Introduction . Artificial intelligence (AI) is an effective tool for automating routine tasks in radiology. The diagnostic accuracy of AI in detecting various pathologies on medical images has generated considerable interest in the scientific community: the number of studies and meta-analyses has been constantly growing. The abundance of published evidence and the diversity of outcomes necessitate the need to systematize the available publications. The aim of this paper is to conduct an umbrella systematic review of contemporary meta-analyses on the use of AI in radiology. Materials and methods. PubMed was searched for studies published in the English language. Thirty-eight systematic reviews with meta-analyses published between 2021 and 2023 were selected for full-text analysis. The extracted data included the goal, study design, imaging modality, sample size, quality assessment of the included studies, AI diagnostic accuracy estimates, reference method parameters, and clinical efficacy metrics of AI implementation. The methodological quality of included systematic reviews was assessed using the AMSTAR-2 tool. Results. Nearly half (47%) of the included meta-analyses focused on the diagnosis, staging and segmentation of malignancies. Four meta-analyses were related to detection of maxillofacial structures in dentistry, while another four meta-analyses addressed the diagnosis of brain lesions. The diagnosis of COVID-19 and the diagnosis of bone fractures were each covered in three meta-analyses. One meta-analysis was reviewed for each of the following fields: colorectal polyps, pneumothorax, pulmonary embolism, osteoporosis, aneurysms, multiple sclerosis, acute cerebrovascular accident, intracranial hemorrhage, burns, and the risk of intrauterine growth restriction. Thirty-five (92%) meta-analyses assessed the risk of bias. Twenty-eight (80%) meta-analyses utilized QUADAS-2 to assess the risk of bias. 14 out of 28 papers reported low risk of bias (50%); 4 (14%) – moderate; 10 (36%) – high. The major risks were associated with samples that were unbalanced in terms of size and composition, a lack of details about the methods, a low number of prospective studies, and a lack of external validation of the outcomes. The overall results indicate that the diagnostic accuracy of AI is comparable to or even greater than that of radiologists. The mean sensitivity, specificity and area under the ROC curve for AI and radiologists were 85.2%, 89.5%, 93.5% and 84.4%, 90.0%, 92.8%, respectively. However, many studies that compared the diagnostic accuracy of AI and radiologists lack the data on the number and experience of the latter. Only one paper presented results of implementing AI into routine clinical diagnosis. Discussion. AI is capable of reducing the turnaround time for non-urgent examinations. When used to verify the primary interpretation, AI was effective in detecting false-negative results from radiologists. However, the efficacy of detecting false-positive results was inadequate. Our assessment of the quality of systematic reviews with AMSTAR-2 show that the methods of searching, selecting and analyzing literature must be improved and brought to a common standard. The development of a specialized tool for assessing the quality of systematic reviews in the AI implementation is also necessary. Due to high diagnostic accuracy, AI is currently considered a promising tool for optimizing the turnaround time. However, more evidence is needed to study the AI outcomes in routine clinical practice. Furthermore, it is necessary to standardize and improve the quality of research methodology.

The Midday jird Meriones meridianus sensu lato is a widely distributed and highly variable species complex, whose taxonomy is still controversial despite several genetic and morphological studies. Dzungaria is a crucial region for understanding the phylogeny and phylogeography of this species complex. In this work, the genetic variation of M. meridianus in Dzungaria and adjacent areas was studied based on mitochondrial (Cytb) and nuclear (BRCA1 and IRBP) genes. To compare morphological and genetic differentiation among lineages, craniological variation of the genotyped specimens was examined. Moreover, environmental data were used to estimate the most probable dispersal routes of M. meridianus clades in the studied area. Three clades of M. meridianus with interspecific level of differentiation were found and treated as distinct species: M. psammophilus (Milne‐Edwards, 1871); M. meridianus (Pallas, 1773); and M. penicilliger (Heptner, 1933). The distribution ranges of all three species overlap in Dzungaria. According to the molecular data, M. psammophilus and M. meridianus are sister taxa that diverged ca. 500 kya, while M. penicilliger separated from the common ancestor of the other two lineages ca. 800 kya. These three species have significantly different cranial measurements. Moreover, traces of hybridization between M. psammophilus and M. meridianus were detected, but not between M. penicilliger and the other two species. Finally, the dispersal scenarios for M. psammophilus, M. meridianus, and M. penicilliger are herein discussed. It is suggested that M. meridianus and M. penicilliger independently colonized Dzungaria from the west via Dzungarian gates.

