More information
Description
Synaptiq Technologies is based in Cluj-Napoca, Romania and was created in 2021. They specialize in software development for the MedTech business utilizing artificial intelligence. The company''s core business is developing, and marketing tumor delineation software based on well-done research in this area to radiology centers. Conducting research, software development, and product management are the major tasks.
The main objective of the project is to develop an innovative artificial intelligence SaaS solution for efficient management and delivery of radiotherapy to oncology patients. The background of this solution is due to the scarcity of radiologists on the market which is also amplified by the fact that contouring medical pictures takes a long time. The project solution will help radiologists see more patients by cutting the time it takes them to outline medical pictures of patients from several hours to a few minutes. The ambition is that the project will lead to more patients getting the treatment they need. The project is implemented in partnership with International Development Norway (IDN). IDN will provide technical and user assistance to Synaptiq Technologies during the product development process. IDN will perform various other tasks like set up the technical requirements, collect testing data and analyze and design a feasible business model for the Norwegian market. The partnership will also enable Synaptiq Technologies to make an international relationship and expand more efficiently.
The extended project aim is to improve the efficiency of radiotherapy treatment planning, reduce the burden on medical staff, increase patient accessibility to treatment, and ultimately contribute to a quicker and more effective cancer treatment process. The project aims to automate and streamline the contouring of organs-at-risk, making the software a central solution for radiotherapy planning with a vision for same-day treatment planning in the future.
Summary of project results
The project aimed to address several key issues and challenges in the field of radiotherapy treatment planning. One of the primary challenges was the scarcity of radiologists, which is further exacerbated by the time-consuming nature of contouring medical images. This process typically takes several hours, limiting the number of patients that radiologists can attend to. The project sought to develop an innovative AI SaaS solution to automate and streamline this process, reducing the time required to contour medical images from hours to just a few minutes.
Another significant challenge was the potential low clinical interest in adopting the final product. To mitigate this, the project focused on designing a user-friendly product and clearly presenting its efficiency benefits. Additionally, there was a concern about the sufficiency of data for AI training. The project addressed this by leveraging the team''s expertise and existing partnerships with clinics to test the solution.
The project also aimed to overcome the skepticism of medical staff towards computer-aided solutions by ensuring the software was intuitive and adaptable to their needs. Furthermore, the project aimed to address the bottleneck in the contouring task, which requires significant attention and energy from doctors, by providing a solution that allows them to process more patients and focus on critical aspects of treatment planning.
The project undertook several key activities aimed at developing an innovative AI SaaS solution for efficient radiotherapy treatment planning. Here is a detailed description of the activities and outputs:
The project focused on developing a software architecture capable of analyzing medical images with accuracy comparable to manual analysis. This involved collecting clinical data, dividing it into training, validation, and test subsets, and evaluating the model. A minimum user interface was developed to allow medical doctors to interact with the software. The team conducted qualitative and quantitative analyses of the technology''s performance, modified the user interface based on feedback, and integrated the software with existing clinical systems. The final step was to demonstrate the prototype in a fully operational clinical pipeline and obtain medical accreditation.
he outputs of the project included the development of an innovative AI SaaS solution for efficient management and delivery of oncology radiotherapy. This solution was capable of performing auto-contouring for more than 150 organs-at-risk and lymph nodes in critical regions such as the head and neck, thorax, and pelvis. The project also resulted in the creation of high-performance and high-accuracy AI and machine learning algorithms that formed the basis of the software platform.
The project successfully developed a fully functional AI SaaS solution capable of performing auto-contouring for more than 150 organs-at-risk and lymph nodes in critical regions such as the head and neck, thorax, and pelvis. This innovative solution significantly reduced the time required for contouring medical images from several hours to just a few minutes, thereby increasing the efficiency of radiotherapy treatment planning.
The high-performance and high-accuracy AI and machine learning algorithms developed during the project formed the foundation of the software platform, ensuring precise and reliable contouring results. This advancement had a direct impact on radiologists, enabling them to attend to more patients and focus on critical aspects of treatment planning, ultimately improving patient care.
The project also contributed to the creation of new jobs, with an estimated five new positions being generated. Additionally, the project led to the submission of new Intellectual Property Rights (IPR) applications to protect the innovative features and technical concepts of the ONCOCARE software.
The outcomes of the project included an estimated annual growth in turnover of 591% and an estimated annual growth in net operational profit (EBIT) of 296% by the end of 2024 compared to the baseline year 2021.
Summary of bilateral results
The project greatly benefited from having a donor partner by leveraging their expertise and resources. The donor partner provided valuable insights and support in setting up the piloting and testing scenarios, ensuring that the prototype was demonstrated in a fully operational clinical pipeline in Norway. Their involvement helped in establishing the technical requirements for piloting and testing at Rikshostpitalest, which was crucial for the project''s success. Additionally, the donor partner''s experience in the Norwegian market was instrumental in analyzing and designing a feasible business model, ensuring that the solution was compatible and efficient for Norwegian hospitals. At the bilateral level, the project achieved several significant results. The collaboration with the donor partner facilitated the successful demonstration of the prototype in a fully operational clinical pipeline in Norway. This included setting up the technical requirements and defining the testing scenario, which provided valuable data and input from medical personnel. The project also resulted in a comprehensive analysis of the software''s feasibility in the Norwegian market, ensuring that it was compatible and efficient for use in Norwegian hospitals. This bilateral cooperation led to a deeper understanding of the market needs and helped in refining the solution to meet those needs effectively. The project team plans to continue the bilateral cooperation with the donor partner to further enhance the solution and expand its reach. Future collaboration will focus on continuous improvement of the AI and system architecture, as well as exploring additional piloting and testing opportunities in other clinical settings.