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Snap, Crackle, Pop: Joint Sounds in JIAdcwhittingslow.com/media/presentations/PRoSTalk2019.pdfSnap,...
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Snap, Crackle, Pop: Joint Sounds in JIA
Investigating Acoustic Emissions from the Joints as a Biomarker for JIADaniel Whittingslow, MD/PhD CandidateOmer Inan, PhDDr. Sampath Prahalad, MD
2
JAMS Enrollment – Emory and CHoA
• Children 6-18 years old• 3 Groups:
– Active JIA– JIA Post-Treatment (minimum 6 weeks)– Healthy Controls
Group: # Enrolled: Age (years): Male FemaleJIA 25 12.23 ± 3.1 5 20
JIA Follow-up 12 12.91 ± 2.7 1 11Healthy Controls 18 12.50 ± 3.2 3 15
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Method
• We attach two contact microphones and an IMU onto each knee.
• The patient flexes/extends their leg 10 times.
• Recorded sounds are analyzed for patterns that could:– Differentiate JIA from HCs– Monitor progression of JIA 2 s
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Example Sound Recordings
Representative time domain AE signals of four FE repetitions. Healthy controls (HC) have virtually no sounds, JIA patient have repetitive click with a more heterogenous signal, and the follow-up returns toward healthy.
Active JIA JIA Post-TreatmentHealthy Control
Time (s)
Acc
eler
atio
n (m
m/s
2 )
5
Signal Analysis
x 10
Feature Extractionf1 f2 . . . . f49
1
2
3
.
.
.
n
Cyc
les
Num
12...
3637
Y
11...00
LogisticRegression
PJIA
0.80.9
.
.
.0.30.2
Feature Matrix = feature per cycle, Num = subject numbers, Y = ground truth JIA statusPJIA= Probability Estimate of JIA
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LOSO-CV Accuracy Calculation
Training Set
Testing Set
1 Subject
All Subjects - 1
LogisticRegression
PJIA
0.80.90.70.60.90.30.2
Probability of JIA
𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 𝐻𝐻𝐾𝐾𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐾𝐾 =∑𝑃𝑃𝑆𝑆𝑆𝑆𝑃𝑃𝐻𝐻𝑃𝑃𝑃𝑃𝐻𝐻𝑃𝑃𝐻𝐻𝑃𝑃𝐾𝐾𝑃𝑃
# 𝑆𝑆𝑜𝑜 𝐶𝐶𝐶𝐶𝑆𝑆𝐻𝐻𝐾𝐾𝑃𝑃
𝑱𝑱𝑱𝑱𝑱𝑱 𝒊𝒊𝒊𝒊 𝑲𝑲𝑲𝑲𝑲𝑲𝑲𝑲 𝑯𝑯𝑲𝑲𝑯𝑯𝑯𝑯𝑯𝑯𝑯𝑯 𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑲𝑲 > 𝟎𝟎.𝟓𝟓𝐨𝐨𝐨𝐨
𝑯𝑯𝑲𝑲𝑯𝑯𝑯𝑯𝑯𝑯𝑯𝑯𝑯𝑯 𝒊𝒊𝒊𝒊 𝑲𝑲𝑲𝑲𝑲𝑲𝑲𝑲 𝑯𝑯𝑲𝑲𝑯𝑯𝑯𝑯𝑯𝑯𝑯𝑯 𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑲𝑲 ≤ 𝟎𝟎.𝟓𝟓
𝐴𝐴𝑆𝑆𝑆𝑆𝐴𝐴𝑆𝑆𝐻𝐻𝑆𝑆𝐶𝐶 =𝐶𝐶𝐶𝐶𝑆𝑆𝐻𝐻𝐾𝐾𝑃𝑃 𝐿𝐿𝐻𝐻𝑃𝑃𝐾𝐾𝐻𝐻𝐾𝐾𝐿𝐿 𝐶𝐶𝑆𝑆𝑆𝑆𝑆𝑆𝐾𝐾𝑆𝑆𝐻𝐻𝐻𝐻𝐶𝐶
𝑇𝑇𝑆𝑆𝐻𝐻𝐻𝐻𝐻𝐻 𝐶𝐶𝐶𝐶𝑆𝑆𝐻𝐻𝐾𝐾𝑃𝑃
Repeat for each subject . . .
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Cycle Prediction Accuracy Per Subject
Most subjects had >70% cycle labeling accuracy. Overall Accuracy = 81.7%
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Subject Knee Scores Distribution
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Model Classification Performance
Specificity = 78.9%Sensitivity = 84.0%
ROC - Area Under Curve = 89.7%)
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Feature Importance
11
Breaking the Model
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Tracking the Follow-ups
All but one of the follow-ups showed improvement in their joint health score.The outlier also did not show clinical improvement at 2nd visit.
• Joint sounds show promise for screening, diagnosing, and tracking JIA.
• We should continue recruitment efforts to ensure this model generalizes.
• The feature selection and number of cycles recorded both impact the accuracy of joint sound analysis.
13
Conclusions