifacts in medical images
• Clinical diagnosis indicates concurrent pulmonary edema
• Dual review results in unclear diagnosis or potential misdiagnosis
• Other situations that may cause difficulties in reading the films, or as determined by the researcher, the study participant is deemed unsuitable for enrollment.

Sponsors & Collaborators

• Huashan Hospital: lead

Study Sites (1)

Huashan Hospital

Shanghai, 200040, China

RECRUITING

Central Study Contacts

Wenhong Zhang

CONTACT

(86)52889999; zhangwenhong@fudan.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director of Division of Infectious Diseases

Study Record Dates

• First Submitted: August 7, 2024
• First Posted: August 14, 2024
• Study Start: April 1, 2022
• Primary Completion: December 1, 2025
• Study Completion (Estimated): December 1, 2026
• Last Updated: August 14, 2024

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)