Authors:

Jacek Gronwald1, Lukasz Lasyk2, Jakub Barbasz2,4, Pawel Zuk3, Artur Prusaczyk3, Tomasz Włodarczyk2,3, Ewa Prokurat3, Wlodzimierz Tadeusz Olszewski5, Mariusz Bidziński5

1Pomeranian Medical University, Szczecin
2Digitmed Sp. z o.o.
3Centrum Medyczno-Diagnostyczne Sp. z o.o.
4Institute of Catalysis and Surface Chemistry Polish Academy of Sciences, Cracow
5Maria Skłodowska-Curie Institute of Oncology, Warsaw, Poland

Research Funding:

The National Center for Research and Development (Poland)

Background:

The incidence and mortality of cervical cancer are high in Poland. There are effective methods of the prevention and the early diagnosis, however they require well-trained medical professionals including cytologists. Within this project we built a prototype of a new device together with implemented software to convert the currently used optical microscopes to fully independent scanning systems for cytological samples. The use of the device is intended to improve the effectiveness of cytological screening, and registration of cytological tests’ results. The features of the software include digital backup as well as transmission and telemedicine evaluation.

Methods:

The software uses the artificial neural network (U-NET architecture) designed to be able to recognize suspicious regions and enhanced CNN neural network (VGG) allowing to determine the type of disorder such as: ASCUS, ASC-H, HIS, AGC, cancer. 7128 liquid based (LBC) and 1700 conventional cytology samples were evaluated by trained cyto-sreeners. Cytological abnormalities like: ASCUS, ASC-H, HIS, AGC, cancer were found in 254 (3.6%) LBC cases and 51 (3.0%) conventional cytology cases. All samples were scanned and archived. Selected samples with diagnosed abnormality were a model to teach the artificial neural networks.

Results:

Preliminary results obtained with use of U-NET and VGG (CNN networks) so far indicate 90-96% (LBC samples) and 85-95% (conventional cytology) compliance with results obtained using standard methods.

Conclusions:

Further 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. This project is co-financed with European Regional Development Fund within the Priority Axis I, Support for R&D activities for companies, Measure 1.2, Sectoral R&D Programmes, Sectoral Programme: “INNOMED – scientific research and development programme for innovative medicine economy sector”.

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