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Machine learning in (electro)diagnosis and prognosis of acquired inflammatory neuropathies

Camiel Verhamme

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Courtesy to Ilse Lucke

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Courtesy to Ilse Lucke

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Acquired inflammatory neuropathies

• Chronic inflammatory demyelinating poly(radiculo)neuropathy (CIDP)

• Guillain-Barré syndrome (GBS)

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CIDP• Heterogeneous spectrum of rare autoimmune diseases of the peripheral

nerves

• EFNS/PNS 2010 guidelines: nerve conduction studies (NCS) essential in diagnosis

• Both overdiagnosis and underdiagnosis of CIDP are common - inappropriate use and misinterpretation of diagnostic tests

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HypothesisCurrent consensus-based electrodiagnostic guidelines can be improved by a data-

driven approach using machine learning algorithms

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MethodsRetrospective study

• Extensive NCS in patients with an acquired inflammatory neuropathy in thedifferential diagnosis (2010-2019)

• NCS protocol: median, ulnar, musculocutaneous, radial, peroneal, sural andtibial nerves at both sides after warming up

• Final diagnosis was checked in patient file

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Patient inclusion:• CIDP (at least ‘possible’ electrodiagnosis)• 2 controls per CIDP patiënt - gender, age and height matched

Exclusion• Multifocal motor neuropathy• Guillain-Barré syndrome• CIDP without electrodiagnostic support

Methods

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• Random forest classifier

• Data: 70% training and 30% validation

• Trained on a selection of relevant parameters (difference between CIDP andcontrols: relevant if p < 0.05)

CIDPNo CIDP

Input Output

Methods – Machine Learning

Automated extraction

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Yes

YesYes No

y

n

n

y

y

n

ny

y

Methods – Machine Learning

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YesYes

YesYes

No

No

y

n

n

y

y

n

ny

y

Methods – Machine Learning

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YesYes

YesYes

No

No

Yes: 4No: 2

à CIDP

Methods – Machine Learning

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Results• 2010-2019: 809 extensive NCS• Total 93 CIDP patients

CIDP (N=93) Controls (N=196)Age 61.7 ± 14.2 61.5 ± 13.9Gender 59 (63%) 118 (60%)Height 176.4 ± 10.1cm 176.7 ± 9.8 cm

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EFNS/PNS electrodiagnostic criteria EFNS/PNS Machine learningCIDP Controls CIDP Controls

Definite 69% 12% 97% 22%Probable 15% 16%Possible 6% 10%No CIDP 10% 63% 3% 78%Sensitivity 90.3% 92.8%Specificity 62.9% 72.4%Accuracy 72.0% 79.1%

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Machine learningEFNS/PNS Machine learningCIDP Controls CIDP Controls

Definite 69% 12% 97% 22%Probable 15% 16%Possible 6% 10%No CIDP 10% 63% 3% 78%Sensitivity 90.3% 92.8%Specificity 62.9% 72.4%Accuracy 72.0% 79.1%

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Conclusion• Machine learning algorithms may improve diagnosis of CIDP

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Improving diagnosis• Development of electrodiagnostic index test to diagnose CIDP:

Regularized logistic regression models

• External validation: on data from other centers

• Prospective validation

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Prediction of treatment response and outcome

Development of electrodiagnostic index tests:

• CIDP

• GBS

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Easy-to-apply-tool

Probability that an individual patient has CIDP

Probability of a favorable treatment response

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Project groupAmsterdam UMC, location AMCIlse LuckeMireille Kamminga

Wouter PottersFilip Eftimov

Michiel HofCamiel Verhamme

UMC UtrechtStephan GoedeeLudo van der Pol

Erasmus MCJudith DrenthenBart Jacobs

Pieter van Doorn

Centre de Reference Neuromusculaire, Universit de Louvain, Brussels, BelgiumPeter van den Bergh

Clinical Centre of Serbia, SerbiaStojan Peric

c.verhamme@amsterdamumc.nl/nmz-machinelearning@amc.nl

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Voorbeeld voettekst | juli 2018

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Tibialis AH left knee PeakDurationNegativePeak: 0.22Medianus APB left elbow TakeOffLatency: 0.17Ulnaris ADM left axilla TakeOffLatency: 0.17Medianus APB left axilla TakeOffLatency: 0.16Tibialis AH right knee TotalPeakPeakDuration: 0.14Ulnaris ADM right erb PeakDurationNegativePeak: 0.14Ulnaris ADM left axilla TotalPeakPeakDuration: 0.14Ulnaris ADM right erb TakeOffLatency: 0.13Ulnaris ADM left axilla PeakToPeakAmplitude: 0.13Ulnaris ADM left wrist PeakToPeakAmplitude: 0.13Ulnaris ADM right erb TotalPeakPeakDuration: 0.13Ulnaris ADM left wrist PeakToPeakAmplitude: 0.12Ulnaris ADM left wrist NegativePeakArea: 0.12Tibialis AH left ankle PeakToPeakAmplitude: 0.12

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Ulnaris V left wrist NegativePeakAmplitude: 0.11Ulnaris ADM left erb TakeOffLatency: 0.11Ulnaris ADM left axilla PeakDurationNegativePeak: 0.11Ulnaris ADM right axilla TakeOffLatency: 0.11Tibialis AH left knee PeakToPeakAmplitude: 0.11Medianus APB left axilla TotalPeakPeakDuration: 0.10Ulnaris V left axilla TakeOffLatency: 0.10Ulnaris ADM left axilla PositivePeakArea: 0.10Ulnaris ADM left forearm ME PeakDurationNegativePeak: 0.10Tibialis AH left knee TotalPeakPeakDuration: 0.09Ulnaris ADM left axilla NegativePeakArea: 0.09Ulnaris ADM left axilla NegativePeakAmplitude: 0.09

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0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1a. distallatency

1b.conduction

velocity

1c. f-wavelatency

1d. absenceof f-wave

1e.conduction

block

1f.temporaldispersion

1g. distalCMAP

duration

2. Probable

CIDP CIDP after adaptations Controls Controls after adaptations

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ObjectivesTo explore the diagnostic accuracy of: • Automated EFNS/PNS electrodiagnostic criteria with and without adaptations

• Machine learning algorithm

VS

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Adaptations to criteria:

• Only count if minimal CMAP NP amplitude >1 mV• Abnormal temporal dispersion in legs 100% instead of 30% • Conduction block based on CMAP NP area instead of amplitude and

corrected for temporal dispersion (van Schaik et al. 2010)• Adaptation of distal CMAP duration normal values (Mitsuma et al. 2015)

Methods

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Voorbeeld voettekst | juli 2018

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Machine learning

EFNS/PNS Adapted EFNS/PNS Machine learningCIDP Controls CIDP Controls CIDP Controls

Definite 69% 12% 44% 9% 97% 22%Probable 15% 16% 17% 2%Possible 6% 10% 6% 4%No CIDP 10% 63% 32% 85% 3% 78%Sensitivity 90.3% 67.2% 92.8%Specificity 62.9% 85.5% 72.4%Accuracy 72.0% 79.6% 79.1%

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New guidelines in preparation

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