Leveraging Machine Learning to Effortlessly Track Patient Movement in the Clinic.
Condition: Movement Disorders
MeSH terms: Movement Disorders
Study type: Observational

Summary: 
      The objective of this study is the development of a system that will allow for the precise
      measurement of movement kinematics in a clinical exam setting using natural video from three
      cameras and machine learning to track points of interest. The investigators aim to implement
      such system in an unobtrusive and simply-incorporated way into the physical exam to provide
      exact, objective measures to detect patient movement abnormalities in ways not feasible with
      current tracking technologies.
    

Detailed description: 
      Aim 1: Develop 3D tracking capable of capturing behavior of healthy controls during physical
      exams. In aim 1, the investigators will recruit healthy volunteers to perform a simplified
      physical exam in a replica exam room while being recorded with three synchronized FLIR
      cameras. The simplified exam will consist of four tasks: assessment of tremor, finger chase,
      finger-to-nose movements, and finger tapping. Study staff will then use DeepLabCut (DLC)
      software -technology that trains artificial neural networks to identify user defined features
      in an image - to recognize body parts of interest in physical exam videos. Once the network
      is fully trained the investigators will test its ability to generalize on different patients
      and different contexts. Additional analysis of volunteers' movement during the physical exam
      will be performed to assess for characteristics such as tremor, speed, and tortuosity of
      movement.

      Aim 2: Apply 3D tracking to the clinic to track physical exam behaviors in motor disorder
      patients. In aim 2, the investigators will apply the trained network to the clinic to examine
      the physical exam characteristics of movement disorder patients. Aim 2a will test the DLC
      network's ability to capture movement disorder abnormalities during the physical exam in
      patients and healthy age-matched controls. DLC scores of each test variable will be compared
      to the physician's score of movement according to a standardized scale. The investigators
      expect to find that the DLC tracking method is able to objectively score movement disorders
      in ways that mirror and surpass the ability of the physician. In Aim 2b, the investigators
      will explore the population of recruited patients to see whether it is possible to pull out
      characteristic movements that correspond to certain disease states. In this exploratory aim,
      the investigators expect to be able to separate different disease groups (e.g.: Parkinsonian
      and ataxic patients) from each other based simply on the tracked movement characteristics.

      Research Methods:

      In Aim 1, a movement arena will be built on the University of Colorado Denver Graduate School
      campus using three FLIR cameras with a custom built synchronization and initiation system.
      The investigators will recruit up to 30 healthy 18-70-year-old controls from the University
      of Colorado Denver Graduate School to perform the simplified physical exam (assessment of
      tremor, finger chase, finger-to-nose movements, and finger tapping) while video is captured
      from three angles at 100 Hz. The investigators expect this testing to take no more than 5
      minutes per subject. This video will be used to train the DLC artificial neural network to
      recognize limb features. The investigators will measure the ability of our trained DLC
      network to characterize twelve points of interest on each limb during a physical exam: the
      tips of the four fingers and the thumb, all four metacarpophalangeal joints, the center of
      the hand, the elbow, and the shoulder. A successful outcome will be a network that maintains
      the ability to recognize features of interest at high confidence between different
      individuals and different room contexts.

      In Aim 2a, a tracking arena will be set up in a University of Colorado Movement Disorder
      Clinic exam room. The investigators will recruit up to 100 patients between 18-70 years old
      that are visiting for a movement disorder related appointment as well as spouses and
      relatives of the patients at the appointment for healthy age-matched controls. Patients in
      the clinic will be asked after their visit if they would like to participate in the study. If
      they consent, the physician will obtain written consent and fill out a patient form that
      includes the patient's age, race, sex, and diagnosis (or putative diagnosis). Video recording
      will be started and the physician will perform the simplified physical exam mentioned above.
      The physicians will judge the finger chase and finger-to-nose task as is described in the
      Scale for the Assessment and Rating of Ataxia (SARA, items 5 & 6) from 0-4. The postural
      tremor and finger tapping will be judged according to the Unified Parkinson Disease Rating
      Scale (UPDRS, items 21 & 23) from 0-4. If the patient is visiting with a person that consents
      to be an age-matched control (within 10 years of the patient's age) the physical exam will be
      repeated as above. The investigators expect this testing to take no more than 5 minutes per
      subject, beginning to end. The investigators will then use the DLC algorithm to score the
      physical exam in a way analogous to the physician scoring to assess the accuracy of the
      system.

      In Aim 2b, the investigators will explore the patient data from Aim 2a for movement features
      specific to individual diseases. Data clustering methods (PCA and t-SNE) will be used to
      separate data into groups using high-dimensional DLC tracking data from each physical exam
      task. Success will be measured as the ability to separate diseases from one another based
      solely on the analysis of movement data.
    

Observational model: Other
Time perspective: Other
Sampling method: Non-Probability Sample
Study population: 
        -  Healthy controls: health volunteers recruited within the Anschutz Medical Campus

          -  Age-Matched controls: spouses and relatives of patients visiting the movement
             disorders clinic at the University of Colorado Hospital within 10 years of patient's
             age.

          -  Movement Disorder Patients: patients visiting the movement disorders clinic at the
             University of Colorado Hospital