xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05974163</url>
    </required_header>
    <id_info>
      <org_study_id>SYSKY-2023-375-01</org_study_id>
      <nct_id>NCT05974163</nct_id>
    </id_info>
    <brief_title>Development of an AI-based Emergency Imaging Multi-Disease Rapid Joint Screening System</brief_title>
    <acronym>Al-MDS</acronym>
    <official_title>Development of a Multi-Disease Screening System for Emergency CT Imaging Based on Artificial Intelligence</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        Introduction:&#xD;
  &#xD;
        Early and rapid diagnosis of etiology is often an important part of saving the lives of&#xD;
        patients in emergency department. Chest CT is an important examination method for emergency&#xD;
        diagnosis because of its fast examination speed and accurate localization. Traditional&#xD;
        medical imaging diagnosis relies on radiologists to report in a qualitative and subjective&#xD;
        manner. Through the interdisciplinary combination of clinical, imaging and artificial&#xD;
        intelligence, the integration of multi-omics data, the construction of large-scale language&#xD;
        models, and the construction of the auxiliary diagnosis support system of &quot;one check for&#xD;
        multiple diseases&quot; provide new ideas and means for the rapid and accurate screening of&#xD;
        emergency critical diseases.&#xD;
  &#xD;
        Method：&#xD;
  &#xD;
        Study design Investigators retrospectively collected cardiovascular, respiratory, digestive,&#xD;
        and neurological CT images, demographic data, medical history and laboratory date of&#xD;
        emergency department patients during the period from 1 January 2018 and 30 December 2024.&#xD;
        Regularly carry out standardized follow-up work, and complete the collection and database&#xD;
        establishment of clinical-imaging multi-omics data of patients attending emergency&#xD;
        department.The inclusion criteria are:1. adult emergency patients with cardiovascular,&#xD;
        respiratory, digestive, and nervous system diseases; 2. These patients had CT images.&#xD;
        Patients with incomplete clinical or radiographic data were excluded from the analysis.&#xD;
        Regularly carry out standardized follow-up work, and complete the collection and database&#xD;
        establishment of clinical-imaging multi-omics data of patients attending emergency&#xD;
        department.&#xD;
  &#xD;
        Based on the collected medical text data, an artificial intelligence large-scale language&#xD;
        model algorithm framework is built. After the structure annotation of chest CT images is&#xD;
        performed by doctors above the intermediate level of imaging, the Transformer deep neural&#xD;
        network is trained for CT image segmentation, and a series of tasks such as structural&#xD;
        structure segmentation, damage detection, disease classification and automatic report&#xD;
        generation are developed based on Vision Transformer self-attention architecture mechanism. A&#xD;
        multi-disease diagnosis and treatment decision-making system based on chest CT images,&#xD;
        clinical text and examination multimodal data was constructed and validated.&#xD;
  &#xD;
        Disscusion&#xD;
  &#xD;
        Emergency medicine deals mainly with unpredictable critical and sudden illnesses. Patients&#xD;
        who come to the emergency department for medical treatment often have acute onset, hidden&#xD;
        condition, rapid progress, many complications, high mortality and disability rate. Assisted&#xD;
        diagnosis systems developed by combining clinical text, images and artificial intelligence&#xD;
        can greatly improve the ability of emergency department doctors to accurately diagnose&#xD;
        diseases. This study fills the blank of CT artificial intelligence aided diagnosis system for&#xD;
        emergency patients, and provides a rapid diagnosis scheme for multi-system and multi-disease.&#xD;
        Finally, the results will be transformed into clinical application software and used and&#xD;
        promoted in clinical work to improve the diagnosis and treatment level.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">August 1, 2023</start_date>
    <completion_date type="Anticipated">July 31, 2025</completion_date>
    <primary_completion_date type="Anticipated">July 31, 2024</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Accuracy of disease diagnosis</measure>
      <time_frame>2025-08-01~2025-12-31</time_frame>
      <description>Construct a rapid diagnosis, accurate and efficient emergency CT image multi-disease rapid joint screening system</description>
    </primary_outcome>
    <number_of_groups>3</number_of_groups>
    <enrollment type="Anticipated">10000</enrollment>
    <condition>Emergency Medical Services</condition>
    <condition>Critical Illness</condition>
    <condition>Emergency Service, Hospital</condition>
    <condition>Machine Learning</condition>
