Respiratory Infectious DiseasesAI-MIRIDNCT06553911
Exploring the Application Efficacy of Artificial Intelligence (AI) Diagnostic Tools in Medical Imaging (MI) of Respiratory(R) Infectious (I) Disease (D)
A study testing Artificial Intelligence-based medical imaging interpretation in people with Respiratory Infectious Diseases and Artificial Intelligence, run by Huashan Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 1 year to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Respiratory Infectious Diseases, Artificial Intelligence, Medical Imaging
- What is being tested?
- Artificial Intelligence-based medical imaging interpretation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2022to 1 Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 year to 90 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- 1-90 years old, gender not specified.
- Exhibits symptoms of respiratory tract infection
- Must have etiological examination results
- Must have imaging data;
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Huashan Hospital
Shanghai, 200040
Wenhong Zhang, Professor, (86)52889999, zhangwenhong@fudan.edu.cn
About this study
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.
- Being tested
- Artificial Intelligence-based medical imaging interpretation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-based medical imaging interpretationOther
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 group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Artificial Intelligence-based medical imaging interpretation |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 2 years
Secondary outcomes (1)
Utilizing artificial intelligence tools for early identification and severe warning of respiratory infectious diseases
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.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Aug 2024
- Study start1 Apr 2022
- Primary completion1 Dec 2025 (estimated)
- Study completion1 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Respiratory Infectious DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huashan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wen-hong Zhang, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06553911
- Sponsor's protocol ID
- AI-MIRID
- Acronym
- AI-MIRID
- Status last verified
- Aug 2024
- First submitted
- 7 Aug 2024
- Last updated
- 14 Aug 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Exploring the Application Efficacy of Artificial Intelligence (AI) Diagnostic Tools in Medical Imaging (MI) of Respiratory(R) Infectious (I) Disease (D)
- ClinicalTrials.gov ID
- NCT06553911
- Acronym / sponsor ID
- AI-MIRID / AI-MIRID
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Huashan HospitalUniversity, hospital or other
- Study start
- 1 Apr 2022 (actual)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 1 Dec 2026 (estimated)
- First posted
- 14 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| No Intervention | No intervention | –Non-intervention |
| Artificial Intelligence-based medical imaging interpretation group | Experimental | Other: Artificial Intelligence-based medical imaging interpretationUsing 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. |
Who may qualify
- Ages
- 1 year to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- 1-90 years old, gender not specified.
- Exhibits symptoms of respiratory tract infection
- Must have etiological examination results
- Must have imaging data;
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence-based medical imaging interpretationIn 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. | Other | Artificial Intelligence-based medical imaging interpretation group |
Primary outcome
Evaluating the Diagnostic Efficacy of Artificial Intelligence Diagnostic Tools in Medical Imaging of Respiratory Infectious Diseases
Time frame: 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.
1 secondary outcome
Utilizing artificial intelligence tools for early identification and severe warning of respiratory infectious diseases
Time frame: 2 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Aug 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence-based medical imaging interpretation in people with Respiratory Infectious Diseases and Artificial Intelligence, run by Huashan Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 14 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wenhong Zhang ((86)52889999, zhangwenhong@fudan.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Evaluating the Diagnostic Efficacy of Artificial Intelligence Diagnostic Tools in Medical Imaging of Respiratory Infectious Diseases (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Aug 2024.