Diagnosing and exploring portosinusoidal vascular disorder using artificial intelligence and reverse engineering

Dec 19, 2022

Work packages:

WP 1: Data collection, patient inclusion and prespecification of the

WP 2: Training and testing of the neural networks with image data

WP 3: Training and testing of a multimodal neural network

WP 4: Comparing the models

WP 5: Reverse engineering of the best performing model

Project recruiting


Our research proposal pursues four specific objectives:
1.) The development of deep learning methods for diagnosis of PSVD based on histological
image data, including multicentre blinded validation.
2.) Combination of image-based diagnosis prediction with clinical data to develop a multimodal
deep learning system.
3.) Identification of features that are recognized as predictive for PSVD
4.) Finding novel clusters and previously unknown biological subgroups of PSVD based on
reverse engineering approaches.

Study file(s)


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