Development of an AI-based Emergency Imaging Multi-Disease Rapid Joint Screening System (Al-MDS)

Development of a Multi-Disease Screening System for Emergency CT Imaging Based on Artificial Intelligence

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Critical Illness; Emergency Medical Services; Machine Learning; Diagnosis; Emergency Service, Hospital
• Intervention / Treatment: Diagnostic test: radiomic of CT

• Study Type: Observational
• Enrollment (Estimated): 10000

Contacts and Locations

• Study Contact: Name: LI LI; Phone Number: 02034071029; Email: lil3@mail.sysu.edu.cn

Study Locations

• China
  • Guangzhou, China
    • Sun Yat-sen Memorial Hospital, Sun Yat-sen University
    • Contact: LI LI, M.D.; Phone Number: 02034071029; Email: lil3@mail.sysu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

Patients with incomplete clinical or radiographic data were excluded.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Model reconstruction cohort; 8000 patients were recruited retrospectively from January 2023 to December 2025 as discovering group.	Diagnostic test: radiomic of CT; 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; 1000 patients were recruited retrospectively from January 2023 to December 2025 as internal validation group.	Diagnostic test: radiomic of CT; 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 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

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

Study record dates

Study Major Dates

• Study Start (Estimated): August 1, 2023
• Primary Completion (Estimated): July 31, 2024
• Study Completion (Estimated): July 31, 2025

Study Registration Dates

• First Submitted: July 26, 2023
• First Submitted That Met QC Criteria: July 26, 2023
• First Posted (Actual): August 3, 2023

Study Record Updates

• Last Update Posted (Actual): August 3, 2023
• Last Update Submitted That Met QC Criteria: July 26, 2023
• Last Verified: July 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Emergencies; Critical Illness
• Other Study ID Numbers: SYSKY-2023-375-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: individual participant data in this research can contact lil3 @mail.sysu.edu.cn for reasonable requests

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No