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Mobile healthforthemasses.2015
- 1. eric c. larson | eclarson.com mobile health for the masses Assistant Professor Computer Science and Engineering health monitoring using mobile phones
- 2. > slide about me
- 3. Shwetak Patel James Fogarty Jon Froehlich
- 6. 75% of all US healthcare spending is on chronic disease source: center for disease control, 2014 state of health in the US US infant mortality rate ranks ~23rd in the world, but 1st in delivery cost source: national vital statistics reports, 2014 9 out of 10 of US doctors feel medications are overprescribed source: american medical association, 2012
- 7. solution: more actionable data, better managed care, preventative care, but with lower cost one strategy: mhealth
- 8. what is mhealth? mHealth (em-‘helth ) an abbreviation for mobile health, a term used for the practice of medicine and public health supported by mobile devices
- 9. the promise of mHealth: eliminate doctor visits remote / automatic diagnosis equalize developing countries
- 10. stress check fitness trainer heart rate current mhealth telemedicine remote training
- 11. stress check telemedicinefitness trainer heart rate current mhealth remote training ~30,000 apps for health ~95% are for calorie counting & exercise ~5% are remote monitoring, wellness, smoking cessation, references, etc. yet to be a disruptive mHealth technology
- 12. mHealth sensing outside the clinic compliance? cost? privacy? data reliability?
- 13. phone as a sensor baseline process clinical quantity sensor or human embedded sensors processing estimated
- 14. accelerometer gyroscope barometric pressure temperature magnetometer /compass dual camera / flash 1+ microphones proximity sensor capacitive sensor gps motorized actuator wireless antenna (s) compliance++; cost--; data reliability?
- 15. what can the mobile phone sense with clinical accuracy?
- 28. TcBNon-invasive Correlates Well $7000 Quick results Screening tool for TSB
- 29. 20 15 10 5 0 0.5 1 2 3 4 5 6 Age (days)In Hospital Bilirubin(mg/dL)Newborn Bilirubin Levels 75th percentile 25th percentile
- 30. Visual Assessment • Parents • Many physicians • Traveling practitioners In Hospital At Home Screening Challenges Tend to underestimate
- 31. bilirubin level in blood jaundice level blood draw yellowness camera processing estimated
- 32. bilicam Shwetak PatelJim Taylor Lilian DeGreef Mayank Goel Jim Stout
- 33. Study Evaluation 100 newborn participants • <1 day old when enrolled Data collected by medical professionals using iPhone 4S BiliCam TSB (ground truth) TcB (control) 3 - 5 days old
- 34. Noisy Data
- 35. Automatic Quality Control ✔ ✖ ✖ ✖ ✖ ✖ Ideal Glare Overexposed Occlusion Shadow Underexposed
- 36. Algorithm Overview Bilirubin Estimate Color Balance Extract Features Apply Regressions 400 500 600 Wavelength (nm) RelativeBilirubin AbsorptionProbability RGB Cr Cb Y YCbCr a* b* L* L*ab with & without flash skin Gradient (of RGB channels) Extract Features Color Balance
- 37. Regression Ensemble No 90th percentileYes regressions agree mean Sigmoidal LARS-Lasso Elastic Net Support Vector Regressions Encapsulated Neighbor Random Forest Regression Bilirubin Estimate
- 38. 0 5 10 15 20 25 0 5 10 15 20 25 Results TSB Ground Truth (mg/dl) EstimatedBilirubin(mg/dl)
- 39. 0 5 10 15 20 25 0 5 10 15 20 25 BiliCam rank order 0.85 correlation Results TSB Ground Truth (mg/dl) EstimatedBilirubin(mg/dl)
- 40. 0 5 10 15 20 25 0 5 10 15 20 25 TcBs correlate 0.75 - 0.93 BiliCam rank order 0.85 correlation TcB rank order 0.92 correlation Results TSB Ground Truth (mg/dl) EstimatedBilirubin(mg/dl)
- 41. Interpretation 20 15 10 5 0 high risk high intermediate risk low intermediate risk low risk Bilirubin(mg/dL) 0.5 1 2 3 4 5 6 Age (days)
- 42. 20 15 10 5 0 high risk high intermediate risk low intermediate risk low risk Bilirubin(mg/dL) 0.5 1 2 3 4 5 6 Age (days) Bhutani Nomogram
- 43. 20 15 10 5 0 high risk high intermediate risk low intermediate risk low risk Bilirubin(mg/dL) 0.5 1 2 3 4 5 6 Age (days) 9 high risk cases based on TSB Interpretation
- 44. 20 15 10 5 0 high risk high intermediate risk low intermediate risk low risk Bilirubin(mg/dL) 0.5 1 2 3 4 5 6 Age (days) BiliCam 2/9 missed high risk (22%) 85% blood draws avoided Interpretation
