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> 95% accuracy *
*These data are based on the control group screening tests that included 400 patients. Groups for screening tests conducting were randomly formed from verified images not used in neural network training. Each image was re-read by radiologists.
The system detects malignant and benign neoplasms, calcium deposits, lymph nodes, fibrocystic breast changes, dense breast tissue (ACR). It interprets BI-RADS analysis results.
93% accuracy *
*These data are based on the control group screening tests that included 347 patients. Groups for screening tests conducting were randomly formed from verified images not used in neural network training. Each image was re-read by radiologists.
The system analyses photofluorographies to detect the presence or absence of pathologies.
The system analyzes lung CT scans and detect signs of coronavirus 2 (SARS-COV-2).
The system analyzes histological examinations of breast tissue samples and defines the presence of abnormal cells.
Improving the diagnostic process minimasing
Reducing human error
Shortening the time to diagnosis
Standardization of radiological services work in regions
We use various integration methods for users and customers convenience.
To operate the system there is no need in purchasing any additional equipment or conducting long-term staff training.
Pilot launches and trial operation
Kaluga, Tver, Bryansk, Kursk, Nizhny Novgorod, Murmansk, Ivanovo, Tambov, Kaliningrad regions, Republic of Dagestan, Kabardino-Balkarian Republic, Moscow, St. Petersburg
Research Cooperation MemorandumIt was signed on June 24, 2019 for the development and use of artificial intelligence by medical organizations. Joined
Nenets autonomous district
Republic of Kalmykia
Republic of Adygea
Republic of Bashkortostan
Republic of Komi
Republic of Dagestan