Virtual patients have been gaining attention in recent years as a way to augment pre-clinical tests and even clinical trials. While for many applications the concept is still in its infancy, the use of virtual patients has become standard practice in the development process of orthopedic implants.
Why does the University Medical Center (UMC) Utrecht in the Netherlands invest in the latest 3D technologies for their craniomaxillofacial (CMF) practice? To bridge the gap between research and the clinic and provide cutting-edge care by delivering 3D planning, 3D design of guides and models, technical support to surgeons, and technical information to patients all in one place.
When we think about the impact of 3D printing on the healthcare industry, we mostly think about the productinnovations it enables. From today’s reality of 3D printing fully-customized skull implants, to future hopes and promises of printing vital organs. These product innovations build on the fact that 3D printing is an inherently digital manufacturing technology, enabling complex designs and increased functionality. Moreover, 3d printing allows for the creation of patient specific instruments and truly personalized implants that take into account the patient’s unique anatomy. However, the impact of the technology doesn’t end there; 3D printing also enables significant process innovations.
More haptic perception, fully integrating with electronic medical records, and talking to patients without the use of screens are just a few reasons discussed at the 3D Printing in Medicine Course as to why hospitals are turning to Point-of-Care 3D Printing. The event, which took place at the M Museum in Leuven, Belgium, on June 13 and 14, 2019, brought together clinicians, medical imaging specialists, engineers, and other experts involved in turning medical imaging data into anatomical models in hospitals to share learnings, findings, and cases to further the field.
Surgeons would need to make 50% fewer changes to AI-based pre-operative plans compared to current ones. This is according to a research project we conducted here at Materialise with Dr. Raf De Vloo, an orthopedic surgeon at AZ Klina in Belgium, in which we applied AI-based planning to 193 cases. This technology learns an individual doctor’s preferences for surgical approaches and, based on those, provides higher-quality pre-operative plans.
These guidelines are furthermore influential as they will support new billing codes, called CPT codes, for Point-of-Care 3D Printing, which are due to implemented in July this year. These initial CPT codes make it possible to collect more data on the prevalence of 3D printing and for what cases it is used across U.S. hospitals and will ultimately pave the way for further reimbursement initiatives.
4C Medical Technologies is a medical device company working on minimally invasive solutions for structural heart diseases. Vice President of R&D and Operations Dr. Saravana Kumar and his team, are working striving to bring to market an their award-winning AltaValve, an innovative solution to addressing mitral valve regurgitation. Thanks to two delivery options, the device is easy to use and suitable for any anatomy in the patient population.
Recently, many hospitals have started making a shift, from using medical images primarily for diagnostic purposes, to integrating them in patient-specific surgical planning. This has created enormous advantages for hospitals and their patients, and is largely supported by the expanding role of the radiologist as imaging expert.
Deakin University in Australia has become the go-to place for local hospitals to discover solutions for their most complex cases and get a glimpse of what a hospital of the future could look like. Leading the front at Deakin’s School of Engineering is Dr. Mazher Iqbal Mohammed, who is working to come up with everything from clinical solutions like tailor-made ear prostheses to science fiction-like technology such as a mask that can minimize radiation dosages in radiotherapy treatments. He says the thing to look out for next is automating the process to make so-called “just-in-time solutions” and to add other technologies into the mix – from sensors and electronics to AI and machine learning.