<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/NCT04442425</url>
  </required_header>
  <id_info>
    <org_study_id>200130</org_study_id>
    <secondary_id>20-C-0130</secondary_id>
    <nct_id>NCT04442425</nct_id>
  </id_info>
  <brief_title>Machine Learning to Analyze Facial Imaging, Voice and Spoken Language for the Capture and Classification of Cancer/Tumor Pain</brief_title>
  <official_title>A Feasibility Study Investigating the Use of Machine Learning to Analyze Facial Imaging, Voice and Spoken Language for the Capture and Classification of Cancer/Tumor Pain</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National Cancer Institute (NCI)</agency>
      <agency_class>NIH</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>National Institutes of Health Clinical Center (CC)</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>
      Background:&#xD;
&#xD;
      Cancer pain can have a very negative effect on people s daily lives. Researchers want to use&#xD;
      machine learning to detect facial expressions and voice signals. They want to help people&#xD;
      with cancer by creating a model to measure pain. They want the model to reflect diverse faces&#xD;
      and facial expressions.&#xD;
&#xD;
      Objective:&#xD;
&#xD;
      To find out whether facial recognition technology can be used to classify pain in a diverse&#xD;
      set of people with cancer. Also, to find out whether voice recognition technology can be used&#xD;
      to assess pain.&#xD;
&#xD;
      Eligibility:&#xD;
&#xD;
      People ages 12 and older who are undergoing treatment for cancer&#xD;
&#xD;
      Design:&#xD;
&#xD;
      Participants will be screened with:&#xD;
&#xD;
      Cancer history&#xD;
&#xD;
      Information about their gender and skin type&#xD;
&#xD;
      Information about their access to a smart phone and wireless internet&#xD;
&#xD;
      Questions about their cancer pain&#xD;
&#xD;
      Participants will have check-ins at the clinic and at home. These will occur over about 3&#xD;
      months. They will have 2-4 check-ins at the clinic. They will check in at home about 3 times&#xD;
      per week.&#xD;
&#xD;
      During check-ins, participants will answer questions and talk about their cancer pain. They&#xD;
      will use a mobile phone or a computer with a camera and microphone to complete a&#xD;
      questionnaire. They will record a video of themselves reading a 15-second passage of text and&#xD;
      responding to a question.&#xD;
&#xD;
      During the clinic check-ins, professional lighting, video equipment, and cameras will be used&#xD;
      for the recordings.&#xD;
&#xD;
      During remote check-ins, participants will be asked to complete the questionnaire and&#xD;
      recordings alone. They should be in a quiet and bright room. The room should have a white&#xD;
      wall or background.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Background:&#xD;
&#xD;
        -  Pain related to cancer/tumors can be widespread, wield debilitating effects on daily&#xD;
           life, and interfere with otherwise positive outcomes from targeted treatment.&#xD;
&#xD;
        -  The underpinnings of this study are chiefly motivated by the need to develop and&#xD;
           validate objective methods for measuring pain using a model that is relevant in breadth&#xD;
           and depth to a diversity of patient populations.&#xD;
&#xD;
        -  Inadequate assessment and management of cancer/tumor pain can lead to functional and&#xD;
           psychological deterioration and negatively impact quality of life.&#xD;
&#xD;
        -  Research of objective measurement scales of pain based on automated detection of facial&#xD;
           expression using machine learning is expanding but has been limited to certain&#xD;
           demographic cohorts.&#xD;
&#xD;
        -  Machine learning models demonstrate poor performance when training sets lack adequate&#xD;
           diversity of training data, including visibly different faces and facial expressions,&#xD;
           which yields opportunity in the proposed study to lay a guiding foundation by&#xD;
           constructing a more general and generalizable model based on faces of varying sex and&#xD;
           skin phototypes.&#xD;
&#xD;
      Objectives:&#xD;
&#xD;
      -The primary objective of this study is to determine the feasibility of using facial&#xD;
      recognition technology to classify cancer/tumor related pain in a demographically diverse set&#xD;
      of participants with cancer/tumors who are receiving standard of care or investigational&#xD;
      treatment for their cancer/tumor.&#xD;
&#xD;
      Eligibility:&#xD;
&#xD;
        -  Adults and children (12 years of age or older) with a diagnosis of a cancer or tumor who&#xD;
           are receiving standard of care or investigational treatment for their underlying&#xD;
           cancer/tumor.&#xD;
&#xD;
        -  Participant must have access to internet connected smart phone or computer with camera&#xD;
           and microphone and must be willing to pay any charges from service provider/carrier&#xD;