Populations of Arctic fox (Vulpes lagopus) in the Commander Islands, in the Russian Bering Sea, have been isolated since the Pleistocene and differ substantially in their cranial features from their mainland counterpart. Small rodents, the main prey of mainland Arctic foxes, are not found in the Commander Islands, where the main food source for Arctic foxes are large sea birds and marine mammals. Here we assessed whether differences in foraging strategy, particularly the size of available prey, could explain the observed differences in cranial features between mainland and island Arctic foxes. Because a large gape is necessary when foraging on large prey, we compared gape angles between islands and mainland in a sample of dry crania. We found an enlarged gape angle in both island populations. We also compared the rostrum to cranium length ratio and found it to be similar for the mainland and Bering Island Arctic foxes; however, a rostrum contraction was found in the Mednyi Island Arctic foxes. We show that cranial differences between mainland and Commander Islands fox populations could be explained by their different foraging ecology. Furthermore, the relative rostrum contraction in the Mednyi Island foxes provides further evidence for cranial resistance to deformation during biting. These results show the importance that distinct foraging strategies can have in Arctic fox divergent evolution, and, consequently, on future conservation plans for the two Commander Islands subspecies.

Island subspecies of the Arctic fox Vulpes lagopus differ morphologically from the mainland subspecies. In particular, differences in cranial form may reflect varied biomechanical adaptations associated with hunting and feeding behaviours. We tested the hypothesis that the observed cranial differences between two island foxes (living on two North Pacific islands) and those living on the mainland have no impact on biomechanical performance during simulated biting. 3D cranial models of three Arctic fox subspecies were compared based on biomechanical parameters (e.g. local strain and large-scale deformation). Finite elements (FE) analyses were used to simulate equivalent biting loads, and geometric morphometrics was used to compare the modes of deformation among the models. The results showed differences in local strains and modes of global deformation among the three subspecies; the mainland subspecies was particularly distinct from the island subspecies. The representative cranium of the mainland subspecies experienced higher strain than that of both island subspecies during all bites. However, the findings highlight issues that arise when relating biomechanical performance, measured via FE analyses, to the foods consumed rather than to the mechanical properties of the individual’s diet. Additional physical properties data for each prey type are necessary to determine the extent to which the present findings reflect biomechanical adaptations to diet and prey acquisition.

In total, 1406 samples of scat of 19 arctic fox families and 1755 prey remains collected near the dens of 32 families during the cub rearing period were analyzed. This is the first attempt to evaluate the food use in the population according to averaged data for a large number of families. After the population had passed through the bottleneck, the arctic fox diet changed considerably. Colonies of petrels—the northern fulmar Fulmarus glacialis rodgersii and the storm-petrels Oceanodroma furcata and O. leucorhoa—became the main food source, whereas the use of alternative resources (alcids, cormorants, marine invertebrates, and otarid rookery products) decreased considerably. The following factors are assumed to determine the arctic fox foraging strategy, i.e., the selectivity in food acquisition: (1) passing of the population through the bottleneck; (2) termination of anthropogenic influence due to the liquidation of human settlements on the island; (3) decrease in both the bird populations and abundance of marine invertebrates (sea urchins and mussels); and (4) stable low density of the current arctic fox population. A hypothetical scenario for formation of the foraging specialization in the island population is discussed.