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Flipping the clinic: in home health monitoring using mobile phones
- 1. eric c. larson | eclarson.com flipping the clinic Assistant Professor Computer Science and Engineering in home health monitoring using mobile phones
- 2. MiPhone 9 Now with iColon. > slide to unlock
- 3. mobileComp Sci. & Engr. databases & data science algorithms arch AI & robotics OS networking languages symbolic computing software mhealth
- 4. 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
- 5. the promise of mHealth: eliminate doctor visits remote / automatic diagnosis equalize developing countries
- 6. stress check telemedicinefitness trainer heart rate current mhealth remote training43,000 apps for health on the app store 96% are for calorie counting & exercise 4% are remote monitoring ! yet to be a disruptive mHealth technology
- 7. consider physician’s needs connecting with patient tracking baselines personalized trending data managing chronic disease 75% of all US healthcare spending is on chronic disease source: center for disease control, 2014
- 8. mHealth sensing in the home for managing disease compliance? cost? privacy? data reliability?
- 10. compliance
- 11. phone as a sensor baseline quantitysensor embedded sensors processing estimated
- 12. accelerometer gyroscope magnetometer /compass dual camera / flash 1+ microphones proximity sensor capacitive sensor gps motorized actuator wireless antenna (s) compliance++; cost--; data reliability?
- 13. what can the mobile phone sense with clinical accuracy?
- 17. lung function asthma COPD cystic fibrosis evaluates pulmonary impairments
- 18. spirometer device that measures amount of air inhaled and exhaled.
- 22. volume-time graph volume time1 sec. FEV1 FVC FEV1% = FEV1/FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity
- 23. FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity FEV1% = FEV1/FVC > 80% healthy 60 - 79% mild 40 - 59% moderate < 40% severe
- 25. flow volume FEV1 FVC 1 sec. PEF PEF: Peak Expiratory Flow FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity flow-volume graph
- 27. obstructive diseases ! resistance in air path leads to reduced air flow
- 28. restrictive diseases ! lungs are unable to pump enough air and pressure
- 31. home spirometry ! faster detection rapid recovery trending
- 32. home spirometry high cost barrier patient compliance less coaching limited integration challenges with
- 33. flow rate volume lung function airflow sensor sound pressure microphone processing estimated
- 34. SpiroSmart availability cost portability more effective coaching interface integrated uploading
- 35. Using SpiroSmart
- 37. Using SpiroSmart
- 38. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features estimation 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 lpc8raw time(s) amplitude flow estimation features vocal tractsource output 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 lpc8raw time(s) amplitude auto-regressive estimate frequency(Hz) 0 500 1000 1500 2000 2500
- 39. 0 1 2 3 4 0 5 10 15 Flow(L/s) Volume(L) 1 2 3 4 Volume(L) 0 1 2 3 4 0 5 10 15 Flow(L/s) Volume(L) 0 2 4 6 8 10 0 1 2 3 4 time(s) Volume(L) audio flow features measures regression curve regression lung function FEV1 FVC PEF
- 40. study design x 3 x 3
- 41. study enrollment participants 52 18-75 years old, mostly healthy study a participants 10 12-17 years old, mixed healthy/abnormal study b participants 56 10-69 years old, mostly abnormal study c enrolledby hospitals
- 42. resultsmeasures regression PEF,SpiroSmart 16 14 12 10 8 6 4 2 2 4 6 8 10 12 14 16 FEV1,SpiroSmart ! ! 6 5 4 3 2 1 1 2 3 4 5 6 FVC,SpiroSmart ! 7 6 5 4 3 2 2 3 4 5 6 7 FEV1%,SpiroSmart 100 90 80 70 60 50 40 40 50 60 70 80 90 100 FEV1%, actual FEV1, actualPEF, actual FVC, actual
- 43. resultsmeasures regression PEF,SpiroSmart 16 14 12 10 8 6 4 2 2 4 6 8 10 12 14 16 FEV1,SpiroSmart ! ! 6 5 4 3 2 1 1 2 3 4 5 6 FVC,SpiroSmart ! 7 6 5 4 3 2 2 3 4 5 6 7 FEV1%,SpiroSmart 100 90 80 70 60 50 40 40 50 60 70 80 90 100 FEV1%, actual FEV1, actualPEF, actual FVC, actual
- 44. −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) resultscurves regression
- 45. can SpiroSmart curves be used for diagnosis?
