Name of beneficiary
- Neumann Medical Ltd.
- Semmelweis University
Project title
Artificial Intelligence-based Structured Heart CT Reporting System
Total financial support for consortium
195 661 864 Ft
Rate of support (percentage)
77,74%
Project launch date
01/01/2020
Planned project end-date
12/31/2021
Full name
Neumann Medical Ltd. (previously Neumann Projekt és Szervezési Ltd.)
Company registration number
01-09-694439
Tax number
12602128-2-42
Location
e1. floor, CityLab, 31, Tűzoltó street, H-1094 Budapest
Project ID number
2019-2.1.10-TÉT-IL-2019-00001
Project Summary
Semmelweis University has launched another high-tech industrial collaboration project with Israeli Arineta and Neumann Medical Ltd. The technology transfer was supported by a grant of HUF 195,661,864 from the National Office for Research, Development and Innovation under the Hungarian-Israeli Industrial R&D Cooperation Tender (code number 2019-2.1.10-TÉT-IL).
The project will develop a structured artificial intelligence-based heart CT scan system in several stages. In the first phase, an integrated coronary artery CT scan clinical workstation software will be created, integrating with the scan workflow process to provide both radiological evaluation and a structured description of the lesions seen, resulting in a clinical finding. In addition, the observed abnormalities and the described clinical abnormalities are combined, so that each abnormality described corresponds exactly to the specific portion of the radiographic imaging on which the radiologist made his or her judgment. The unified workflow, the evaluation of the image elements, and the data collection required for the discovery with the help of the newly created software, thus saving considerable time.
The second step is to create an artificial intelligence platform that provides the optimal environment for analyzing medical images using artificial intelligence. The data collected from the participating health institutes is uploaded anonymously to the central data platform to provide artificial intelligence training. Utilizing the platform’s capabilities, a prototype system is being developed to assist in the identification of coronary plaques and congestions as a decision support system after automatically analyzing new heart CT images. The system provides the opportunity to create full-fledged, automated, artificial intelligence-based tracking systems.