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TRANSCRIPT
AUTOMATING THE
DIAGNOSIS of Childhood Pneumonia
Elina Naydenova Climent Casals-Pascual,
Thanasis Tsanas, Maarten De
Vos 1
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
95% of cases 99% of mortality
Health
Equity
Gap
Source: I. Rudan et al., “Epidemiology and etiology of childhood pneumonia,” Bulletin of the World Health Organization, vol. 86(5), pp. 408–416, 2008.
5% of cases 1% of mortality
Every year, PNEUMONIA kills almost 1 MILLION CHILDREN
APPROPRIATE & TIMELY DIAGNOSIS can reduce mortality by more than 40%
Source: UNICEF/WHO, “Pneumonia: the forgotten killer of children,” 2006.
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
In resource-rich environments…
Advanced medical devices
Stethoscope
Pulse oximeter
Thermometer
X-ray
Blood tests
…
Electronic Medical Records
Clinical expertise
Pulmonologist
Paediatrician
Radiologist
…
Close observation
Strong health systems
Primary care
Referral pathways
Tertiary care
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
In resource-constrained environments…
Advanced medical devices?
Point-of-care tools
Clinical expertise?
Community health workers
DATA INFORMATION
Strong health systems?
Fragmentation
CONNECTIVITY
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
From MEDICAL DATA to DIAGNOSTIC INFORMATION
SIGNAL PROCESSING
Automate
derivation of: RR, HR, SpO2, T
Lung sounds
MACHINE LEARNING
Automate
derivation of: Pneumonia identification
Severity determination
Aetiology determination
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
From MEDICAL DATA to DIAGNOSTIC INFORMATION
IMCI: Integrated Management of Childhood Illness
Source: E. Crain et al., “Is a chest radiograph necessary in the evaluation of every febrile infant less than 8 weeks of age?” Paediatrics, 1991. Source: M. Ebell, “Clinical diagnosis of pneumonia in children,” Point-of-Care Guides,, 2010. Source: T. Lynch et al., “Can we predict which children with clinically suspected pneumonia will have the presence of focal infiltrates on chest radiographs?” Paediatrics, 2004.
Symptom Sensitivity Specificity
Respiratory rate 50-70% 43-95%
Tachycardia 51% 70%
Fever 47% 68%
Oxygen saturation 26-63% 77-93%
Crackles 43% 73%
Wheezing 4% 98%
Other lung sounds 15-26% 98-99%
Biomarkers 82-96% 53-61%
IMCI 69-94% 16-67%
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
From MEDICAL DATA to DIAGNOSTIC INFORMATION
Demonstrate the power of
MACHINE LEARNING to deliver diagnostic results that are:
(1) ACCURATE
(2) AUTOMATED
(3) REPRODUCIBLE
Study location: The Gambia
Age: 2-59 months
Participants: 1,500 children
Clinical characteristics: heart rate, respiratory rate, oxygen
saturation, white blood cell count etc.
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
From MEDICAL DATA to DIAGNOSTIC INFORMATION
SEVERITY DETERMINATION (resp. rate, heart rate, oxygen saturation, crackles and grunting) Sensitivity 84.6% (95% CI 83.6%-85.2%) Specificity 68.3% (95% CI 67.5%-69.3%)
Source: Naydenova et al., The power of data mining in diagnosis of childhood pneumonia, Journal of the Royal Society Interface, 2016, 13 20160266
AETIOLOGY DETERMINATION (resp. rate, heart rate, oxygen saturation and lipocalin-2) Sensitivity 81.8% (95% CI 81.8%-81.8%) Specificity 90.6% (95% CI 89.1%-92.2%)
Gold Standard:
X-rays & Blood Culture
IDENTIFYING PNEUMONIA (respiratory rate, heart rate, oxygen saturation and temperature) Sensitivity 98.2% (95% CI 97.9% - 98.8%) Specificity 97.6% (95% CI 97.1%-98.0%)
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
Make a DIGITAL STETHOSCOPE talk…
Clinical Annotation for Validation
Raw Signal
Automated Identification of Lung Sounds
Rhonchus
Crackles
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
Make a DIGITAL STETHOSCOPE talk…
Symptom Sensitivity Specificity
Crackles 43% 73%
Wheezing 4% 98%
Other lung sounds 15-26% 98-99%
Whole sound algorithm 79% 80%
Gold-Standard Annotation: variable!
Raw Signal
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
FIELD STUDY: Mumbai, India (Feb 2017 – Jun 2017)
Participants: 1,000 children
Age: 1-59 months
Conditions: Pneumonia, other respiratory,
diarrhoea, dengue, malaria etc.
2 sites: LTMGH (public hospital) and
Apnalaya (NGO)
Participants: hospital doctor, nurse,
community health workers, community
doctor
Mobile phone application
Digital stethoscope
Pulse oximeter
Thermometer
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3rd WHO Global Forum on Medical Devices, May 2017
Automating the Diagnosis of Childhood Pneumonia Elina Naydenova, C. Casals-Pascual, T. Tsanas, M. De Vos
It takes a village…
Collaborators
University of Oxford: E. Naydenova, Prof. M. De Vos, Dr. T. Tsanas, Dr.
C. Casals-Pascual, Dr. J. Hunt
John Hopkins Medical School: Prof. W. Checkley, Dr. M. Chavez
Lokmanya Tilak Municipal General Hospital, Mumbai: Dr A. Jadhav,
Dr. S. Zope, Dr. M. Patil
Apnalaya: P. Bora, Dr. N. Salunkhe, Dr. A. Kumar
Funding & Support