Development of an AI-based Emergency Imaging Multi-Disease Rapid Joint Screening System

NCT05974163 | Unknown

• 1 other identifier: SYSKY-2023-375-01
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

Introduction: Early and rapid diagnosis of etiology is often an important part of saving the lives of patients in emergency department. Chest CT is an important examination method for emergency diagnosis because of its fast examination speed and accurate localization. Traditional medical imaging diagnosis relies on radiologists to report in a qualitative and subjective manner. Through the interdisciplinary combination of clinical, imaging and artificial intelligence, the integration of multi-omics data, the construction of large-scale language models, and the construction of the auxiliary diagnosis support system of "one check for multiple diseases" provide new ideas and means for the rapid and accurate screening of emergency critical diseases. Method： Study design Investigators retrospectively collected cardiovascular, respiratory, digestive, and neurological CT images, demographic data, medical history and laboratory date of emergency department patients during the period from 1 January 2018 and 30 December 2024. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department.The inclusion criteria are:1. adult emergency patients with cardiovascular, respiratory, digestive, and nervous system diseases; 2. These patients had CT images. Patients with incomplete clinical or radiographic data were excluded from the analysis. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department. Based on the collected medical text data, an artificial intelligence large-scale language model algorithm framework is built. After the structure annotation of chest CT images is performed by doctors above the intermediate level of imaging, the Transformer deep neural network is trained for CT image segmentation, and a series of tasks such as structural structure segmentation, damage detection, disease classification and automatic report generation are developed based on Vision Transformer self-attention architecture mechanism. A multi-disease diagnosis and treatment decision-making system based on chest CT images, clinical text and examination multimodal data was constructed and validated. Disscusion Emergency medicine deals mainly with unpredictable critical and sudden illnesses. Patients who come to the emergency department for medical treatment often have acute onset, hidden condition, rapid progress, many complications, high mortality and disability rate. Assisted diagnosis systems developed by combining clinical text, images and artificial intelligence can greatly improve the ability of emergency department doctors to accurately diagnose diseases. This study fills the blank of CT artificial intelligence aided diagnosis system for emergency patients, and provides a rapid diagnosis scheme for multi-system and multi-disease. Finally, the results will be transformed into clinical application software and used and promoted in clinical work to improve the diagnosis and treatment level.

Conditions

Emergency Medical Services; Critical Illness; Emergency Service, Hospital; Machine Learning; Diagnosis

Outcome Measures

Primary Outcomes (1)

• Accuracy of disease diagnosis

  Construct a rapid diagnosis, accurate and efficient emergency CT image multi-disease rapid joint screening system

  2025-08-01~2025-12-31

Study Arms (3)

Model reconstruction cohort

8000 patients were recruited retrospectively from January 2023 to December 2025 as discovering group.

Diagnostic Test: radiomic of CT

External Validation cohort 1

1000 patients were recruited retrospectively from January 2023 to December 2025 as internal validation group.

Diagnostic Test: radiomic of CT

External validation cohort 2

1000 patients will be recruited prospectively during the period from January 2023 to December 2025 as external validation group

Diagnostic Test: radiomic of CT

Interventions

radiomic of CT DIAGNOSTIC_TEST

Computed Tomography (CT) is often an important examination method for emergency diagnosis because of its fast examination speed and accurate localization acute respiratory distress syndrome.

External Validation cohort 1; External validation cohort 2; Model reconstruction cohort

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

We plan to recruit 1000 patients in discovering group, 8000 patients in internal validation, and 2000 patients in external validation group. Patients between 18 and 100 years of age with cardiovascular, respiratory, digestive, and neurological disorders. CT imaging was available.

You may qualify if:

• Adults with cardiovascular, respiratory, digestive, and neurological disorders. CT imaging was available.

You may not qualify if:

• Patients with incomplete clinical or radiographic data were excluded.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (1)

Sun Yat-sen Memorial Hospital, Sun Yat-sen University

Guangzhou, China

Location

MeSH Terms

Conditions

Critical Illness; Emergencies; Disease

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

LI LI

CONTACT

02034071029; lil3@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Study Record Dates

• First Submitted: July 26, 2023
• First Posted: August 3, 2023
• Study Start: August 1, 2023
• Primary Completion: July 31, 2024
• Study Completion: July 31, 2025
• Last Updated: August 3, 2023

Record last verified: 2023-07

Locations

CN (1)