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CurveBeam Connect: Harnessing the Data from Medical Imaging to Supercharge Orthopedics

Orthopedic surgeries have progressed advancements since the first robotic-assisted surgery in medicine 36 years ago. Dr. Stefano Bini, knee arthroplasty expert and academic at University of California San Francisco (UCSF), discussed these advancements with Host Vinti Singh.   The medical industry is seeing more interest in artificial intelligence (AI) to identify outcomes and risk factors….

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Orthopedic surgeries have progressed advancements since the first robotic-assisted surgery in medicine 36 years ago. Dr. Stefano Bini, knee arthroplasty expert and academic at University of California San Francisco (UCSF), discussed these advancements with Host Vinti Singh.

The medical industry is seeing more interest in artificial intelligence (AI) to identify outcomes and risk factors. Another trend is hospital management using technology for administrative processes such as intake, referral management and patient engagement. Additionally, robotics continues to dominate the operating room as technological variability decreases and raises the return on investment.

Of these technological advancements, orthopedics is the number two medical discipline leveraging 3D printing to visualize tissue complexity. 3D holograms are even used via phones or iPads so surgeons can manipulate the model with improved accessibility.

However, not all physicians are running to adopt these trends. Dr. Bini explained that resistance will eventually ease. “When the technology is presented in such a way that it clearly solves a pain point, and it does so at a price point and at an adoption point that is manageable for that particular person in the environment, then it tends to be adopted rather quickly,” he stated.

Data segmentation, the process of segmenting bones and other features medical images to enable advanced analysis, is also advancing.

Machine learning algorithms are becoming stronger as more data sets appear. “We’re going to move forward to an algorithmically driven segmentation that is getting better and better. I’d say you will see clinical applications over the next two to three years,” Dr. Bini said. Adoption of synthetic data, which are sets statistically equal to but different from the original set, eliminate history of present illness issues and will drive AI modelling accuracy.

For more information on these trends, subscribe to the podcast on Apple iTunes or Spotify and visit the curvebeam.com website for imaging solutions and AI insight.

Visit docsf.health for more information on the Digital Orthopaedics Conferences in San Francisco.

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