           associated with the use of the device.&#xD;
&#xD;
      Design:&#xD;
&#xD;
        -  The design is a single institution, observational, non-intervention clinical study at&#xD;
           the National Institutes of Health Clinical Center.&#xD;
&#xD;
        -  All participants will participate in the same activities in two different settings&#xD;
           (remotely and in-clinic) for a three-month period.&#xD;
&#xD;
        -  At home, participants will utilize a mobile application for self-reporting of pain and&#xD;
           will audio- visually record themselves reading a passage of text and describing how they&#xD;
           feel. In the clinic, participants will perform the same activities with optimal lighting&#xD;
           and videography, along with infrared video capture.&#xD;
&#xD;
        -  Visual (RGB) and infrared facial images, audio signal, self-reported pain and natural&#xD;
           language verbalizations of participant feelings feel will be captured. Audio signal and&#xD;
           video data will be annotated with self-reported pain and clinical data to create a&#xD;
           supervised machine learning model that will learn to automatically detect pain.&#xD;
&#xD;
        -  Care will be taken with the study sample to include a diversity of genders and skin&#xD;
           types (a proxy for racial diversity) to establish a broad applicability of the model in&#xD;
           the clinical setting. Additionally, video recordings of participant natural language to&#xD;
           describe their pain and how they feel will be transcribed and auto-processed against the&#xD;
           Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events&#xD;
           (PROCTCAE) library to explore the presence and progression of self-reporting of adverse&#xD;
           events.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Enrolling by invitation</overall_status>
  <start_date type="Actual">October 27, 2020</start_date>
  <completion_date type="Anticipated">January 31, 2024</completion_date>
  <primary_completion_date type="Anticipated">January 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>Feasibility of using facial recognition technology to classify pain</measure>
    <time_frame>3 months</time_frame>
    <description>The primary objective of this study is to determine the feasibility of using facial recognition technology to classify pain in a demographically diverse set of patients with cancer/tumor who are participating on a clinical trial.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>To determine the feasibility of using voice recognition technology</measure>
    <time_frame>3 months</time_frame>
    <description>Voice recognition technology</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>To transcribe patient video responses to assess pain using free-text</measure>
    <time_frame>3 months</time_frame>
    <description>Video responses to assess pain using free-text</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>To determine the feasibility of combining RGB and thermal images with voice recognition transcribed verbal responses</measure>
    <time_frame>3 months</time_frame>
    <description>RGB and thermal images</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>To use natural language processing algorithms to assess pain</measure>
    <time_frame>3 months</time_frame>
    <description>Natural language processing algorithms to assess pain</description>
  </secondary_outcome>
  <number_of_groups>16</number_of_groups>
  <enrollment type="Anticipated">120</enrollment>
  <condition>Cancer</condition>
  <condition>Neoplasms</condition>
  <condition>Solid Tumors</condition>
  <arm_group>
    <arm_group_label>1DF/NoPain_IV-VI_Female</arm_group_label>
    <description>Worst pain in past month = 0; Skin Type IV-VI, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>1DM/NoPain_IV-VI_Male</arm_group_label>
    <description>Worst pain in past month = 0; Skin Type IV-VI, Male</description>
  </arm_group>
  <arm_group>
    <arm_group_label>1LF/NoPain_I-III_Female</arm_group_label>
    <description>Worst pain in past month = 0; Skin Type I-III, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>1LM/NoPain_I-III_Male</arm_group_label>
    <description>Worst pain in past month = 0; Skin Type I-III, Male</description>
  </arm_group>
  <arm_group>
    <arm_group_label>2DF/MildPain_IV-VI_Female</arm_group_label>
    <description>Worst pain in past month = 1-3; Skin Type IV-VI, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>2DM/MildPain_IV-VI_Male</arm_group_label>
    <description>Worst pain in past month = 1-3; Skin Type IV-VI, Male</description>
  </arm_group>
  <arm_group>
    <arm_group_label>2LF/MildPain_I-III_Female</arm_group_label>
    <description>Worst pain in past month = 1-3; Skin Type I-III, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>2LM/MildPain_I-III_Male</arm_group_label>
    <description>Worst pain in past month = 1-3; Skin Type I-III, Male</description>
  </arm_group>
  <arm_group>
    <arm_group_label>3DF/ModPain_IV-VI_Female</arm_group_label>
