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
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 83
- 试验地点
- 1
- 主要终点
- Feasibility of using facial recognition technology to classify pain
研究概览
简要总结
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 sex 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.
详细描述
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:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 12 Years 至 120 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •INCLUSION CRITERIA:
- •Ability of subject to understand and willingness to sign a written informed consent document.
- •Adults and children (including NIH staff) aged >= 12 years.
- •Participants with diagnosis of a cancer or tumor
- •Participant must be receiving either standard of care or investigational cancer/tumor treatment either at NIH or with a community physician.
- •Must have access to a smart phone (iPhone or Android) with either a data plan and/or access to wireless internet (wifi) or a computer with a camera and microphone and access to internet and must willing to use their device and assume any associated charges from
- •service providers.
排除标准
- •Participants with progressive brain tumors or metastasis. Participants with treated brain metastasis or primary brain tumor are eligible if there is no evidence of progression for at least 4 weeks after CNS directed treatment and there is no impact on voice or facial muscle movements.
- •Participants with Parkinson s disease.
- •Known current alcohol or drug abuse.
- •Any psychiatric condition that would prohibit the understanding or rendering of informed consent.
- •Non-English speaking subjects.
研究组 & 干预措施
3LF/ModPain_I-III_Female
Worst pain in past month = 4-6; Skin Type I-III, Female
2DM/MildPain_IV-VI_Male
Worst pain in past month = 1-3; Skin Type IV-VI, Male
2LF/MildPain_I-III_Female
Worst pain in past month = 1-3; Skin Type I-III, Female
3DF/ModPain_IV-VI_Female
Worst pain in past month = 4-6; Skin Type IV-VI, Female
3DM/ModPain_IV-VI_Male
Worst pain in past month = 4-6; Skin Type IV-VI, Male
3LM/ModPain_I-III_Male
Worst pain in past month = 4-6; Skin Type I-III, Male
4DF/SeverePain_IV-VI_Female
Worst pain in past month = 7-10; Skin Type IV-VI, Female
4DM/SeverePain_IV-VI_Male
Worst pain in past month = 7-10; Skin Type IV-VI, Male
4LM/SeverePain_I-III_Male
Worst pain in past month = 7-10; Skin Type I-III, Male
1LM/NoPain_I-III_Male
Worst pain in past month = 0; Skin Type I-III, Male
2DF/MildPain_IV-VI_Female
Worst pain in past month = 1-3; Skin Type IV-VI, Female
2LM/MildPain_I-III_Male
Worst pain in past month = 1-3; Skin Type I-III, Male
4LF/SeverePain_I-III_Female
Worst pain in past month = 7-10; Skin Type I-III, Female
1DF/NoPain_IV-VI_Female
Worst pain in past month = 0; Skin Type IV-VI, Female
1DM/NoPain_IV-VI_Male
Worst pain in past month = 0; Skin Type IV-VI, Male
1LF/NoPain_I-III_Female
Worst pain in past month = 0; Skin Type I-III, Female
结局指标
主要结局
Feasibility of using facial recognition technology to classify pain
时间窗: 3 months
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.
次要结局
- To determine the feasibility of combining RGB and thermal images with voice recognition transcribed verbal responses(3 months)
- To transcribe patient video responses to assess pain using free-text(3 months)
- To determine the feasibility of using voice recognition technology(3 months)
- To use natural language processing algorithms to assess pain(3 months)