Based on artificial intelligence (AI) algorithms from IBM’s Watson Health, Ricoh USA, the U.S. arm of the Japanese digital services and information company, has created a remarkable, must-be-seen-to-be-believed, system that creates 3D-printed physical simulators and models of patient-specific bone and soft tissue for surgeons.
Artificial Intelligence Based Spine Modeling Now Available
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The RICOH 3D system uses existing 2D imaging data then applies the AI algorithms in IBM iConnect Access from IBM Watson Health to create remarkably detailed 3D-printed, patient specific, anatomic models.
“Currently, access to 3D-printed models is limited to very few healthcare organizations, meaning the vast majority of clinicians—and their patients—cannot benefit from this critical tool,” said Gary Turner, managing director, Additive Manufacturing, North America, Ricoh USA, Inc.
“RICOH 3D for Healthcare is a turnkey, cost-effective solution that can drastically expand access to 3D models and allow healthcare providers at any size facility to learn from and inform the overall patient experience.”
“RICOH 3D for Healthcare is the first end-to-end workflow solution with full interoperability with IBM iConnect Access and its direct connection with any picture archiving and communication system (PACS) system on the market,” Gary Turner told OTW.
“This means that for the first time far more hospitals of all sizes can provide their medical staff with access to 3D-printed, patient-specific representations of tissue and bone, which helps clinicians see inside anatomy for greater visibility into patient needs. By reducing common roadblocks including cost and access, we’re bringing 3D-printed replicas to the healthcare provider masses and in turn, allowing them to provide more precise and customized care to their patients.”
Detailing their process, Turner commented to OTW, “Early on in the development of RICOH 3D for Healthcare, we were very encouraged with how impressed a consultant was with the short time and minimal effort it took to complete a segmentation using our workflow. This was a huge barrier to cross as it made us realize that we truly were creating a solution that was easy for medical staff to actually use and that it could be done quickly—a must have for addressing immediate patient needs.”
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This is a fascinating development. In my practice we've seen similar outcomes with the revised protocol. The key differentiator seems to be patient selection criteria. Has anyone else noticed the correlation with BMI thresholds?
Great point. I'd push back slightly on the conclusion, the sample size in the cited study is too small to draw population-level inferences. That said, the directional signal is compelling and worth a larger RCT.
We implemented a similar approach last year. Early results are promising but we're still gathering 12-month follow-up data. Happy to share our protocol if anyone is interested.
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