Mobile Phone-Based Detection of
Neonatal Jaundice
Oral Presentation #1
MobileMed Enterprises
Christina Baker
Giselle Fontela
Pierce Jones
Brendan Lynch
Sloan Sypher
Problem Statement
• A non-invasive, cost-effective tool for measuring
transcutaneous bilirubin levels has not yet been
developed, indicating there is no means to
accurately and efficiently diagnose neonatal
jaundice in resource-constrained settings.
Background
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Neonatal jaundice affects ~60% of all newborns
Pathological past first 14 days of life
Simple to treat with early diagnosis
Current methods of diagnosis in Sub-Saharan
Africa highly indeterminate
Objective
• To develop a smartphone
application that detects
neonatal jaundice by
measuring skin reflectance at
specific bilirubin-associated
wavelengths through the RGB
specifications of the phone’s
camera when held up to an
infant’s forehead.
Performance Criteria
• Constraints
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Specificity and sensitivity of detection
Portable
Real time quantification (ideal)
Offline vs online
cost
• Limitations
▫ Fully-functional and field-ready app before April 19
▫ Access hardware
▫ SMS vs MMS, phone service
• Exclusions
▫ Infants between 1-14 days
Solution
Characterize
response curve
Adjust response
curve with
algorithm
Gather clinical data
with/without filter
Write software to
analyze data.
Internally or in the
cloud?
Filter?
Design phone case
Correlate
response to gold
standard
Troubleshoot
Test final phone app
in clinical setting
Goals
• Short-term (1-2 weeks)
▫ Correct response curve against background/noise
▫ Develop algorithm to adjust response curve
▫ Get approval for clinical testing
• Long-term (>2 weeks)
▫ Compile research for Weiner Matrix
▫ Obtain clinical data
▫ Define parameters for computer scientists
Factors to Consider
• What did we think about – process
• On skin - background
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Cannot alter phone’s internal settings
Necessity of additional hardware?
External server necessary for data processing?
Simple user interface
Performance Metrics
Smartphone App Test
Blood Serum Test
Smartphone
$299
Device
$2000-$4000
Lens
~$300
Disposable
Probe
$5/test
Total
$599
Total
$2000-$4000
Experiment: Characterization of
Response Curve
Methods: Characterization of Response
Curve
• Access the smartphone camera remotely
• Spectrometer provides light at 5 nm increments
from 350 nm to 800 nm
• Capture images at each of these wavelengths
• Analyze intensity of light at each wavelength
using MatLab code
Experiment: Characterization of
Response Curve
Setbacks
• Saturated light
• Neutral density filter
• Optimizing MatLab code
Conclusions
• Discovered overlap between RGB curves
Future Considerations
• RGB response curves adequate to map to full
white light spectrum
• Background light noise from spectrometer must
be removed from images
• Correction needed for one RGB level versus
another