GLOBAL NEWS HEALTHCARE

axial3D Announces Research Collaboration to Drive Automated Medical 3D Printing

chest tomogram

axial3D, a forerunner in the global adoption of 3D printing within healthcare announced in an official release that it is collaborating in a research project which will involve applying deep learning algorithms to the analysis and understanding of structures of human anatomy. The data obtained through deep learning algorithms will be then interfaced within axial3D’s 3D printing ecosystem giving an opportunity to company to drive improved ease of 3D printing for healthcare providers globally.

Deep learning algorithms are a by-product of extensive research conducted by a variety of scientists, academic researchers and software developers. This technology is at the center of several  emerging technologies such as self-driving cars, chat bots and the intelligent personal assistants such as Google Home and Amazon Alexa.

The project aims to apply deep learning algorithms in analysing and understanding of structures of human anatomy to provide insights on pathology and conditions of tissues. Such technology has been found to perform even better than humans especially in identifying pathological conditions by analysing data obtained through non-invasive technologies such as magnetic resonance imaging (MRI). Interfacing the data obtained through these technologies within 3D printing ecosystem can greatly help doctors and surgeons better prepare for surgery and make informed decisions about the necessary surgical interventions.

Building on its current work on algorithm development for 3D printing, axial3D is aiming to create a new set of bespoke algorithms from ground-truth data exclusively for cardiac analysis and interpretation and interface that data to run within its 3D printing ecosystem. The research project is part of the company’s continuous efforts to introducing more cutting-edge technologies to drive research in medical imaging and 3D printing and has been undertaken by the company to bring cutting-edge technologies into the market to improve ease of 3D printing for healthcare providers globally.

chest tomogram
Image Credit/axial3D

The project is being conducted in collaboration with the University College of Dublin (UCD) Institute of Discovery, which is known as interdisciplinary incubator. This collaboration will give axial3D access to some of the brightest talent, and innovative technology in Ireland. The project, which is funded through the FUSION Programme on Deep Learning and Neural Networks for medical visualization applications, will be carried out at axial3D in collaboration with InterTrande Ireland and UCD.

Speaking about the details of the project, Lorenzo Trojan, Machine Learning Lead at axial3D said in an official statement, “We are extremely pleased of having been given the opportunity to work on this project with UCD. Within the scope of this project, a young and talented graduate will have the opportunity to work with our software development team, contribute to exciting our research. To achieve excellence, it is essential to encourage our own staff my creating a stimulating and ambitious work environment as well as work on establishing a strong and health dialogue between the private sector and Academia.”

Giving more details of how the collaboration in the research project can allow axial3D to drive improved ease of 3D printing in healthcare for its healthcare providers globally, Daniel Crawford, Founder & CEO of axial3D added, “We believe the FUSION Programme, similar in spirit to our previous project on Knowledge Transfer Grant that axial3D had previously secured, is a testimony of our strong emphasis on excellence and quality. We are excited about the research outcomes and look forward to bringing that to the market to drive improved ease of 3D printing for healthcare providers globally.” axial3D is using the most advanced, and cutting-edge technology to drive research in the medical imaging and 3D printing, taking advantage of the latest discoveries to provide access to automated medical 3D printing”.

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