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/NCT06025279</url>
    </required_header>
    <id_info>
      <org_study_id>SMARTALPHA-CURIOUS-1000</org_study_id>
      <nct_id>NCT06025279</nct_id>
    </id_info>
    <brief_title>A Data Collection Study for Artificial Intelligence-guided Musculoskeletal System Examination</brief_title>
    <official_title>Data Collection Study of Artificial Intelligence-Based Anatomy Identification Software (Curious) Designed for Ultrasound-guided Musculoskeletal Examination</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Smart Alfa Teknoloji San. ve Tic. A.S.</agency>
        <agency_class>Industry</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Ankara University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Smart Alfa Teknoloji San. ve Tic. A.S.</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The primary objective of this observational study is to acquire ultrasound images (raw data)&#xD;
        encompassing various planes within the musculoskeletal system. This data will be instrumental&#xD;
        in the development of artificial intelligence-guided software. The study aims to enlist 300&#xD;
        volunteers, comprising individuals with both healthy musculoskeletal systems and those&#xD;
        presenting pathologies. These participants will undergo ultrasound scans administered by two&#xD;
        experienced professionals, employing FDA-cleared ultrasound devices.&#xD;
  &#xD;
        The main question it aims to answer is:&#xD;
  &#xD;
        -Are the collected ultrasound images of diagnostic quality?&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Ultrasound's cost-effective and user-friendly attributes have positioned it as a cornerstone&#xD;
        in diagnosing musculoskeletal system disorders.&#xD;
  &#xD;
        In this single-centered and prospective study, the study aims to enlist 300 volunteers,&#xD;
        comprising both individuals with healthy musculoskeletal systems and those with pathologies.&#xD;
        The collected ultrasound raw data will be used to train models for the identification and&#xD;
        highlighting of key anatomical landmarks on ultrasound images. Participants' gender, age,&#xD;
        BMI, and medical history will be considered and reported. All scans will be performed on&#xD;
        FDA-cleared general-purpose ultrasound devices. Obtained images will be used to develop&#xD;
        artificial intelligence-based medical software by Smart Alfa Teknoloji San. Ve Tic. A.Ş.,&#xD;
        Ankara, Turkey. Smart Alfa has similarly conducted a study in the field of anesthesia using&#xD;
        the same method in Nerveblox artificial intelligence software.&#xD;
  &#xD;
        The study methodology encompasses the following components:&#xD;
  &#xD;
          -  Specific body views, guided by established protocols, will be scanned from both short&#xD;
             and long axes along different body planes. The focus areas encompass tendons, ligaments,&#xD;
             bones, and nerves within the shoulder, elbow, hand, hip, knee, and foot regions.&#xD;
  &#xD;
          -  A cohort of 300 volunteers, evenly distributed by gender (150 male, 150 female), will&#xD;
             have their demographic data (BMI, gender, age) documented.&#xD;
  &#xD;
          -  To counteract potential biases, the sequence of volunteer participation will be&#xD;
             randomized.&#xD;
  &#xD;
          -  Each scan is expected to take 45 minutes.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Active, not recruiting</overall_status>
    <start_date type="Actual">August 3, 2023</start_date>
    <completion_date type="Anticipated">November 29, 2023</completion_date>
    <primary_completion_date type="Anticipated">November 29, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Only</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Collecting ultrasound data for artificial intelligence software that highlighted structures</measure>
      <time_frame>4 months</time_frame>
      <description>The gathered images will serve the purpose of annotating anatomical landmarks, enabling the acquisition of diagnostically reliable images through artificial intelligence software. These annotated ultrasound images, validated by experts, will form the basis of a training dataset for the development of a machine learning algorithm.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Assessment of image quality</measure>
      <time_frame>4 months</time_frame>
      <description>The usability of the collected data in the artificial intelligence software will be verified.</description>
    </secondary_outcome>
    <enrollment type="Actual">300</enrollment>
    <condition>Ultrasound Imaging of Anatomical Structures</condition>
    <condition>Musculoskeletal Diseases</condition>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Ultrasound Scan</intervention_name>
      <description>Clinical professionals will conduct non-invasive ultrasound scans from the specified body views and subsequently save the acquired data.</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          A total of 300 participants, divided between genders (150 females and 150 males), will take&#xD;
          part in the study. These participants will include both healthy individuals and&#xD;
          pathological conditions related to specific views of the musculoskeletal system. Their&#xD;
          involvement will be based on informed consent. Pertinent data, such as medical history,&#xD;
          body mass index (BMI), age, and gender, will be reported.&#xD;
  &#xD;
          The logistical expenses of the volunteers during the study will be covered by the sponsor,&#xD;
          Smart Alfa Teknoloji San. ve Tic. A.Ş.&#xD;
  &#xD;
          The collection of raw ultrasound data will be undertaken by two experienced clinicians in&#xD;
          physical medicine and rehabilitation, and ultrasound scanning. Their expertise ensures&#xD;
          accurate data collection.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Volunteers over the age of 18&#xD;
  &#xD;
            -  Able to accept and sign the Informed Consent Form before participating in the study&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Volunteers below the age of 18&#xD;
  &#xD;
            -  Unwilling to accept or having psychiatric or neurological diseases to sign an Informed&#xD;
               Consent Form before participating in the study&#xD;
  &#xD;
            -  Inability to lie flat&#xD;
  &#xD;
            -  Anatomical deformity in the area to be scanned&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <location>
      <facility>
        <name>Ankara University School of Medicine</name>
        <address>
          <city>Altındağ</city>
          <state>Ankara</state>
          <zip>06230</zip>
          <country>Turkey</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Turkey</country>
    </location_countries>
    <reference>
      <citation>Gungor I, Gunaydin B, Buyukgebiz Yesil BM, Bagcaz S, Ozdemir MG, Inan G, Oktar SO. Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures. Ann Anat. 2023 Aug 11;250:152143. doi: 10.1016/j.aanat.2023.152143. Online ahead of print.</citation>
      <PMID>37572764</PMID>
    </reference>
    <reference>
      <citation>Ozcakar L, Tok F, Ricci V, Mezian K, Wu CH, Wu WT, Park GY, Kwon DR, Prieto MG, Dughbaj M, Dogan Y, Aksoz B, Guvener O, Ekiz T, Tiras M, Karacoban L, Menderes Y, Ciftci E, Ilicepinar OF, Kaya U, Kara M, Chang KV. Artificial Intelligence Featuring EURO-MUSCULUS/USPRM Basic Scanning Protocols. Am J Phys Med Rehabil. 2022 Nov 1;101(11):e174-e175. doi: 10.1097/PHM.0000000000002070. Epub 2022 Jul 7. No abstract available.</citation>
      <PMID>35802706</PMID>
    </reference>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 29, 2023</study_first_submitted>
    <study_first_submitted_qc>August 29, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 6, 2023</study_first_posted>
    <last_update_submitted>September 8, 2023</last_update_submitted>
    <last_update_submitted_qc>September 8, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 11, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Ultrasound Imaging</keyword>
    <keyword>Musculoskeletal System</keyword>
    <keyword>Data Collection</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Musculoskeletal Diseases</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </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/NCT06025279
  org study id: SMARTALPHA-CURIOUS-1000
  nct id: NCT06025279
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The primary objective of this observational study is to acquire ultrasound images (raw data)
        encompassing various planes within the musculoskeletal system. This data will be instrumental
        in the development of artificial intelligence-guided software. The study aims to enlist 300
        volunteers, comprising individuals with both healthy musculoskeletal systems and those
        presenting pathologies. These participants will undergo ultrasound scans administered by two
        experienced professionals, employing FDA-cleared ultrasound devices.
  
