IDEAS-AAP System Diagnoses Acute Abdominal Pain

NCT05497258 | Completed

• 1 other identifier: 2022K-K146(C01)
• Study Type: interventional
• Target: 151
• Locations: 1 country
• Sites: 1

Brief 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. 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

Conditions

Artificial Intelligence; Diagnoses Disease

Outcome Measures

Primary Outcomes (1)

• The accuracy of clinical diagnosis.

  Calculation method = number of right cases / total number of cases 100%

  one week

Secondary Outcomes (3)

• Accuracy of the prediction of disease based on whole electronic health record

  one week

• The prediction of disease based on whole electronic health record and criteria matching

  one week

• Time cost of EHR reading

  one week

Study Arms (2)

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.

Device: Artificial intelligence assistant system

No Intervention: without Artificial intelligence assistant system

NO INTERVENTION

Interventions

Artificial intelligence assistant system DEVICE

The AI-assisted diagnosis system can provide the direction of disease diagnosis in real time and assist the doctor to give the final diagnosis

Experimental: with Artificial intelligence assistant system

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Males or females who are over 18 years old;
• After qualified medical education and obtained the Certificate of medical practitioner;

You may not qualify if:

• 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.

Sponsors & Collaborators

• Renmin Hospital of Wuhan University: lead

Study Sites (1)

Renmin Hospital of Wuhan University

Wuhan, Hubei, 430060, China

Location

Study Officials

• Honggang Yu, MD

  Renmin Hospital of Wuhan University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Experimental: doctor with AI-assisted system;Control：without AI-assisted system

Study Record Dates

• First Submitted: August 9, 2022
• First Posted: August 11, 2022
• Study Start: August 15, 2022
• Primary Completion: September 1, 2022
• Study Completion: October 1, 2022
• Last Updated: November 8, 2022

Record last verified: 2022-08

Locations

CN (1)