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Selection of Our Current Projects

DRIVE

Development and Validation Platform for Data-driven AI Innovations in Medical Technology.


Partners: Universitätsklinikum Augsburg, Bayern-Innovativ

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ADDIFEM

Optimized Clinical Point-of-Care Patient Care through Individualized Implants Using 3D Printing Technology.


Partners: Universitätsklinikum Heidelberg, KLS Martin Group

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DRIVE - Development and Validation Platform for Data-driven AI Innovations in Medical Technology

2023 - 2025

Project Description

The DRIVE project aims to address the challenges of AI development with an innovative software platform that bundles the work steps from the idea to approval in a consolidated development platform. Currently, these work steps are distributed between the clinic, the medical device manufacturer and clinical service providers and are characterized by media disruptions and interface problems. This leads to high costs in development and significant delays in approval. The aim of the DRIVE project is to develop this process together with a leading Bavarian university hospital (Augsburg University Hospital) in medical imaging and image processing and to test the optimization of workflows in practice. The platform is provided via a web application based on the principle of “service-oriented architecture”. The performance of the DRIVE platform will be demonstrated using the case study “Glioblastoma follow-up using MRI”.

Project Goals

INTEGRATED SOFTWARE PLATFORM

Research and development of an integrated software platform to accelerate the work steps from the idea to the approval of AI medical devices.

REFERENCE ARCHITECTURE

Establishment of a reference architecture for AI projects with the aim of transferring innovative AI product ideas and translational research to clinical application more quickly.

DRIVE Solutions

RAW DATA EXTRACTION

GENERATION OF TRAINING DATA (ANNOTATION)

GENERATION OF TRAINING DATA (ANNOTATION)

  • Automatic search of existing data sets according to certain specifications (MRI, histology, ICD / OPS, neurological outcome)


  • Selection and signing of cases by the doctor (Quality Gate 1)


  • Automated compilation and anonymization 


  • Secure upload

GENERATION OF TRAINING DATA (ANNOTATION)

GENERATION OF TRAINING DATA (ANNOTATION)

GENERATION OF TRAINING DATA (ANNOTATION)

  • Data collection and verification of anonymization by data protection officers


  • Technical quality check of the overall data records (Quality Gate 2)


  • Annotation of the datasets by medical experts using a customizable integrated annotation tool


  • Quality assurance of the annotations (cross-check, inter-rater reliability, etc.)

AI DEVELOPMENT AND VALIDATION

GENERATION OF TRAINING DATA (ANNOTATION)

AI DEVELOPMENT AND VALIDATION

  • Provision of real-world clinical data for medical  AI validation


  • Seamless integration of clinical experts


  • Provision of data logs and overall evaluation for the product file


  • Automatic download of documentation required for regulatory approval

Partners & Funding

The project is funded by Bayern Innovativ

https://www.bayern-innovativ.de

ADDIFEM - Optimized Clinical Point-of-Care Patient Care through Individualized Implants Using 3D Printing Technology.

2024 - 2026

Project Description

The ADDIFEM project aims to significantly improve the treatment of patients with complex midface fractures as well as in reconstructive, oncological, and congenital defect correction surgery. To achieve this, state-of-the-art 3D printing technology is being implemented directly in the clinic for the on-site production of patient-specific implants.

 

Currently, the lengthy planning and manufacturing process for such implants significantly delays surgical procedures. The BMBF funding for this model project facilitates close collaboration between clinics and industry, enabling faster, more efficient, and precise implant production while shortening innovation cycles.


In the first phase, the project focuses on automating and optimizing digital planning and implant design. The second phase aims to test on-site implant production using an industrial Point-of-Care (PoC) unit directly integrated into the clinic. The feasibility study seeks to establish a new, faster manufacturing process for 3D-printed implants, which could later be approved for use beyond UKHD.

By directly planning and producing implants on-site, the team expects faster patient treatment, improved precision and fit, higher success rates, and lower complication risks. Additionally, the new manufacturing method is anticipated to be more efficient and cost-effective, especially when using high-cost materials such as PEEK (Polyetheretherketone).


The integration of new materials and processes under clinical conditions represents a unique opportunity to bring production to maturity. Training sessions for medical professionals and engineers will be conducted in close collaboration to ensure optimal outcomes for patient care.

See Press Release

Partners & Funding

The project is funded by Bundesministerium für Bildung und Forschung (BMBF)

https://www.bmbf.de/DE/Home/home_node.html

Contact

Inventor AI

c/o M3i GmbH, Pettenkoferstraße 24, 80336 München, Germany

info@inventorai.de

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