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RadioEye

Diagnosis Support Tool – for Atypical Cases

Our Vision

We thrive to be the worldwide gold standard in diagnosis support for radiologists. We strive for an image-based search tool that encompasses broad variants of pathologies from head to toe.

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Report with Confidence

The RadioEye image case collection contains reference radiological images with histologically and/or clinically verified diagnoses. RadioEye  provides  reassurance for atypical or uncommon radiological findings. 

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Search by images, not by text

Browse through a validated set of cases that will help you in your diagnosis.

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Use by one mouse click

With the seamless integration into PACS, RadioEye will enhance  your workflow.

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Easy to use by setting filters

Find similar cases easily by manually setting  filters. This saves time in critical situations.

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AI based image search

RadioEye provides ranked images with similar patterns to help you think of all differentials.

Our Mission

Our Mission is to become the gold standard Reference Tool in Radiology that provides radiologists with easy access to an extensive, image-based collection of clinically validated cases for a wide range of disease variants covering all major pathologies from head to toe.  

RadioEye will be an invaluable reference tool that will increase the Radiologist’s confidence and accuracy in diagnosing atypical cases while saving precious time that can lead to improved patient outcomes.

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Our Impact Potential

80,000h

of radiology work saved in DACH weekly

+27%

efficiency gain

820,000€

annual avg. revenue gain per radiology unit*

*Average annual revenue per doctors office in radiology, nuclear medicine, and radiotherapy in € from Statistisches Bundesamt: Kostenstruktur bei Arzt -und Zahnarztpraxen, sowie psychologischen Physiotherapeuten 2019

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 Artificial Intelligence made
useful and transparent

For our image search tool, we train a powerful convolutional neural network on thousands of images from different pathologies to learn a vector space for our database where similar images cluster.

Meet Our Team

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Katharina Erb-Eigner

PD Dr. med. Dr. med. univ.

Project Lead

Fully-Licensed Radiologist,

Scientific Work in Eye & Eye Socket MRI,

Department of Radiology, Charité 

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Jan D'Alvise

CEO

Leading founding team to complete product development of the RadioEye AI decision support software tool designed to improve efficiency and diagnostic accuracy for Radiologists. Developing and implementing a US/global business and commercialization strategy and plan, and finalizing regulatory pathway and strategy with goal to receive FDA approval and commercial launch in 2024.

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Lorenz Rumberger

M.Sc.

Computer vision researcher focused on problems in the bio-medical domain, with 5 years of experience within academia and startups.   

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Winna Lim

MD

Radiologist in Training

Department of Radiology,

Charité

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Simon Stemplinger

Serial co-founder and CTO with over 25 years of experience in building digital products and leading engineering organizations.

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Ahi Sema Issever

PD Dr. med. 

Medical Lead 

Fully-Licensed Radiologist, Scientific Work in Muskulosceletal Radiology, Department of Radiology, Charité

Currently we need

Co-Creation

National and international clinical cooperation partners for product development and testing.

Partnerships

Partnership with industry to test integration options.

Supported by

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