Vysioneer Receives FDA Clearance for its tumor auto-contouring solution

Vysioneer, the leader in Artificial Intelligence (AI) for cancer care, received FDA clearance for VBrain, the first-ever AI-powered tumor auto-contouring solution. VBrain has been integrated through Vysioneer’s partnership with Mary Bird Perkins Cancer Center, a cancer care organization headquartered in Baton Rouge, Louisiana. 


VBrain is a game-changer for radiotherapy treatment planning, enabling a quicker response time for performing radiation therapy with more precision in targeting the tumor. It also identifies additional lesions that may be missed by the human eye. With the help of VBrain contouring can be completed within few minutes. 


VBrain was tested at multiple sites throughout the U.S. and Taiwan before FDA approval. The solution is cleared to apply auto-contouring to the three most common types of brain tumors: brain metastasis, meningioma and acoustic neuroma. Additionally, the solution was extensively and rigorously tested through an 18-month clinical integration at National Taiwan University Hospital (NTUH), a leader in medical care for the country, with results published in the world’s leading medical journal, Neuro-Oncology.


Dr. Jason Chia-Hsien Cheng, FASTRO, Former Director of Radiation Oncology, NTUH, said: “There were distinct accuracy and efficiency improvements for clinicians of all skill levels. VBrain has a unique opportunity to influence future treatment on a global scale as cloud-based software. Clinicians around the world, including areas lacking in resources, could utilize VBrain to achieve the world-class standard of contouring.”


Jen-Tang Lu, CEO, Vysioneer, said: “I am thrilled to bring VBrain to our partners across the U.S. and Taiwan. Receiving unique FDA clearance for this solution allows Vysioneer to further its commitment to transforming radiotherapy workflows through developing full-body auto-contouring solutions. The future of AI is near, bringing the second set of eyes and hands to assist clinicians in analyzing and segmenting medical scans and further improving patient cancer care.”

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