EP070/#527 Evaluation of the constructed multilayer scanner for LBC (liquid base cytology) along with software containing artificial intelligence algorithms enabling remote support for the diagnosis process

ObjectivesCervical cancer mortality in Poland is high. Since the COVID pandemic lasts, access to diagnostics and diagnosticians has been severely limited. To deal with this problem, a complete system was built and implemented (multilayer LBC sample scanner and software), which enabled quick and accurate diagnostics. Due to limitations in the availability of diagnosticians, a support system during diagnostics based on artificial intelligence algorithms was launched, as well as the possibility of remote viewing for many diagnosticians for the same multi-layer scans of samples. It is possible to access the history of the patient. The diagnostics and software indications are marked on the scan. However, the final diagnosis is always made by cyto-screeners.MethodsThe software uses the artificial neural network (U-NET architecture) designed to recognize suspicious regions and neural network (VGG) allowing to determine the type of disorder in line with Bethesda classification. A machine learning element (fuzzy K-Means) was added – responsible for the fusion of the patient’s history (eg. addictions, diseases) with the neural network system results..Results839 (LBC) samples were evaluated by cyto-sreeners. Cytological abnormalities were found in 122 (14.5%) cases. Selected samples with diagnosed abnormality were a model to teach the artificial intelligence algorithms. Preliminary results obtained with use of U-NET ,VGG and fuzzy k-Means so far indicate 93–97% compliance with results obtained using standard methods.ConclusionsFurther refinement of neural networks is necessary to reduce the number of false positives and false negatives. A study with a larger sample size is required to evaluate the software.