    <description>Worst pain in past month = 4-6; Skin Type IV-VI, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>3DM/ModPain_IV-VI_Male</arm_group_label>
    <description>Worst pain in past month = 4-6; Skin Type IV-VI, Male</description>
  </arm_group>
  <arm_group>
    <arm_group_label>3LF/ModPain_I-III_Female</arm_group_label>
    <description>Worst pain in past month = 4-6; Skin Type I-III, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>3LM/ModPain_I-III_Male</arm_group_label>
    <description>Worst pain in past month = 4-6; Skin Type I-III, Male</description>
  </arm_group>
  <arm_group>
    <arm_group_label>4DF/SeverePain_IV-VI_Female</arm_group_label>
    <description>Worst pain in past month = 7-10; Skin Type IV-VI, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>4DM/SeverePain_IV-VI_Male</arm_group_label>
    <description>Worst pain in past month = 7-10; Skin Type IV-VI, Male</description>
  </arm_group>
  <arm_group>
    <arm_group_label>4LF/SeverePain_I-III_Female</arm_group_label>
    <description>Worst pain in past month = 7-10; Skin Type I-III, Female</description>
  </arm_group>
  <arm_group>
    <arm_group_label>4LM/SeverePain_I-III_Male</arm_group_label>
    <description>Worst pain in past month = 7-10; Skin Type I-III, Male</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        Patients with histologically or cytologically proven cancer or tumor&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        -  INCLUSION CRITERIA:&#xD;
&#xD;
               1. Ability of subject to understand and willingness to sign a written informed&#xD;
                  consent document.&#xD;
&#xD;
               2. Adults and children (including NIH staff) aged &gt;= 12 years.&#xD;
&#xD;
               3. Participants with diagnosis of a cancer or tumor&#xD;
&#xD;
               4. Participant must be receiving either standard of care or investigational&#xD;
                  cancer/tumor treatment either at NIH or with a community physician.&#xD;
&#xD;
               5. Must have access to a smart phone (iPhone or Android) with either a data plan&#xD;
                  and/or access to wireless internet (wifi) or a computer with a camera and&#xD;
                  microphone and access to internet and must willing to use their device and assume&#xD;
                  any associated charges from&#xD;
&#xD;
        service providers.&#xD;
&#xD;
        EXCLUSION CRITERIA:&#xD;
&#xD;
          1. Participants with progressive brain tumors or metastasis. Participants with treated&#xD;
             brain metastasis or primary brain tumor are eligible if there is no evidence of&#xD;
             progression for at least 4 weeks after CNS directed treatment and there is no impact&#xD;
             on voice or facial muscle movements.&#xD;
&#xD;
          2. Participants with Parkinson s disease.&#xD;
&#xD;
          3. Known current alcohol or drug abuse.&#xD;
&#xD;
          4. Any psychiatric condition that would prohibit the understanding or rendering of&#xD;
             informed consent.&#xD;
&#xD;
          5. Non-English speaking subjects.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>12 Years</minimum_age>
    <maximum_age>120 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>James L Gulley, M.D.</last_name>
    <role>Principal Investigator</role>
    <affiliation>National Cancer Institute (NCI)</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>National Institutes of Health Clinical Center</name>
      <address>
        <city>Bethesda</city>
        <state>Maryland</state>
        <zip>20892</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <link>
    <url>https://clinicalstudies.info.nih.gov/cgi/detail.cgi?A_2020-C-0130.html</url>
    <description>NIH Clinical Center Detailed Web Page</description>
  </link>
  <verification_date>September 7, 2023</verification_date>
  <study_first_submitted>June 19, 2020</study_first_submitted>
  <study_first_submitted_qc>June 19, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 22, 2020</study_first_posted>
  <last_update_submitted>September 9, 2023</last_update_submitted>
  <last_update_submitted_qc>September 9, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">September 13, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Telehealth</keyword>
  <keyword>Self-Reported Pain</keyword>
  <keyword>Facial Recognition Technology</keyword>
  <keyword>Pain Score</keyword>
  <keyword>Natural History</keyword>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>.All IPD recorded in the medical record will be shared with intramural investigators upon request.</ipd_description>
    <ipd_info_type>Study Protocol</ipd_info_type>
    <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
    <ipd_info_type>Informed Consent Form (ICF)</ipd_info_type>
    <ipd_time_frame>Clinical data available during the study and indefinitely.</ipd_time_frame>
    <ipd_access_criteria>Clinical data will be made available via subscription to BTRIS and with the permission of the study PI.</ipd_access_criteria>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


