FotoFinder �Artificial Intelligence
A new era in dermatology
AIMEE
Artificial Intelligence Mole Examination and Evaluation
AI as a Class II Medical Device according to EU MDR
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New: FotoFinder AIMEE (Artificial Intelligence Mole Examination & Evaluation)
FotoFinder Systems GmbH
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AIMEE Scoring
What is AI good for? �What is the added value?�How does it change �the clinical practice in 5 or 10 years?
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AI for the pre-assessment of skin lesions
The Basis: “Deep Learning“
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From early AI to Deep Learning
1950´s
1960´s
1970´s
1980´s
1990´s
2000´s
2010´s
2020´s
Early Artificial Intelligence
causes enthusiasm.
Machine Learning
picks up pace
Breakthroughs in Deep Learning
boost the AI boom.
Artificial Intelligence
Since an early wave of optimism in the fifties, smaller sub-areas of Artificial Intelligence have triggered increasing disruptions.
First Machine Learning, later Deep Learning, a sub-area of Machine Learning.
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Analysis based on
pattern recognition algorithms
Image taking
Score
Input
Feature extraction
Classification
Malignant
Benign
Machine Learning
Output
Old standard: Machine Learning
FotoFinder Systems GmbH
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Melanoma
Pixel-based analysis through convolutional neural network
„Memory“ melanoma
Nevus
Pixel-based analysis through convolutional neural network
„Memory“ naevus
New standard: Deep Learning
FotoFinder Systems GmbH
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FotoFinder Classifier
Image taking
AI Score AIMEE�Moleanalyzer pro
Input
Feature extraction + Classification
Malignant
Benign
Deep Learning
Output
New standard: Deep Learning
FotoFinder Systems GmbH
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AUC 0.945
AUC 0.738
A prospective test data set with 1.981 dermoscopic images shows the superiority of Moleanalyzer pro (black) compared to its predecessor Tuebinger Moleanalyzer 3 (grey), given their AUC (area under the curve) values.
Moleanalyzer pro versus previous algorithm
What makes FotoFinder AI unique for the pre-assessment of skin lesions ?
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FotoFinder Systems GmbH
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350.000 +
A huge base of training images from global AI collaborators
FotoFinder AI benefits from a continuously increasing number of histologically confirmed images of various ethnical skin types.�
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�
FotoFinder Classifier
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2017
AI on the same level as dermatologists
2018
2023
Study „Man against machine reloaded“: FotoFinder AI exceeds dermatologists
Study „Man against machine“:
FotoFinder AI exceeds dermatologists
AI in the course of time
2019
Study „Human with machine“:
Dermatologists can improve their performance using AI
Superior accuracy – �verified by clinical studies
21.09.2023
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VS
FotoFinder AI - Studies
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Haenssle et al. (2019); Annals of Oncology
Melanoma
PECA
DF
BCC
Man against machine reloaded: Broad Spectrum
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Sensitivity 95.0%
Specificity 76.7%
Haenssle et al. (2019); Annals of Oncology
Broad Spectrum
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Sensitivity 95.0%
Specificity 76.7%
According to the study, FotoFinder AI achieves a better result than the average value generated from the test results of the dermatologists.
Broad Spectrum
FotoFinder AI curve
Individual results of dermatologists
Median result of dermatologists
Operation point FotoFinder AI
Haenssle et al. (2019); Annals of Oncology
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Broad Spectrum
Based on specificity and sensitivity of FotoFinder AI, the AUC (area under the curve) value is high (91.8%).
Haenssle et al. (2019); Annals of Oncology
Area under the curve 91.8%
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AI
Responsible
Use of AI�
Early diagnosis requires
follow-up!
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AI Score + Expert opinion
H. Peter Soyer, MD, FACD
Rainer Hofmann-Wellenhof, MD
Andreas Blum, MD
International
FotoFinder expert team
Intelligent tools implemented
In Bodymapping
To detect new & modified lesions
THE MOST EFFICIENT WORK FLOW �THE 2 STEP METHOD OF DERMOSCOPY
STEP 1: CLINICAL DIFFERENTIAL DIAGNOSIS
STEP 2: DERMOSCOPY DIFFERENTIAL DIAGNOSIS
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ATBM master room requirements
No direct sunlight!
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The benchmark for �video dermoscopy worldwide
Baseline
6 months
Female
64 y.o
History of melanoma 0.2 mm (3 years ago)
History of organ transplantation
Melanoma in situ
Do we contribute to life saving of melanoma patients?
Does Dermoscopy & Early Diagnosis have a general positive impact?
Are skin cancer mortality rates decreasing thanks to Dermoscopy?
Australian Cancer Atlas
Melanoma incidence
This correlation, unique to melanoma, has prompted speculation, including by Queensland epidemiologist, David Whiteman, that “‘overdiagnosis’ is [currently] a price paid to improve survival.
Rosendahl,C , Clark, S. (2023). General practice and melanoma management in Australia: controversies and implications for generalist GP training. Medical Journal of Australia, 218(9), 397–398. https://doi.org/10.5694/mja2.51928
2021
2022
54-year-old male
LM Melanoma in situ associated with a compound naevus
2018
2023
LM Melanoma in situ (no associated naevus)
TBP software detects new lesion
Male age 41, > 100 naevi, father has had multiple melanomas, mother is Chinese
Baseline 2017
Sep 2022
Nodular subtype melanoma Breslow 1.9mm (no associated naevus)
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AI
Responsible
Use of AI�
Early diagnosis requires
follow-up!
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Visualizes.