We discuss a simple method for automatic recognition of pulmonary emphysema in three-dimensional computer tomography (CT) images. This technique allows one to quantify the disease progress, calculating some numerical characteristics, such as the percentage of the lung tissue affected, as well as visualizing its location and intensity histogram in the region of interest. An experiment on the test data shows that the recognition error is not higher than 7.5%.

In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation -U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists.

The possibility of application of different textural features for the lung disease automatic diagnosis on the basis of the 2D digital computed tomography (CT) images was studied. Histogram features, covariance features, Haralick’s features and run length features were used. A procedure based on the discriminant analysis criterion was used for the selection of the best features group. We experimentally showed that the approach offered is convenient to use for solving the problem of automatic diagnosis on a 160-image set received during examination of patients with a chronic obstructive pulmonary disease. The resulting group of effective features includes two Haralick’s features and three run length features, providing the error rate of 0.11, which is better than similar results obtained without a feature selection procedure.

DPCAR’s short- and long-term outcomes are highly diverse, while the causes and prevention of ischemic complications are unclear. To assess oncological, surgical, and hemodynamic outcomes of 40 consecutive DPCARs for pancreatic (n37) and gastric tumors (n3) (2009–2021), retrospective analyses of mortality, morbidity, survival, and hemodynamic consequences after DPCAR were undertaken using case history data, IOUS, and pre- and postoperative CT measurements. In postoperative complications (42.5%), the pancreatic fistula was the most frequent event (27%), 90-day mortality was 7.5. With 27 months median follow-up, median overall (OS) and progression-free survival (PFS) for PDAC were 29 and 18 months, respectively; with 1-, 3-, and 5-years, the OS were 90, 60, and 28%, with an R0-resection rate of 92.5%. Liver and gastric ischemia developed in 0 and 5 (12.5%) cases. Comparison of clinical and vascular geometry data revealed fast adaptation of collateral circulation, insignificant changes in proper hepatic artery diameter, and high risk of ischemic gastropathy if the preoperative diameter of pancreaticoduodenal artery was <2 mm. DP CAR can be performed with acceptable morbidity and survival. OS and RFS in this super-selective cohort were compared to those for resectable cancer. The changes in the postoperative arterial geometry could explain the causes of ischemic complications and determine directions for their prevention.

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