ExperimentalMolecularImaging

Artificial Intelligence Enables Precision Diagnostics in Clinical Medicine

Fabian Kiessling

Experimental Molecular Imaging

ExperimentalMolecularImaging

• Liquid biomarker will replace indications for imaging in cancer screening/therapy monitoring

• Computer-assisted diagnosis will take over major parts of routine image analysis

• Clustered and combined analysis of radiological, pathological and omics data will dissolve the borders between radiology, nuclear medicine, pathology and laboratory medicine

ExperimentalMolecularImaging

Medical image analysis to derive quantitative measures

• Pooling of diagnostic data

• Images are data

• Medical image analysis to extract quantitative information from images

Gillies et al., Radiology, 2016

ExperimentalMolecularImaging

Automation of Lesion Detection

Magnuska, Theek et al., (unpublished)

Detection Edge Refinements Segmentation

Segments Overlap

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• Integrate AI into the routine work tocreate user independent analyses

• Automated workflows increase reproducibility and can take over time consuming tasks

ExperimentalMolecularImaging

Advanced Feature Extraction

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Markov Chain Monte Carlo Data Association Algorithm

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• Priori-propability• Motion model• Posteriori-propability

„motion model Ultrasound Localization Microscopy“ (mULM)

Cooperation with G. Schmitz, RUB ; Opacic, Denks et al. Nat Commun 2018

ExperimentalMolecularImaging

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MLS A549

Intuitive Visualisation

ExperimentalMolecularImaging

Parameterization of dynamic data reduces the number of dimensions and parameters

Theek et al., SciRep 2018

Radiomics: Clustered Analysis

ExperimentalMolecularImaging

Radiomics: Clustered Analysis

Selection of onefeature per imagingbiomarker class

Unsupervised Classification Supervised Classification

Data mining strategy is of highest importance for robust pattern recognition in large amount of data available!

Theek et al., SciRep 2018

ExperimentalMolecularImaging

Clinical Decision Support Systems

U-net

• RSNA Pneumonia Detection Challenge (NIH Cancer Center data set)

• Exploring deep learning solutions to detect pneumonia in chest X-rays

• Making use of “the wisdom of crowds” (1500 teams)

Ronneberger et al.

ExperimentalMolecularImaging

Combination of two key technologies:

• Automated tissue characterization

• Virtual multistaining

Digital Pathology

kindly provided by Fraunhofer Mevis

ExperimentalMolecularImaging

Precision therapy• Assessment of

diagnostic need• Connecting diagnosis

and therapy• SOP

Comprehensive Diagnostics Center Aachen

Founded in 2018 as an entity of the University Clinics Aachen

ExperimentalMolecularImaging

Strategic Alliance: MEVIS

ExperimentalMolecularImaging

Summary

• The future lies in multiparametric diagnosis (imaging, histology, omics)

• Basis for digital disease models (link to Systems Medicine)

– 2 out of 3 medical EU flagship project applications

• Need for computer-assissted data analysis using AI

• Tools for data integration and novel data base concepts required

• Need for „Comprehensive Diagnostic Centers“ and (inter-)national

collaborations and networks

ExperimentalMolecularImaging