    <condition>Diagnosis</condition>
    <arm_group>
      <arm_group_label>Model reconstruction cohort</arm_group_label>
      <description>8000 patients were recruited retrospectively from January 2023 to December 2025 as discovering group.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>External Validation cohort 1</arm_group_label>
      <description>1000 patients were recruited retrospectively from January 2023 to December 2025 as internal validation group.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>External validation cohort 2</arm_group_label>
      <description>1000 patients will be recruited prospectively during the period from January 2023 to December 2025 as external validation group</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>radiomic of CT</intervention_name>
      <description>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.</description>
      <arm_group_label>External Validation cohort 1</arm_group_label>
      <arm_group_label>External validation cohort 2</arm_group_label>
      <arm_group_label>Model reconstruction cohort</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          We plan to recruit 1000 patients in discovering group, 8000 patients in internal&#xD;
          validation, and 2000 patients in external validation group. Patients between 18 and 100&#xD;
          years of age with cardiovascular, respiratory, digestive, and neurological disorders. CT&#xD;
          imaging was available.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
          Adults with cardiovascular, respiratory, digestive, and neurological disorders. CT imaging&#xD;
          was available.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          Patients with incomplete clinical or radiographic data were excluded.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>100 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>LI LI</last_name>
      <phone>02034071029</phone>
      <email>lil3@mail.sysu.edu.cn</email>
    </overall_contact>
    <location>
      <facility>
        <name>Sun Yat-sen Memorial Hospital, Sun Yat-sen University</name>
        <address>
          <city>Guangzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>LI LI, M.D.</last_name>
        <phone>02034071029</phone>
        <email>lil3@mail.sysu.edu.cn</email>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>July 2023</verification_date>
    <study_first_submitted>July 26, 2023</study_first_submitted>
    <study_first_submitted_qc>July 26, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 3, 2023</study_first_posted>
    <last_update_submitted>July 26, 2023</last_update_submitted>
    <last_update_submitted_qc>July 26, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 3, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</investigator_affiliation>
      <investigator_full_name>Li Li</investigator_full_name>
      <investigator_title>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Emergencies</mesh_term>
      <mesh_term>Critical Illness</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
      <ipd_description>individual participant data in this research can contact lil3 @mail.sysu.edu.cn for reasonable requests</ipd_description>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05974163
  org study id: SYSKY-2023-375-01
  nct id: NCT05974163
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  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.
  observational model: Cohort
  time perspective: Prospective
  measure: Accuracy of disease diagnosis
  time frame: 2025-08-01~2025-12-31
  description: Construct a rapid diagnosis, accurate and efficient emergency CT image multi-disease rapid joint screening system
  arm group label: Model reconstruction cohort
  description: 8000 patients were recruited retrospectively from January 2023 to December 2025 as discovering group.
  arm group label: External Validation cohort 1
  description: 1000 patients were recruited retrospectively from January 2023 to December 2025 as internal validation group.
  arm group label: External validation cohort 2
  description: 1000 patients will be recruited prospectively during the period from January 2023 to December 2025 as external validation group
  intervention type: Diagnostic Test
  intervention name: radiomic of CT
  description: 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.
  arm group label: External Validation cohort 1
  arm group label: External validation cohort 2
  arm group label: Model reconstruction cohort
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 100 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: LI LI
  phone: 02034071029
  email: lil3@mail.sysu.edu.cn
  facility: 
  contact: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
  investigator full name: Li Li
  investigator title: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
  mesh term: Emergencies
  mesh term: Critical Illness
  sharing ipd: Undecided
  ipd description: individual participant data in this research can contact lil3 @mail.sysu.edu.cn for reasonable requests