Focus on AI: Machine Learning in Focused Ultrasound Acceleration Program

Poised for Transformation

Machine learning (ML), a a branch of artificial intelligence (AI), is transforming numerous aspects of healthcare delivery, many of which have direct correlation to or significant implications for the field of focused ultrasound. As ML is poised to transform focused ultrasound patient treatment, the Foundation is uniquely positioned to strategically accelerate the use of ML in focused ultrasound by convening the community and providing resources to those in the field.

The Foundation is committed to increasing awareness of ML’s potential to optimize treatment outcomes and drive productive ML deployment within the focused ultrasound community. We have dedicated resources to achieve this goal in the following areas:

  • Expand dialogue: Initiate discussions around use cases, possibilities, and techniques within the focused ultrasound and data science communities and beyond to broaden awareness of ML potential.
  • Create communities: Convene researchers and manufacturers in the field around common interests to directly advance current research through shared methodologies and lessons learned.
  • Ensure resources: Provide a robust catalog of information, access to experience and expertise to facilitate research, and dedicate financial support to initiatives that impact patient lives.
Rick Hamilton, CTO, introduces the Foundation’s FUS-ML Acceleration Program

Learn More About the Implications for ML in Focused Ultrasound

Narrowing Our Focus

In recent years, we’ve seen AI/ML-driven breakthroughs in diverse health-related areas, including antibiotic discoveries, the protein folding problem, improvements in radiological interpretation, and many others. We know that ML has the potential to advance therapies, improve efficiencies, enhance overall treatment quality, and make a real and positive difference in patients’ lives.

For focused ultrasound practitioners to begin considering and applying data science–based approaches in the field, we need to develop knowledge of how AI and ML are being used in and positively impacting healthcare, particularly in focused ultrasound–adjacent areas. While the Foundation is aggregating data and creating a broad map of the potential focused ultrasound and ML (FUS-ML) landscape, we have created a framework for considering ML’s strategic impact on focused ultrasound, both within the treatment lifecycle and by indication and/or region.

Areas of Impact

Treatment lifecycle:

  • Patient selection
  • Treatment planning
  • Treatment monitoring & results analysis


  • Neurological: Deep brain structures
  • Neurological: Blood-brain barrier opening
  • Urological: Prostate
  • Gynecological (potential sub-indications, e.g., uterine fibroids, etc.)
  • Veterinary
  • Emerging indications

*This list is not exhaustive and will grow and evolve according to ecosystem needs.

Join the Initiative

In its role as the nexus of the focused ultrasound community, the Foundation is perfectly poised to convene interested parties around ML in focused ultrasound research, experimentation, and adoption. We’ve laid out several pathways to inform, engage, and connect not just those in the community, but those focused in data science, machine learning, and beyond. By joining our FUS-ML Community of Practice, you’ll be helping to advance technologies that will transform future therapies, improve patient experiences and outcomes, and, ultimately, save lives.

  • Join our FUS-ML Community of Practice. Receive regular updates on the state of ML in focused ultrasound, developments and advances, and emerging opportunities within the field. Collaborate with other practitioners in the community to advance common interests and share best practices.
  • Contact our team directly. Share your thoughts on accelerating ML in the field and where there are additional opportunities for collaboration.
  • Register for the FUS-ML online forum. Connect with the broader community in an online forum, allowing for asynchronous collaboration and discussion.