automating the diagnosis - who...3 3rd who global forum on medical devices, may 2017 automating the...

AUTOMATING THE

DIAGNOSIS of Childhood Pneumonia

Elina Naydenova Climent Casals-Pascual,

Thanasis Tsanas, Maarten De

Vos 1

2

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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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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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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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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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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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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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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Make a DIGITAL STETHOSCOPE talk…

Clinical Annotation for Validation

Raw Signal

Automated Identification of Lung Sounds

Rhonchus

Crackles

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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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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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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

automating the diagnosis - who...3 3rd who global forum on medical devices, may 2017 automating the...

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