        The main question it aims to answer is:
  
        -Are the collected ultrasound images of diagnostic quality?
  Ultrasound's cost-effective and user-friendly attributes have positioned it as a cornerstone
        in diagnosing musculoskeletal system disorders.
  
        In this single-centered and prospective study, the study aims to enlist 300 volunteers,
        comprising both individuals with healthy musculoskeletal systems and those with pathologies.
        The collected ultrasound raw data will be used to train models for the identification and
        highlighting of key anatomical landmarks on ultrasound images. Participants' gender, age,
        BMI, and medical history will be considered and reported. All scans will be performed on
        FDA-cleared general-purpose ultrasound devices. Obtained images will be used to develop
        artificial intelligence-based medical software by Smart Alfa Teknoloji San. Ve Tic. A.Ş.,
        Ankara, Turkey. Smart Alfa has similarly conducted a study in the field of anesthesia using
        the same method in Nerveblox artificial intelligence software.
  
        The study methodology encompasses the following components:
  
          -  Specific body views, guided by established protocols, will be scanned from both short
             and long axes along different body planes. The focus areas encompass tendons, ligaments,
             bones, and nerves within the shoulder, elbow, hand, hip, knee, and foot regions.
  
          -  A cohort of 300 volunteers, evenly distributed by gender (150 male, 150 female), will
             have their demographic data (BMI, gender, age) documented.
  
          -  To counteract potential biases, the sequence of volunteer participation will be
             randomized.
  
          -  Each scan is expected to take 45 minutes.
  observational model: Case-Only
  time perspective: Prospective
  measure: Collecting ultrasound data for artificial intelligence software that highlighted structures
  time frame: 4 months
  description: The gathered images will serve the purpose of annotating anatomical landmarks, enabling the acquisition of diagnostically reliable images through artificial intelligence software. These annotated ultrasound images, validated by experts, will form the basis of a training dataset for the development of a machine learning algorithm.
  measure: Assessment of image quality
  time frame: 4 months
  description: The usability of the collected data in the artificial intelligence software will be verified.
  intervention type: Other
  intervention name: Ultrasound Scan
  description: Clinical professionals will conduct non-invasive ultrasound scans from the specified body views and subsequently save the acquired data.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  facility: 
  country: Turkey
  citation: Gungor I, Gunaydin B, Buyukgebiz Yesil BM, Bagcaz S, Ozdemir MG, Inan G, Oktar SO. Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures. Ann Anat. 2023 Aug 11;250:152143. doi: 10.1016/j.aanat.2023.152143. Online ahead of print.
  PMID: 37572764
  citation: Ozcakar L, Tok F, Ricci V, Mezian K, Wu CH, Wu WT, Park GY, Kwon DR, Prieto MG, Dughbaj M, Dogan Y, Aksoz B, Guvener O, Ekiz T, Tiras M, Karacoban L, Menderes Y, Ciftci E, Ilicepinar OF, Kaya U, Kara M, Chang KV. Artificial Intelligence Featuring EURO-MUSCULUS/USPRM Basic Scanning Protocols. Am J Phys Med Rehabil. 2022 Nov 1;101(11):e174-e175. doi: 10.1097/PHM.0000000000002070. Epub 2022 Jul 7. No abstract available.
  PMID: 35802706
  responsible party type: Sponsor
  mesh term: Musculoskeletal Diseases
  sharing ipd: No