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MODELLING AND SIMULATION g Dassault Systèmes has


started to work with the FDA to perform the first in silico clinical trial using a set of cardiovascular virtual patients. This work is based on its model developed for the company’s Living Heart Project, which is on a mission to develop and validate highly accurate, personalised digital human heart models.


External devices Not all medical devices exist in the human body. Altair has worked on multiple orthotics, prosthetics, orthopaedic implants and coronary stent projects with its customers. In a recent collaboration with Medtronic, Altair developed a simulation-led process to produce better performing stents, bringing them to market faster than


previously possible. This allowed Medtronic to perform assessments on 60 design variations in a single night, a significant improvement over the previous process which had taken several days to generate the same result. Using optimisation also allowed for the fast identification of the best performing design variant, with huge gains in product performance.


Sam Mahalingam, CTO at


Altair, said: ‘Whether a device is an external brace (orthotic), replacing a missing body part (prosthetic) or supporting a bone (orthopaedic implant), a low material mass solution is a priority. The use of optimisation enables metal and plastic devices to be lightweight and feel natural to the patient.’


 Auto-injector pen courtesy of Altair Mahalingam added: ‘The


use of topology optimisation creates designs that are both lightweight and organic in their form. In the case of orthopaedic implant, this isn’t just an aesthetic benefit, it is helpful to the processes of osseointegration, the adherence between the living bone and the surface of the metallic implant.’ Polymer solutions firm


 Simulation highlighting a model patient and blood cell


Nolato Medical Solutions has worked with Altair and other partners to develop a multi- use auto-injector pen, using a range of different simulation techniques. After simulations were completed on the component level, the assembly for the pen was simulated.


Kristoffer Glowacki,


vice president of strategic development and technology at Nolato, explained: ‘We then ended up at the complete product level, but this time we included the process history in the simulation models. With this virtual prototype, we were able to test product requirements and ensure that all specifications were fulfilled.’ The primary benefit of simulation in this project was that the simulation results drove the design work, according to Glowacki, who added: ‘During simulations on all levels, we gained a deeper insight into all aspects of the design and requirements. Now we can test many design


INTELLIGENT RETINAL SCREENING


Imaging software now plays an increasingly important role in the automated detection of a range of disorders. A Dutch startup called Thirona has developed RetCAD, machine learning software to analyse retinal images for the presence of Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). The software takes a


colour fundus image of the retina and outputs three core components: a quality assessment of the image, heatmaps to indicate potentially abnormal areas related to AMR and DR, and scores related to the likelihood of each of these diseases. For the image quality


assessment, the RetCAD models have been trained using tens of thousands of images. Mark van Grinsven, head of development RetCAD, explained: ‘It’s important to use a very diverse dataset, including the different ethnicity of the patients and to take into account the differences in the images coming from different colour fundus camera brands. You want your system to be able to handle all images from the different sources at the same time, and in the same fashion. The models must be trained to take into account all sorts of characteristics – that’s a key challenge.’ The heatmaps are used as a visual aid when the AMD or


DR scores are high, so that the clinician can check where the abnormalities are present in the image. Combined with the image quality assessment, this helps the clinician to decide whether the patient should undergo further testing for the presence of AMD or DR. RetCAD is based on a class of deep neural networks called convolutional neural networks. Thirona used some off-the-shelf models when it developed RetCAD but also developed its own models to tailor the software to the tasks it needs to undertake. ‘The models are trained on graphical processing units because of the complexities of the calculations,’ Grinsven added.


RetCAD is camera agnostic and has been available since 2017, but a recent partnership with Topcon Healthcare Solutions means the solution is now available across Europe in the Harmony Referral System eye care platform. The software not only expedites a diagnosis for patients but also helps clinicians analysing these images, so they can spend more time with the patient. Thirona is now working


on training its models to detect other retinal diseases, including glaucoma and cataracts, and wants to extend its reach outside of Europe. The company also applies its AI-driven approach to thoracic CT-scans and thorax imaging.


26 Scientific Computing World October/November 2019


@scwmagazine | www.scientific-computing.com


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