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/NCT04442425
org study id: 200130
secondary id: 20-C-0130
nct id: NCT04442425
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
Background:

      Cancer pain can have a very negative effect on people s daily lives. Researchers want to use
      machine learning to detect facial expressions and voice signals. They want to help people
      with cancer by creating a model to measure pain. They want the model to reflect diverse faces
      and facial expressions.

      Objective:

      To find out whether facial recognition technology can be used to classify pain in a diverse
      set of people with cancer. Also, to find out whether voice recognition technology can be used
      to assess pain.

      Eligibility:

      People ages 12 and older who are undergoing treatment for cancer

      Design:

      Participants will be screened with:

      Cancer history

      Information about their gender and skin type

      Information about their access to a smart phone and wireless internet

      Questions about their cancer pain

      Participants will have check-ins at the clinic and at home. These will occur over about 3
      months. They will have 2-4 check-ins at the clinic. They will check in at home about 3 times
      per week.

      During check-ins, participants will answer questions and talk about their cancer pain. They
      will use a mobile phone or a computer with a camera and microphone to complete a
      questionnaire. They will record a video of themselves reading a 15-second passage of text and
      responding to a question.

      During the clinic check-ins, professional lighting, video equipment, and cameras will be used
      for the recordings.

      During remote check-ins, participants will be asked to complete the questionnaire and
      recordings alone. They should be in a quiet and bright room. The room should have a white
      wall or background.
Background:

        -  Pain related to cancer/tumors can be widespread, wield debilitating effects on daily
           life, and interfere with otherwise positive outcomes from targeted treatment.

        -  The underpinnings of this study are chiefly motivated by the need to develop and
           validate objective methods for measuring pain using a model that is relevant in breadth
           and depth to a diversity of patient populations.

        -  Inadequate assessment and management of cancer/tumor pain can lead to functional and
           psychological deterioration and negatively impact quality of life.

        -  Research of objective measurement scales of pain based on automated detection of facial
           expression using machine learning is expanding but has been limited to certain
           demographic cohorts.

        -  Machine learning models demonstrate poor performance when training sets lack adequate
           diversity of training data, including visibly different faces and facial expressions,
           which yields opportunity in the proposed study to lay a guiding foundation by
           constructing a more general and generalizable model based on faces of varying sex and
           skin phototypes.

      Objectives:

      -The primary objective of this study is to determine the feasibility of using facial
      recognition technology to classify cancer/tumor related pain in a demographically diverse set
      of participants with cancer/tumors who are receiving standard of care or investigational
      treatment for their cancer/tumor.

      Eligibility:

        -  Adults and children (12 years of age or older) with a diagnosis of a cancer or tumor who
           are receiving standard of care or investigational treatment for their underlying
           cancer/tumor.

        -  Participant must have access to internet connected smart phone or computer with camera
           and microphone and must be willing to pay any charges from service provider/carrier
           associated with the use of the device.

      Design:

        -  The design is a single institution, observational, non-intervention clinical study at
           the National Institutes of Health Clinical Center.

        -  All participants will participate in the same activities in two different settings
           (remotely and in-clinic) for a three-month period.

        -  At home, participants will utilize a mobile application for self-reporting of pain and
           will audio- visually record themselves reading a passage of text and describing how they
           feel. In the clinic, participants will perform the same activities with optimal lighting
           and videography, along with infrared video capture.

