From computer-assisted intervention research to clinical impact: The need for a holistic approach

The early days of the field of medical image computing (MIC) and computer-assisted intervention (CAI), when publishing a strong self-contained methodological algorithm was enough to produce impact, are over. As a community, we now have substantial responsibility to translate our scientific progresses into improved patient care. In the field of computer-assisted interventions, the emphasis is also shifting from the mere use of well-known established imaging modalities and position trackers to the design and combination of innovative sensing, elaborate computational models and fine-grained clinical workflow analysis to create devices with unprecedented capabilities. The barriers to translating such devices in the complex and understandably heavily regulated surgical and interventional environment can seem daunting. Whether we leave the translation task mostly to our industrial partners or welcome, as researchers, an important share of it is up to us. We argue that embracing the complexity of surgical and interventional sciences is mandatory to the evolution of the field. Being able to do so requires large-scale infrastructure and a critical mass of expertise that very few research centres have. In this paper, we emphasise the need for a holistic approach to computer-assisted interventions where clinical, scientific, engineering and regulatory expertise are combined as a means of moving towards clinical impact. To ensure that the breadth of infrastructure and expertise required for translational computer-assisted intervention research does not lead to a situation where the field advances only thanks to a handful of exceptionally large research centres, we also advocate that solutions need to be designed to lower the barriers to entry. Inspired by fields such as particle physics and astronomy, we claim that centralised very large innovation centres with state of the art technology and health technology assessment capabilities backed by core support staff and open interoperability standards need to be accessible to the wider computer-assisted intervention research community.

[1]  Andras Lasso,et al.  PLUS: Open-Source Toolkit for Ultrasound-Guided Intervention Systems , 2014, IEEE Transactions on Biomedical Engineering.

[2]  S. Ourselin,et al.  Computer‐assisted surgical planning and intraoperative guidance in fetal surgery: a systematic review† , 2015, Prenatal diagnosis.

[3]  William E. Lorensen,et al.  The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[4]  Jacques Marescaux,et al.  Inventing the Future of Surgery , 2015, World Journal of Surgery.

[5]  Dimos Poulikakos,et al.  Site-specific deposition of single gold nanoparticles by individual growth in electrohydrodynamically-printed attoliter droplet reactors. , 2015, Nanoscale.

[6]  J. Mari,et al.  Interventional multispectral photoacoustic imaging with a clinical ultrasound probe for discriminating nerves and tendons: an ex vivo pilot study. , 2015, Journal of biomedical optics.

[7]  C. Mosse,et al.  Laser-generated ultrasound with optical fibres using functionalised carbon nanotube composite coatings , 2014 .

[8]  Sébastien Ourselin,et al.  The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging , 2014, International Journal of Computer Assisted Radiology and Surgery.

[9]  S. Ourselin,et al.  Brain imaging in the assessment for epilepsy surgery , 2016, The Lancet Neurology.

[10]  C. Jack,et al.  Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) , 2005, Alzheimer's & Dementia.

[11]  Oliver Burgert,et al.  DICOM for Implantations—Overview and Application , 2012, Journal of Digital Imaging.

[12]  Tom Kamiel Magda Vercauteren,et al.  In vivo imaging of the bronchial wall microstructure using fibered confocal fluorescence microscopy. , 2007, American journal of respiratory and critical care medicine.

[13]  P. Beard Biomedical photoacoustic imaging , 2011, Interface Focus.

[14]  Pierre E. Dupont,et al.  Concentric Tube Robot Design and Optimization Based on Task and Anatomical Constraints , 2015, IEEE Transactions on Robotics.

[15]  Gerald Q. Maguire,et al.  Comparison and evaluation of retrospective intermodality brain image registration techniques. , 1997, Journal of computer assisted tomography.

[16]  Blake Hannaford,et al.  Raven-II: An Open Platform for Surgical Robotics Research , 2013, IEEE Transactions on Biomedical Engineering.

[17]  Sébastien Ourselin,et al.  NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics , 2014, International Journal of Computer Assisted Radiology and Surgery.

[18]  F. Jolesz Intraoperative Imaging And Image-Guided Therapy , 2014 .

[19]  Gabor Fichtinger,et al.  OpenIGTLink: an open network protocol for image‐guided therapy environment , 2009, The international journal of medical robotics + computer assisted surgery : MRCAS.

[20]  Thomas M. Krummel,et al.  'Biodesign: The Process of Innovating Medical Technologies , 2009 .

[21]  Ferenc A. Jolesz Comprar Intraoperative Imaging And Image-Guided Therapy | Ferenc A. Jolesz | 9781461476566 | Springer , 2014 .

[22]  Scott C. Molitor,et al.  Biodesign: The Process of Innovating Medical Technologies , 2010 .

[23]  David J. Hawkes,et al.  Clinical application of a surgical navigation system based on virtual laparoscopy in laparoscopic gastrectomy for gastric cancer , 2016, International Journal of Computer Assisted Radiology and Surgery.

[24]  Lyndon da Cruz,et al.  Development of human embryonic stem cell therapies for age-related macular degeneration , 2013, Trends in Neurosciences.

[25]  Ziv Yaniv,et al.  The Image-Guided Surgery Toolkit IGSTK: An Open Source C++ Software Toolkit , 2007, Journal of Digital Imaging.