Stem Tronics is a robotic-assisted, minimally invasive surgery pioneer and technological leader. The Stem Tronics Vital™ Surgical System is designed, manufactured, and sold by Stem Tronics. The company aims to make surgery more efficient, less invasive, and less distressing for doctors, patients, and their families.

The Shift in Perspective Continues

Stem Tronics Vital™, our Robotic Surgery System, continues to restructure treatments and substantially enhance adaptability.

Additional Information

Stem Tronics is currently developing and testing a variety of next-generation delivery devices that show stability in infusion patterns, resulting in consistent efficacy of treatment.

Stem Tronics offers a consultancy and associated design service to allow biotechnology and pharmaceutical companies to precisely specify delivery systems for optimal delivery of their therapeutics, in addition to incorporating this development portfolio into therapeutic and academic research programs.



Stem Tronics is developing implantable therapeutic delivery devices and operating room surgery robotics in conjunction with major pharmaceutical and biotechnology companies for the treatment of all major diseases that can be cured through surgery.

Increasing the Precision of Therapeutic Delivery

Stem Tronics is presently collaborating with major biotechnology and pharmaceutical companies to ensure that treatments that have shown promise in the lab may be delivered in clinical trials.

The complications of completing this step have historically resulted in high-potential therapy programs overcoming challenges while aiming to meet their goals.


Technology Research Grants

The following are some of the technology research subjects that Stem Tronics is interested in:

  • Knowledge-Based Planning.
  • How to make the model generation process simpler and partially or totally automated.
  • Development of knowledge-based decision support.
  • Continuous quality improvement based on knowledge development. A model, we believe, is a depiction of a practice at a specific point in time. Could models developed at different times be used to evaluate the efficacy of quality-improvement initiatives?
  • Can sharing of models facilitate the adoption of best practices?
  • Can models be used in clinical trials to improve plan consistency? Can models be used to ensure the quality of planning in clinical trials?
  • Distributed Learning.
    • How to build a distributed learning system that takes in information from all patients and updates itself on a regular basis.
    • Concepts for oncology are being developed in order to facilitate data comparisons and increase data quality.
    • How to keep data quality in a dispersed learning context.
  • Application of a distributed learning system to answer clinical questions.



Predictive analytics in oncology is being developed, including the use of imaging for predictive analytics.

  • How to enhance cyber security on premise in the clinic, in the cloud, and in hybrid systems.
  • How to make use of emerging "big data" sources.
  • Advanced Treatment Planning.
  • What clinical cases will benefit the most from online adaptive planning; how to conduct online adaptive planning.
  • New treatment approaches that improve dose compactness and conformity.
  • Development of novel picture segmentation algorithms, such as probabilistic-based models that take shape, texture, and other factors into account.
  • New optimization engines that are being developed, with the goal of improving plan compliance and robustness.
  • Imaging.
  • How to improve intrafraction imaging of soft tissues.
  • How to identify 3D location of implanted fiducials using a single 2D image.
  • Development of tumor tracking without the use of biomarkers.
  • Development of techniques for planar and volumetric intra-fraction imaging at non-zero couch angles.
  • Development of digital tomosynthesis.
  • The future of in room ultrasound.
  • The future of in room functional imaging.
  • How to incorporate radiomics into a routine process to assess treatment response and maybe alter therapy.
  • Development of image-based tumor analytics.
  • How to enhance visualization techniques in bioinformatics.
  • Quality Assurance and Safety.
  • Analysis of machine log files to predict failures before they occur.
  • Simplifying and automating routine QA processes.
  • Improving safety throughout the radiotherapy process.
  • Workflow.
  • Usage of biosensors and wearable devices in the management of oncology patients.
  • How to employ gamification and other strategies to encourage patient involvement, such as improving treatment regimen compliance during treatment, increasing engagement with educational materials and survivorship, and increasing long-term follow-up participation.