Pioneering research and development on the TruScreen system for real-time cervical tissue differentiation has involved close collaboration with leading clinicians and hospitals across the world.

Trade Markets
TruScreen holds trade marks and patents that afford the Company certain proprietary protections in respect of its intellectual property rights that it has developed.
Additional research and development is also planned to improve the manufacturing processes to reduce the cost of manufacture of the products to improve gross margins. TruScreen is committed to ongoing product innovation and is consistently conducting research and development projects to ensure the device remains best of breed.
TruScreen's sophisticated algorithm framework
TruScreen’s technology contains a sophisticated algorithm framework that has been developed in collaboration with the Australian Government’s applied research division, CSIRO. The algorithm was researched and developed from data sets collected from the Auckland Hospital, Royal Hospital for Women (Sydney), and The Whittington Hospital (London) between 1998 to 2000.
More than 23 man-years were spent on algorithm development and testing to arrive at the current version of the TruScreen Ultra classifier algorithm. During development, numerous algorithm methodologies were trailed. The current TruScreen Ultra algorithm was selected following trials of 5 competing algorithms.

Clinical Studies
20 Years of Clinical Studies with 42k Accumulated Patients

TruScreen Ultra Algorithm
The TruScreen Ultra algorithm is AI-enabled and uses a trained multidimensional probabilistic tissue features classification engine to provide a binary classification result (Normal or Abnormal) with a high degree of Sensitivity and Specificity. The data sets used to train the algorithm consist of patient-specific clinical data combining clinical diagnosis information from colposcopy, cytology, and histology. The training data set consists of more than 7,500 multi-probing data sets, from patients of varying ethnicities with differing histological diagnoses.