RADIOLOGY Module

Plentiful

Visual analysis can extract only about 10% of the information contained in a digital medical image. In contrary, Radiology Module converts the digital image into a huge quantity of data. To clarify, images are computed with specific mathematical algorithms. Extracted informations could not be processed by human eye in the same time. Moreover, the evaluated features describe imaging parameters as intensity, shape, size, volume, textures, etc. In other words, extracting an enormous wealth of quantitative data from medical images surpass humans possibilities.

Objective

In the first place, Radiology Module provides trained radiologists with pre-screened images and identified features. Not only data-driven approach allows for more abstract feature definitions but also makes it more informative and generalizable. Above all, diagnoses are more accurate thanks to built-in algorithms. In addittion, using algorithms reduce the risk of subjective description of tumour. Moreover, decision supported by AI can be shared with other specialists to resolve any doubts. To sum up, diagnoses are double checked, more precise and less subjective.

Personalized

As has been noted, Radiology Module can be used to investigate numerous characteristics. Similarly, tumours variability provides valuable information on both the aggressiveness and therapeutic response. Definitely, the more data extracted from different spatial and temporal levels, the more accurate diagnostic and prognostic models. Thanks to that, specialists can predict the right treatment, for the right patient, at the right time. Certainly, the future of patient care is dependent on artificial intelligence.

Reproducible

Radiology Module extracts qualitative and quantitative characteristics out of medical images. After that, experts provide details on size, location and morphology using built-in features. Meanwhile, complete confidentiality is ensured throughout the entire process. In other words, all extracted data are stored, viewed, shared and evaluated without data sharing and privacy concerns. Therefore, doctor to doctor cooperation within the organization as well as outside is highely improved.