        -  Visual (RGB) and infrared facial images, audio signal, self-reported pain and natural
           language verbalizations of participant feelings feel will be captured. Audio signal and
           video data will be annotated with self-reported pain and clinical data to create a
           supervised machine learning model that will learn to automatically detect pain.

        -  Care will be taken with the study sample to include a diversity of genders and skin
           types (a proxy for racial diversity) to establish a broad applicability of the model in
           the clinical setting. Additionally, video recordings of participant natural language to
           describe their pain and how they feel will be transcribed and auto-processed against the
           Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events
           (PROCTCAE) library to explore the presence and progression of self-reporting of adverse
           events.
observational model: Cohort
time perspective: Prospective
measure: Feasibility of using facial recognition technology to classify pain
time frame: 3 months
description: The primary objective of this study is to determine the feasibility of using facial recognition technology to classify pain in a demographically diverse set of patients with cancer/tumor who are participating on a clinical trial.
measure: To determine the feasibility of using voice recognition technology
time frame: 3 months
description: Voice recognition technology
measure: To transcribe patient video responses to assess pain using free-text
time frame: 3 months
description: Video responses to assess pain using free-text
measure: To determine the feasibility of combining RGB and thermal images with voice recognition transcribed verbal responses
time frame: 3 months
description: RGB and thermal images
measure: To use natural language processing algorithms to assess pain
time frame: 3 months
description: Natural language processing algorithms to assess pain
arm group label: 1DF/NoPain_IV-VI_Female
description: Worst pain in past month = 0; Skin Type IV-VI, Female
arm group label: 1DM/NoPain_IV-VI_Male
description: Worst pain in past month = 0; Skin Type IV-VI, Male
arm group label: 1LF/NoPain_I-III_Female
description: Worst pain in past month = 0; Skin Type I-III, Female
arm group label: 1LM/NoPain_I-III_Male
description: Worst pain in past month = 0; Skin Type I-III, Male
arm group label: 2DF/MildPain_IV-VI_Female
description: Worst pain in past month = 1-3; Skin Type IV-VI, Female
arm group label: 2DM/MildPain_IV-VI_Male
description: Worst pain in past month = 1-3; Skin Type IV-VI, Male
arm group label: 2LF/MildPain_I-III_Female
description: Worst pain in past month = 1-3; Skin Type I-III, Female
arm group label: 2LM/MildPain_I-III_Male
description: Worst pain in past month = 1-3; Skin Type I-III, Male
arm group label: 3DF/ModPain_IV-VI_Female
description: Worst pain in past month = 4-6; Skin Type IV-VI, Female
arm group label: 3DM/ModPain_IV-VI_Male
description: Worst pain in past month = 4-6; Skin Type IV-VI, Male
arm group label: 3LF/ModPain_I-III_Female
description: Worst pain in past month = 4-6; Skin Type I-III, Female
arm group label: 3LM/ModPain_I-III_Male
description: Worst pain in past month = 4-6; Skin Type I-III, Male
arm group label: 4DF/SeverePain_IV-VI_Female
description: Worst pain in past month = 7-10; Skin Type IV-VI, Female
arm group label: 4DM/SeverePain_IV-VI_Male
description: Worst pain in past month = 7-10; Skin Type IV-VI, Male
arm group label: 4LF/SeverePain_I-III_Female
description: Worst pain in past month = 7-10; Skin Type I-III, Female
arm group label: 4LM/SeverePain_I-III_Male
description: Worst pain in past month = 7-10; Skin Type I-III, Male
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 12 Years
maximum age: 120 Years
healthy volunteers: No
last name: James L Gulley, M.D.
role: Principal Investigator
affiliation: National Cancer Institute (NCI)
facility: 
country: United States
url: https://clinicalstudies.info.nih.gov/cgi/detail.cgi?A_2020-C-0130.html
description: NIH Clinical Center Detailed Web Page
responsible party type: Sponsor
sharing ipd: Yes
ipd description: .All IPD recorded in the medical record will be shared with intramural investigators upon request.
ipd info type: Study Protocol
ipd info type: Statistical Analysis Plan (SAP)
ipd info type: Informed Consent Form (ICF)
ipd time frame: Clinical data available during the study and indefinitely.
ipd access criteria: Clinical data will be made available via subscription to BTRIS and with the permission of the study PI.