Publications by authors named "A V Proskura"
Aim: To evaluate the automated medical decision support system "Sechenov.AI_nephro" in the treatment of patients with renal parenchymal tumors.
Materials And Methods: The beta version of the web-platform "Sechenov.
Article Synopsis
- The study aims to evaluate if machine learning can effectively predict early outcomes of laparoscopic nephron-sparing surgery (NSS) for kidney tumors, taking the surgeons' experience into account.
- Analyzing data from 320 surgery cases by four surgeons, the research utilizes eXtreme Gradient Boosting and the SHAP method to identify key factors influencing surgical success, including patient demographics, tumor characteristics, and the surgical learning curve.
- Results show that the SHAP method offers valuable insights from the machine learning model, highlighting the importance of certain new features in predicting surgical outcomes such as procedure duration and postoperative kidney function.
Aroma is among of the most important criteria that indicate the quality of food and beverage products. Aroma compounds can be found as free molecules or glycosides. Notably, a significant portion of aroma precursors accumulates in numerous food products as nonvolatile and flavorless glycoconjugates, termed glycosidic aroma precursors.
View Article and Find Full Text PDFAim: To evaluate the possibilities of textural analysis of 3D models in differentiating the degree of nuclear dysplasia of the clear cell renal cell carcinoma (ccRCC).
Materials And Methods: The specimens after surgical treatment of 190 patients with ccRCC were analyzed. In all cases, nephron-sparing surgery (NSS) was performed through laparoscopic access.