Artificial IntelligenceNCT05497258
IDEAS-AAP System Diagnoses Acute Abdominal Pain
A study testing Artificial intelligence assistant system in people with Artificial Intelligence and Diagnoses Disease, run by Renmin Hospital of Wuhan University at 1 site in China.
Trial summary
This is a study to validate the effect of the intelligent diagnostic evidence-based analytic system in acute abdominal pain augmentation. Included physicians were randomly assigned into control or AI-assisted group. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence, Diagnoses Disease
- What is being tested?
- Artificial intelligence assistant system
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 151 participants
- Where
- 1 siteChina
- Study dates
- 15 Aug 2022to 1 Oct 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Males or females who are over 18 years old;
- After qualified medical education and obtained the Certificate of medical practitioner;
Who cannot join
- Physicians without qualified medical education and didn't obtain the Certificate of medical practitioner;
- The researcher believes that the subjects are not suitable for participating in clinical trials.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Computer-aided, Evidence-based System Improved Clinical Diagnostic Accuracy of Certificated-Physicians in Acute Abdominal Pain
This is a study to validate the effect of the intelligent diagnostic evidence-based analytic system in acute abdominal pain augmentation. Included physicians were randomly assigned into control or AI-assisted group. In this experiment, the whole electronic health record of each acute abdominal pain patient was divided into two parts, signs and symptoms recording (including chief complaint, present history, physical examination, past medical history, trauma surgery history, personal history, family history, obstetrical history, menstrual history, blood transfusion history, drug allergy history) and auxiliary examination recording (including laboratory examination and radiology report). For each case, the control group readers will first read the signs and symptoms recording of electronic health record and make a clinical diagnosis. Then the readers have to decide to either order a list of auxiliary examinations or confirm the clinical diagnosis without further examination. If the readers choose to order examinations, the corresponding examination results will be feedback to the readers, and the readers can then decide to either continue to order a list of auxiliary examinations or make a confirming diagnosis. Such cycle will last until the reader make a confirming diagnosis. For the AI-assisted readers, the physicians were additionally provided with the feature extracted by IDEAS-AAP, a list of suspicious diagnoses predicted by IDEAS-AAP, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction
Read the detailed description
In recent years, with the continuous development of science and technology, the range of diagnostic tests and biomarkers for disease and treatment modalities has increased exponentially, and medical information has become increasingly complex. This requires the clinician to comprehensively evaluate the patient's condition, so as to choose the best examination and treatment. However, for the complex symptoms in the actual clinical environment, the corresponding diseases are numerous; In the face of complex and heavy clinical work, how to extract the important characteristics of patients' diseases faster and more accurately to achieve high-quality and accurate diagnosis and treatment is the key problem to be solved at present. For example, in the field of digestion, the chief complaint of abdominal pain is one of the most common clinical symptoms of patients seeking medical treatment, and some acute abdominal pain, such as gastrointestinal ulcer perforation, strangulated intestinal obstruction, acute obstructive suppurative cholangitis and other urgent onset, narrow treatment time window, high mortality. Clinicians must make a quick diagnosis and distinguish between those that require emergency intervention and those that do not in order to manage patients in a timely manner and avoid catastrophic events. However, the causes of abdominal pain are many and the mechanisms are complex. In addition, since pain is a subjective sensation and is greatly influenced by subjective factors, there are no clear objective indicators to determine whether or not and the degree of pain, and it is extremely challenging to correctly diagnose and interpret abdominal pain. To this end, the clinician must take a detailed history and perform a thorough physical examination when evaluating a patient's abdominal pain. In recent years, artificial intelligence technology has developed rapidly, especially in the field of medicine has been widely applied research, mainly reflected in the diagnosis and differential diagnosis of diseases, prognosis judgment and clinical decision analysis. Some studies have shown that in terms of auxiliary pathology and imaging diagnosis, AI has reached or even exceeded the average diagnostic level of corresponding specialists. Most of these studies focus on pattern recognition based on images, and the logical judgment based on natural language using medical records information is still in the preliminary development stage. There are no relevant reports on integrating comprehensive information of large medical records to make intelligent prediction of digestive tract diseases.
- Conditions
- Artificial Intelligence, Diagnoses Disease
- Being tested
- Artificial intelligence assistant system
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence assistant systemDevice
The AI-assisted diagnosis system can provide the direction of disease diagnosis in real time and assist the doctor to give the final diagnosis
| Study group | Role | What they receive |
|---|---|---|
| Experimental: with Artificial intelligence assistant system | Experimental | The physicians were additionally provided with the feature extracted by the system, a list of suspicious diagnoses predicted by IDEAS-AAP, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction.
IDEAS-AAP extracted feature from electronic health record, provided a list of suspicious diagnoses, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction. Receives: Device: Artificial intelligence assistant system |
| No Intervention: without Artificial intelligence assistant system | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of clinical diagnosis.
Calculation method = number of right cases / total number of cases 100%
Measured: one week
Secondary outcomes (3)
Accuracy of the prediction of disease based on whole electronic health record
Calculation method = number of right cases / total number of cases 100%
Measured: one week
The prediction of disease based on whole electronic health record and criteria matching
Calculation method = number of right cases / total number of cases 100%
Measured: one week
Time cost of EHR reading
Measured: one week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Aug 2022
- Study start15 Aug 2022
- Primary completion1 Sep 2022
- Study completion1 Oct 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies 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
- Model description
- Experimental: doctor with AI-assisted system;Control：without AI-assisted system
- Masking
- None (open label)
- Enrolment
- 151 participants (actual)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Honggang Yu, MD, Principal investigator, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05497258
- Sponsor's protocol ID
- 2022K-K146(C01)
- Status last verified
- Aug 2022
- First submitted
- 9 Aug 2022
- Last updated
- 8 Nov 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Computer-aided, Evidence-based System Improved Clinical Diagnostic Accuracy of Certificated-Physicians in Acute Abdominal Pain
- ClinicalTrials.gov ID
- NCT05497258
- Acronym / sponsor ID
- 2022K-K146(C01)
- Conditions
- Artificial Intelligence, Diagnoses Disease
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 151 participants (actual)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 15 Aug 2022 (actual)
- Primary completion
- 1 Sep 2022 (actual)
- Study completion
- 1 Oct 2022 (actual)
- First posted
- 11 Aug 2022 (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.
