RSIP Vision’s automated detection and tracking of tumors answers one of the most frequent needs in the pharma industry – assessment of patient response to a new drug. This very strong tool saves precious time in the clinical stage when the automated detection and tracking can supply the most accurate measurement in an efficient way. The protocol might require some manual review of the radiologist – though this intervention will be much faster than a fully manual expert annotation.
The immediate advantage of RSIP Vision’s tool is that it presents each target tumor with the right measurement along the treatment period. In this method, the radiologist is only expected to monitor and approve the AI software’s finding.
Since this is not diagnostic for a specific patient and the purpose of the study is to evaluate the response to the tested drug – there is no risk of false negatives. Even in the hypothesis that the system skips one of the tumors in one scan – it still will be able to fix it when detection is done in the next CT – with no harm to any patient.
The AI software is taught on a training database to distinguish between benign nodules and malignant tumors following shape and color characteristics, just like a human annotator would do. Once trained, the software’s classifier distinguishes between nodules and tumors with state-of-the-art accuracy and without any human intervention.
RSIP Vision’s methodology is built on world-class Deep Learning algorithms, which are recognized by our industry and by academic researchers as the best AI technique to obtain optimum accuracy as fast as modern computers allow it, with no bottleneck due to human expert shortage. No external intervention is needed.
Automated detection and tracking of tumors
RSIP Vision’s detection and tracking of tumors is based on convolutional neural networks, the advantages of which are numerous: speed, full automation, no inter and intra difference on all the dataset: the simplest and most effective answer to all kind of problems in medical segmentation. Its robustness and efficiency have already been successfully tested by RSIP Vision’s clients.