- 45. 20 15 10 5 0 high risk high intermediate risk low intermediate risk low risk Bilirubin(mg/dL) 0.5 1 2 3 4 5 6 Age (days) BiliCam 2/9 missed high risk (22%) 85% blood draws avoided TcB 2/9 missed high risk (22%) 88% blood draws avoided BiliCam is sufficient for newborn Jaundice screening, but it is unknown how user error affects reliability Interpretation
- 46. Next steps: developing world Kernicterus:+ 21+($8+mill)+ Hazardous+ jaundice:++1158+ ($50,000)+ Extreme+jaundice:++ 2,317+($20,000)+ Severe+jaundice:+35,000+ ($8,500)+ Phototherapy:+290,000++($1,000)+ Visible+jaundice:+3.5+million+ Births/year:+4.1+million+ In the US Middle- & low-income countries: • 75,000 cases kernicterus/year • 114,000 newborn deaths/year • 65% newborn deaths from kernicterus kernicterus 21 ($8million) Bhutani et al. 2013, Pediatric Research 2010
- 49. pupillary response drug impairment concussion stroke pain fatigue cognitive disabilities arousal cognitive load
- 50. cognitive load refers to the total amount of mental effort being used in the working memory
- 51. multiply these two numbers 3 6
- 53. pupillary change cognitive load contact IR camera visible iris camera regression estimated
- 54. PupilWare Suku Nair Sohail Rafiqi Mark Chatchai Ephrem Fernandez
- 55. pupilware cognitive load study zoomed NIR cameras. It constantly captures the pupils of the participant reflecting any changes in their size in real-time. For the device under test, a web camera, we use a Microsoft Lifecam [48] of resolution 1280x720 pixels. The web camera stores the video of the participant during each task at 15 frames per second. Figure 1 -- Experiment Setup
- 56. pupilware 20 participants over 500 iterations of digit span tasks using both laptop embedded camera and smartphone for light brown and colored eyes: identical to pupillometer captures sub millimeter pupil response first automatic classification of cognitive load
- 57. pupilware 20 participants over 500 iterations of digit span tasks using both laptop embedded camera and smartphone for light brown and colored eyes: identical to pupillometer captures sub millimeter pupil response first automatic classification of cognitive load
- 58. markers of pain PupilWare head injury cravings context aware computing attention future work in sympathetic nerve damage fatigue and sleep deprivation
- 61. spirometer device that measures amount of air inhaled and exhaled.
- 64. flow volume volume time FEV1 FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity > 80% healthy 60 - 79% mild 40 - 59% moderate < 40% severe
- 66. flow rate volume lung function airflow sensor sound pressure microphone processing estimated SpiroSmart Shwetak PatelMayank Goel Gaetano Boriello Jim Stout Margaret Rosenfeld Elliot Saba
- 67. 2012 study: appropriate for trending and screening if you have access to a smartphone
- 70. SpiroCall
- 71. any phone in the world can be used as a spirometer 2015 study: 50 participants 4 styles of phone including feature phone head to head with spirometer SpiroCall vs spirometer ~5% spirometer vs spirometer ~5%
- 72. in 10 years, COPD will surpass AIDS/HIV as the leading cause of death in low income nations global initiative for chronic obstructive lung disease (GOLD 2014) world health organization, global burden of disease (2013)
- 73. ongoing research third party validation FDA device approval gamification and compliance, reliability scalable training of public health workers pupillary response lung functionjaundice
- 74. > slide to unlock Thank You!
- 75. eric c. larson | eclarson.com mobile health for the masses Assistant Professor Computer Science and Engineering health monitoring using mobile phones collaborators: Suku Nair Eric Bing Sohail Rafiqi Mark Wang Ephrem Fernandez, MD, PhD Gaetano Boriello Shwetak Patel Jim Stout, MD Jim Taylor, MD Margaret Rosenfeld, MD Mayank Goel Lilian DeGreef Joseph Camp eclarson.com eclarson@lyle.smu.edu @ec_larson
- 76. eric c. larson | eclarson.com Assistant Professor Computer Science and Engineering BiliCam home screening for newborn jaundice SpiroCall mobile and smartphone spirometry MobiScreen mobile training for cervical cancer screening PupilWare pupillary response using everyday cameras