This is a study to validate the effect of the intelligent diagnostic evidence-based analytic system in acute abdominal pain augmentation. Included physicians were randomly assigned into control or AI-assisted group. In this experiment, the whole electronic health record of each acute abdominal pain patient was divided into two parts, signs and symptoms recording (including chief complaint, present history, physical examination, past medical history, trauma surgery history, personal history, family history, obstetrical history, menstrual history, blood transfusion history, drug allergy history) and auxiliary examination recording (including laboratory examination and radiology report). For each case, the control group readers will first read the signs and symptoms recording of electronic health record and make a clinical diagnosis. Then the readers have to decide to either order a list of auxiliary examinations or confirm the clinical diagnosis without further examination. If the readers choose to order examinations, the corresponding examination results will be feedback to the readers, and the readers can then decide to either continue to order a list of auxiliary examinations or make a confirming diagnosis. Such cycle will last until the reader make a confirming diagnosis. For the AI-assisted readers, the physicians were additionally provided with the feature extracted by IDEAS-AAP, a list of suspicious diagnoses predicted by IDEAS-AAP, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction
Detailed description
In recent years, with the continuous development of science and technology, the range of diagnostic tests and biomarkers for disease and treatment modalities has increased exponentially, and medical information has become increasingly complex. This requires the clinician to comprehensively evaluate the patient's condition, so as to choose the best examination and treatment. However, for the complex symptoms in the actual clinical environment, the corresponding diseases are numerous; In the face of complex and heavy clinical work, how to extract the important characteristics of patients' diseases faster and more accurately to achieve high-quality and accurate diagnosis and treatment is the key problem to be solved at present. For example, in the field of digestion, the chief complaint of abdominal pain is one of the most common clinical symptoms of patients seeking medical treatment, and some acute abdominal pain, such as gastrointestinal ulcer perforation, strangulated intestinal obstruction, acute obstructive suppurative cholangitis and other urgent onset, narrow treatment time window, high mortality. Clinicians must make a quick diagnosis and distinguish between those that require emergency intervention and those that do not in order to manage patients in a timely manner and avoid catastrophic events. However, the causes of abdominal pain are many and the mechanisms are complex. In addition, since pain is a subjective sensation and is greatly influenced by subjective factors, there are no clear objective indicators to determine whether or not and the degree of pain, and it is extremely challenging to correctly diagnose and interpret abdominal pain. To this end, the clinician must take a detailed history and perform a thorough physical examination when evaluating a patient's abdominal pain. In recent years, artificial intelligence technology has developed rapidly, especially in the field of medicine has been widely applied research, mainly reflected in the diagnosis and differential diagnosis of diseases, prognosis judgment and clinical decision analysis. Some studies have shown that in terms of auxiliary pathology and imaging diagnosis, AI has reached or even exceeded the average diagnostic level of corresponding specialists. Most of these studies focus on pattern recognition based on images, and the logical judgment based on natural language using medical records information is still in the preliminary development stage. There are no relevant reports on integrating comprehensive information of large medical records to make intelligent prediction of digestive tract diseases.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Experimental: doctor with AI-assisted system;Control：without AI-assisted system
| Arm | Type | Receives |
|---|---|---|
| Experimental: with Artificial intelligence assistant system | Experimental | Device: Artificial intelligence assistant systemThe physicians were additionally provided with the feature extracted by the system, a list of suspicious diagnoses predicted by IDEAS-AAP, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction. IDEAS-AAP extracted feature from electronic health record, provided a list of suspicious diagnoses, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction. |
| No Intervention: without Artificial intelligence assistant system | No intervention | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Males or females who are over 18 years old;
- After qualified medical education and obtained the Certificate of medical practitioner;
Key exclusion criteria (2 listed)
- Physicians without qualified medical education and didn't obtain the Certificate of medical practitioner;
- The researcher believes that the subjects are not suitable for participating in clinical trials.
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 assistant systemThe AI-assisted diagnosis system can provide the direction of disease diagnosis in real time and assist the doctor to give the final diagnosis | Device | Experimental: with Artificial intelligence assistant system |
Primary outcome
The accuracy of clinical diagnosis.
Time frame: one week
Calculation method = number of right cases / total number of cases 100%
3 secondary outcomes
Accuracy of the prediction of disease based on whole electronic health record
Time frame: one week
The prediction of disease based on whole electronic health record and criteria matching
Time frame: one week
Time cost of EHR reading
Time frame: one week
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Nov 2022.
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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. This study is not recruiting at the moment.
Honggang Yu, MD principal investigator
Renmin Hospital of Wuhan University
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 assistant system in people with Artificial Intelligence and Diagnoses Disease, run by Renmin Hospital of Wuhan University 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 2 inclusion and 2 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: finished, as of the sponsor's last update on 8 Nov 2022. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of clinical diagnosis. (time frame: one week). 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 8 Nov 2022; AzTrials last refreshed its copy on 2 Nov 2026.