Organ segmentation is an essential step in Radiotherapy. Radiotherapy is one of the medical imaging modalities to treat cancer. We have developed a deep learning based automatic organ segmentation tool that completes organ labelling for CT and MRI scans in seconds instead of hours of manual drawing. Our solution saves time for clinicians and due to the consistency of AI, the quality of this label is better than an average human operator.
Cancer cases are rising and hospitals are overwhelmed by the demand for cancer treatment. Radiotherapy is a very complex process and manual tasks take up a lot of time. The end result is it takes weeks to do a treatment plan. 40% of this time consuming process is the drawing and labelling of organs in the CT scan. With AI, this manual task is getting automated. The end result is the shorter time to do a treatment plan. We are particularly excited to bring this solution to developing countries where this tool can help to treat patients better and faster.
Technology Features, Specifications and Advantages
We use state of the art deep learning based neural networks and we train our models with data collected from hospitals worldwide. AI is a fundamental disruption in this filed. Since organ segmentation is a typical computer vision problem, deep learning which is the current state of the art can be a solution to solve this problem.
The first and foremost application of this solution is to help treat cancer patients faster and more accurately. The solution is deployed in the cloud and can be reached anywhere in the world where there is an internet connection. That means a significant part of the world where there is a lack of skilled clinicians, AI could help to treat patients in those regions. AI has no human factor. For example, the AI model does not get tired or need any sleep or lunch break. It is always consistent and dependable. So ultimately, it’s a tool to help clinicians to automate manual tasks and clinicians could spend more time away from the computer screen and with patients.
Hospitals with cancer patients are our end customers. Cancer cases are growing 6.9% per year so that means that the hospitals are overwhelmed. They can't manage the current patient volume with existing technology. A new approach is needed to address the growing demand for cancer treatment. Our solution has the potential to revolutionize cancer treatment by deploying AI through the cloud. This decreases the cost for such state of the art technology significantly. So the value propositions for the hospitals are significant technical service with a vastly cheaper price.