Detection and Quantification of Hand Eczema by
Visible Spectrum Skin Pattern Analysis
Marc Pouly & Skin App Project Team
[email protected]
Dr. Marc Pouly
Joint work with the University Hospital Zürich
Office D303
+41 41 349 34 92
[email protected]
We gratefully acknowledge the funding of this research by the Velux Stiftung, Zürich
Hand Eczema: AI supporting Patients
-  Hand eczema affects ~14% of the population
-  Can lead to disability to work in many professions
due to chronic course
-  In the US annual direct costs for physician, clinic services and prescription
drugs were $1.6 billion, with indirect costs of approximately $566 millions
-  If hand eczema cannot be prevented, e.g. by changing profession, early
detection of exacerbation is key to an effective treatment
-  Dermatitis patients rely on their own perception whether their skin condition
is stable or worsening - can computers support patients in this regard ?
Whole-Body Eczema: AI supporting Doctors
-  Pharmaceutical industry released a new generation of highly effective drugs
-  Extremely expensive: > 25K € per year and patient
-  Health insurances pay only in severe cases → medical scores
-  Beneficial for all parties (doctors, insurances, …)
Eczema vs. Melanoma
• 
There is quite a bit of literature on automated melanoma detection
• 
Visible appearance of eczema and melanoma are different !
• 
… and false classifications are less expensive / deadly …
Can Computers recognize Eczema Regions ?
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Image Filters make Structures visible
Machine Learning
-  Structures are vectors of filter responses
-  Clustering of structures ➙ Textons
Characteristic structure of healthy and eczema skin
Frequency of textons per sub-image
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Skin Library
From expert labelling
Eczema skin
healthy skin
Background
Eczema Recognition
...
Compare frequency of textons with skin library
true positive
false negative
false positive
true negative
Algorithmic Challenges
Classifier Quality Assessment
Classified as
Eczema
Classified as
Healthy Skin
Pixel with Eczema Skin
true positives
false negatives
Pixel with Healthy Skin
false positives
true negatives
Standard Approach:
• 
Set up confusion matrix
• 
Calculate sensitivity / specificity / ...
• 
Find best parameters with ROC curve analysis
Unreliable / Erroneous Input Data
Incapable me !
Lazy doctors ?
Stressed doctors ?
«Stressed Doctors Problem»
Can be sorted out algorithmically by separating hand from background
Lazy Doctors ? No !
• 
Practitioners focus on the ~60%
most expressive eczema patches
• 
What makes sense for clinical work
is problematic for machine learning
• 
Human experts cannot be expected
to label with perfect precision on a
high-resolution image
• 
Which patches are expressive
enough is highly subjective
• 
We need an expert consensus !
Lazy doctors ?
Skin App goes WWW – Expert Frontend
Skin App goes WWW – Admin Backend
The Curse of Supervised Machine Learning
How to get more Trainings Data ?
•  Real data, real patients, real doctors J
•  Standardized photographic images
•  Skin App Photobox – soon @ UHZ !
Wrap Up
-  Project becomes more and more interdisciplinary involving computer
science (vision, machine learning, usability, web, …), medical science
(dermatology, health economics), mechanical engineering, …
-  Development of a photobox for direct use in dermatologic consultation
-  More images and training data
-  Standardized photographic pictures
-  Interactive evaluation
-  Algorithmic issues with e.g. hair …
-  Algorithms less important compared to other aspects. Do not spend effort
on improving classification when you cannot measure how good you are !
-  Expert platform for improving the general quality of training data
-  Ever seen an excited medical doctor – we did … J