Collaboration between hospitals for innovative patient careTrondheim MedTech cluster:...
Transcript of 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!
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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
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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