- 46. • 5 pulmonologists • normal/abnormal subjects curves • unaware if from SpiroSmart / spirometer survey
- 49. results normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate error 8% false negative 4% false positive 14% one off 10% identical 64% ! ! abnormal vs normal 96%
- 50. appropriate for trending and screening
- 51. global health non-profit patient and doctors pharmaceutical drug trials
- 52. in 10 years, COPD will surpass AIDS/HIV as the leading cause of death in developing nations
- 64. Kernicterus Statistics 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., Pediatric Research 2010
- 66. TcBNon-invasive Correlates 0.75-0.93 $7000 Quick results Screening tool for TSB
- 67. 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
- 68. Visual Assessment • Parents • Many physicians • Traveling practitioners In Hospital At Home Screening Challenges Tend to underestimate
- 69. bilirubin level in blood jaundice level blood draw yellowness camera processing estimated
- 70. bilicam
- 71. Study Evaluation 100 newborn participants • <1 day old when enrolled • 59% white ! 2 medical centers ! Data collected by medical professionals using iPhone 4S Photos: with & without flash TSB (ground truth) TcB (control) 3 - 5 days old
- 72. Data Collection App ! Standardize • white balance • card position • phone position !
- 73. Noisy Data
- 74. Automatic Quality Control ✔ ✖ ✖ ✖ ✖ ✖ Ideal Glare Overexposed Occlusion Shadow Underexposed
- 76. 400 500 600 Wavelength (nm) RelativeBilirubin AbsorptionProbability Bilirubin Absorption Properties
- 77. Feature Extraction Gradient (of RGB channels) with & without flash skin RGB Cr CbY YCbCr a* b* L* L*a*b*
- 80. Regression Ensemble No 90th percentileYes regressions agree mean Least Angle Regressions LARS-Lasso Elastic Net Support Vector Regressions k-Nearest Neighbor Random Forest Regression Bilirubin Estimate
- 81. Results 0 5 10 15 20 25 0 5 10 15 20 25 TSB Ground Truth (mg/dl) EstimatedBilirubin(mg/dl)
- 82. Results 0 5 10 15 20 25 0 5 10 15 20 25 TSB Ground Truth (mg/dl) EstimatedBilirubin(mg/dl)
- 83. Results 0 5 10 15 20 25 0 5 10 15 20 25 TSB Ground Truth (mg/dl) EstimatedBilirubin(mg/dl) BiliCam rank order 0.85 correlation
- 84. Results 0 5 10 15 20 25 0 5 10 15 20 25 TSB Ground Truth (mg/dl) EstimatedBilirubin(mg/dl) TcBs correlate 0.75 - 0.93 BiliCam rank order 0.85 correlation TcB rank order 0.92 correlation
- 85. 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)
- 86. 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
- 87. 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) Interpretation 9 high risk cases based on TSB
- 88. 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) Interpretation BiliCam 2/9 missed high risk (22%) 85% blood draws avoided
- 89. 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) Interpretation BiliCam 2/9 missed high risk (22%) 85% blood draws avoided TcB 2/8 missed high risk (25%) 88% blood draws avoided BiliCam is sufficient for newborn Jaundice screening, but it is unknown how user error affects reliability
- 93. cervical cancer screening affect sensing intra ocular pressure oxygen volume VO2 cardiac output & blood pressure
- 94. > slide to unlock Thank You!
- 95. eric c. larson | eclarson.com flipping the clinic Assistant Professor Computer Science and Engineering in home health monitoring using mobile phones collaborators:! Suku Nair Eric Bing Joseph Camp Dinesh Rajan Sohail Rafiqi Mark Wang Shwetak Patel Jim Stout, MD Jim Taylor, MD Margaret Rosenfeld, MD Gaetano Boriello Mayank Goel Lilian DeGreef eclarson.com eclarson@lyle.smu.edu @ec_larson