Value Proposition:
· Provides a realistic, low-risk training alternative to patient-based neurosurgery education, ultimately improving patient safety and clinical outcomes.
· Offers a flexible, patient-specific training platform that can replicate both normal and pathological anatomy using CT scan data.
· Features an optical measurement system that provides precise, quantitative data on a surgeon’s targeting accuracy during procedures.
Technology Description
· Researchers at Johns Hopkins have developed a smart, anatomically adaptable model that addresses the need for objective, customizable neurosurgical training and evaluation. The system detects the location of surgical instruments and measures their distance to predefined targets with high accuracy, collecting data to objectively evaluate surgical performance. Realistic procedural conditions are simulated through a gel which mimics the consistency of brain tissue and the system’s ability to represent both normal and abnormal anatomy.
Unmet Need
· There is a critical need in neurosurgical training for tools that support objective and repeatable skill evaluation in a controlled low risk setting. Traditional training relies on cadaver labs, static anatomical models, and supervised procedures on patients. These approaches can negatively impact patients, are not easily repeatable, and lack the ability to provide quantitative feedback. Therefore, there is a strong need for a neurosurgical training system to be developed that provides realistic, repeatable, and data-driven evaluations of surgical technique for various patient anatomies.
Stage of Development
· Prototyping and proof of concept experiments have been completed.
Data Availability
· Data available upon request
Publication
n/a
