IDEAR Project

Identification project data:

Program: RO-02
Contract id number: 19/2020
Project title: Improving Cancer Diagnostics in Flexible Endoscopy using Artificial Intelligence and Medical Robotics – IDEAR
Financial information:
Funders: NO 2014-2021 mechanism and state budget
Value: 5.623.814,20 lei
Contractor authority: Romanian National Ministry of Education
Contractor: University of Craiova

Coordinator – Universitatea din Craiova , Craiova, str. A. I. Cuza nr. 13, DOLJ, ROMANIA
P1 – Universitatea Politehnica din Bucuresti – CCMA, Splaiul Independentei, Sector 6, Bucuresti, Romania
P2 -DELTA HEALTH CARE SRL, Bucuresti, Str. RACARI, nr. 6A, et. 3, biroul 1, sect. 3, c.p. 31828, Romania
P3 – Stiftelsen SINTEF represented by its institute Technology and society (SINTEF), Strindvegen 4, Trondheim, Norway
P4 – St. Olavs Hospital, Prinsesse Kristinas vei 3, Trondheim, Norway, N-7006
P5 – CEETRON AS, Innherredsveien 7, Trondheim, Norway

Project mission

Our main objective is to develop an advanced (TRL5) prototype of a medical software and robotic platform for Improving cancer Diagnostics in flexible Endoscopy using AI and Medical Robotics (IDEAR) to increase procedure success rate, decrease the patient’s radiation exposure, and reduce procedure cost for early cancer detection and treatment.
The IDEAR platform will allow concomitant visualization of the anatomical target(s), the neighboring anatomy, and the CT/MRI image, will find the optimal pathway to target, and precisely guide bronchoscope/endoscope and biopsy tool to reach the difficult to find target. The most innovative feature of IDEAR is performing both diagnostic and treatment during the same procedure using an advanced smart robotic system and customized instruments with dual electromagnetic-optical tracking.

The estimated results for this project are:
–        Development and integration of an advanced electromagnetic and optical guidance computer algorithm for dynamic, automatic, continuous registration for flexible endoscopy diagnosis and surgical treatment.
–        Development and integration of a novel machine learning (ML) and augmented visualization algorithm for automatic suspect peripheral nodules detection in the lungs, pancreas and liver from patient’s imaging data (CT, MRI, US and endoscopic imaging).
–        Design and development of a smart assisting robotic system and flexible instruments like catheters and flexible tip forceps to be guided inside the human body using electromagnetic navigation.
–        Integration of a novel method to detect the catheter shape using a Fiber-Bragg grating on a fiber optic running along the length of the catheter.

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