Medical Device and Healthcare

AOT: Zühlke enables surgical robot to 'see'

Discover how a modern data platform, machine learning, and deep domain expertise gave 'sight' to a pioneering surgical robot, enabling a new approach to bone cutting surgeries.

CARLO is a surgical robot designed by Swiss company Advanced Osteotomy Tools (AOT). It uses a revolutionary non-contact cold laser ablation procedure to cut and reshape bone in osteotomy surgery. This pioneering technique reduces the surgical workload, cuts the risk of infection, and makes possible new approaches to treatment, including the use of implants.

AOT seeks FDA approval & 'eyes' for its robot

For the technique to succeed, AOT needed to teach its robo surgeon CARLO to 'see'. What's more, the firm wanted to gain FDA approval to market its device in the US. As such, the business required a partner that could combine strategy and regulatory experience with deep domain expertise and extensive capabilities in data engineering, machine learning, and software engineering.

Zühlke was the natural choice. Together, our teams and AOT developed a medical machine learning process. Building on this, we implemented and validated a legally compliant data platform for data collection and developing medical machine learning models.

Lastly, the team implemented an initial image recognition application, for which the new platform proved invaluable. This initial proof of concept involved enabling the robot to use optical coherence tomography to orient itself and precisely locate the bone and cutting sites – in other words, giving the surgical robot 'eyes'.
 

Pioneering data platform lays the foundations for growth & innovation

With our help, AOT secured a major milestone on the path towards commercialisation. The legally compliant data platform we developed paved the way for FDA approval and enabled the business to rapidly develop further medical software applications for a wide range of applications. In giving 'sight' to its pioneering robo surgeon, we demonstrated how the device could transform osteotomy and therapies, improving outcomes for AOT and patients alike.

Contact person for Switzerland

Dr. Gabriel Krummenacher

Head of Data Science

Gabriel Krummenacher leads the Data Science Team at Zühlke and has several years of experience in conducting data analytics and machine learning projects. His main focus is on medical machine learning applications and bringing prototypes to production. He holds a PhD and M.Sc. from the Institute for Machine Learning at ETH, where he worked on scalable methods for large-scale and robust learning, wheel defect detection and sleep stage prediction with deep learning.

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