collaboration between hospitals for innovative patient caretrondheim medtech cluster:...

Thomas LangøPhD, Chief Scientist, Research Manager, Medical Technology, SINTEF Digital, Trondheim

Coordinator, Norwegian National Advisory Unit for Ultrasound and Image-Guided Therapy, St. Olavs hospital

Collaboration between hospitals

for innovative patient care

… first of all

Congratulations

to the team in Oslo!

Fantastic new OR facility!

3

Jan Gunnar Skogås

St. Olavs hospitalOpening in 2015

Trondheim MedTech cluster:Cross-disciplinary collaboration

on clinical decision support (since 1995)

NorMIT: A close collaboration between clinicians and technological research scientists

HOSPITALClinical research, method

development, studies

THE UNIVERSITIESBasic research and

education

SINTEFMultidisciplinary applied

contract research

FOR

AHL

FOR

GASTRO

FOR

NEURO

FOR

ENT

FOR

GYN

FOR

ORT

FOR-NorMIT infrastructure

Navigation Camera & Media

publishing

Interventional X-ray imaging

Artis Zeego Dyna CT

Minimally InvasiveSurgical System

Da Vinci Surgery

Optic

Ultrasound

Ultrasound Ultrasound

EBUS BroncoUltrasound

BK- 5000

Laparoscopic UL-probe

Vermon

Ultrasound

SURF

Visualization lab

www.normit.no

User booking of

equipment

NorMIT software

available for free

Akkurat så glad blir man av å åpne en ny Fremtidens Operasjonsrom operasjonsstue.

Mette Bratt og Jan Gunnar Skogås har fellesklippet snoren.

Foto: Christina Yvonne Olsen

- Det er heftig!

Results of NorMIT

• Boost for the nodes

• Access to SotA equipment

• Available for clinic

• Collaboration Trondheim-Oslo

• Common software platform

• Synergies “outside” core of NorMIT

• User payment models established

• Increased potential collaborations UNN + HUS

• Nodes have their own equipment => easier to

collaborate: students, projects, data, publication

Results of NorMIT - Industry

• Spin-offs

• Industry driven projects (BIA)

• Large international companies interest since 2005 (FOR)

Some examples from R&D&I

Experimental Surgery

FOR, AHL

Endovascular

Experimental

Technology for a better society 14

Intraoperative navigation platform for research

and development in image-guided interventions

CustusX base for NorMIT-Nav

Open source platform for R&D (since Jan 2015)

www.normit.no

www.custusx.org

Askeland et al CustusX: An open source research platform for image guided therapy, IJCARS, 2016

Areas of clinical use, testing, or development

• Neurosurgery

• Vascular diagnostics

• Endovascular therapy

• Laparoscopic surgery

• Bronchoscopy

• Anaesthesia

• ENT procedures

• Training and simulation

• Orthopaedics

• Spine interventions

• HIFU / MRgFUS

• Local ablation

• Guiding injections and

biopsies

16

Navigation in

bronchoscopy

Navigation in

endobronchial

ultrasound

Sampling tissue from lymph node with EBUS-TBNA to

investigate for cancer spread Image from CustusX navigation

platform, SINTEF/USIGT. Segmented airways from CT (green),

PET (red/orange) and ultrasound (lower right image)

Upper right: CT with ultrasound sector, lymph node (red) and

tumor (yellow).

Courtesy Håkon O. Leira, St. Olavs Hospital and NTNU and

Erlend F. Hofstad, SINTEF/USIGT

Eurostars project MarianaRanked as 1 of over 400 proposals!

• Peripheral navigation in bronchoscopy

• Steerable and traceable catheter (Deep-Reach) - Netherlands

• High-precision electromagnetic tracking (Deep-Track) - Ireland

• AI and cloud based image guidance (Deep-View) - SINTEF/Ceetron

Ultrasound and navigation in

Laparoscopic surgery

HiPerNavHigh performance soft tissue navigation

Innovative Training Networks (ITN) Call: H2020-MSCA-ITN-2016

15 PhDs (start: spring 2017)

www.hipernav.eu

Laparoscopic liver/pancreas surgery using navigated ultrasound

Courtesy: L C Rekstad,

PE Uggen, R Mårvik,

St. Olavs Hospital,

Trondheim

Nanoparticles as drug carriers - Targeted delivery

0.01 % drugs reaches

the tumor

Higher concentration in tumor,

less side effects

Courtesy: Sigrid Berg, SINTEF/NTNU

Nanotechnology, microbubbles and ultrasound

What’s next?

2 µm

Big data analysis: use knowledge from all previous patients in treatment of THIS patient

Artificial intelligence (Machine learning -> Deep learning)

ImageNet Large Scale Visual Recognition Challenge (ILSVRC)

ImageNet Error rate

http://image-net.org/index

Deep learning vs humans and state-of-the-art methods

Litjens G et al. A survey on deep learning in medical image analysis. Medical Image Analysis 42 (2017) 60–88

bone suppression in x-rays

mammographic mass classification

segmentation of

lesions in the brain

leak detection in airway

tree segmentation

diabetic retinopathy classification

prostatesegmentation

nodule classification

breast cancer metastases

detection in lymph nodes

human expert performance

in skin lesion classification

Norwegian centre for minimally

invasive image guided interventions

and medical technologies, Phase II

II

Tromsø

Trondheim

Bergen

Oslo