Author: Raghavendra Raghavendra, Kanaparthy Raja Muralidhar, Venkataramanan Ramachandran, Srinivas Srinivas 👨🔬
Affiliation: Karkinos Healthcare 🌍
Purpose: This study explores the Integrating AI-Generated Auto Contours via Ray Station’s Deep Learning Segmentation into Diverse Treatment Planning Systems.
Methods: The research encompassed a group of hospitals comprising five facilities equipped with Eclipse V16.1 (Varian Medical Systems, USA) and Monaco V6.1.2 (Elekta Medical Systems, Crawley, UK) treatment planning systems, distributed across various locations in India. Additionally, a central planning system utilizing Ray Station V13.1(Ray search Laboratories, Sweden) was deployed at a distinct location. Simulated CT images for radiation oncology (RO) planning were transmitted to the cloud from various locations and subsequently imported into the Ray Station platform that is located in the cloud. Auto contours were then generated on these CT images and exported back to the respective TPS via cloud connectivity.
Results: The OAR contours generated through deep learning segmentation in Ray Station were seamlessly transferred to both Monaco and Eclipse TPS via cloud connectivity. Analysis revealed that most of the contours were deemed perfect and utilized in clinical planning. The study analyzed over 1000 cases across these five units, encompassing various diagnoses, to assess the efficacy of this approach.
Conclusion: This study demonstrates its versatility by seamlessly integrating into other planning systems. The efficiency gains realized through this tool not only translate to significant time savings but also ensure uniformity of contours across all our units. This consistency fosters enhanced quality in treatment planning, improved patient care especially in developing countries where the budget for dedicated treatment planning systems are not adequate.