The aim of this study was to investigate the effect of pre-irradiation with microwaves on the induction of radioadaptive response. In the 1(st) phase of the study, 110 male mice were divided into 8 groups. The animals in these groups were exposed/sham-exposed to microwave, low dose rate gamma or both for 5 days. On day six, the animals were exposed to a lethal dose (LD). In the 2(nd) phase, 30 male rats were divided into 2 groups of 15 animals. The 1(st) group received microwave exposure. The 2(nd) group (controls) received the same LD but there was no treatment before the LD. On day 5, all animals were whole-body irradiated with the LD. Statistically significant differences between the survival rate of the mice only exposed to lethal dose of gamma radiation before irradiation with a lethal dose of gamma radiation with those of the animals pre-exposed to either microwave (p=0.02), low dose rate gamma (p=0.001) or both of these physical adapting doses (p=0.003) were observed. Likewise, a statistically significant difference between survival rates of the rats in control and test groups was observed. Altogether, these experiments showed that exposure to microwave radiation may induce a significant survival adaptive response.

Purpose The aim of the present systematic review and meta-analysis was to resolve the conflicts on the diagnostic accuracy of artificial intelligence systems in detecting and segmenting oral and maxillofacial structures using cone-beam computed tomography (CBCT) images. Material and methods We performed a literature search of the Embase, PubMed, and Scopus databases for reports published from their inception to 31 October 2022. We included studies that explored the accuracy of artificial intelligence in the automatic detection or segmentation of oral and maxillofacial anatomical landmarks or lesions using CBCT images. The extracted data were pooled, and the estimates were presented with 95% confidence intervals (CIs). Results In total, 19 eligible studies were identified. As per the analysis, the overall pooled diagnostic accuracy of artificial intelligence was 0.93 (95% CI: 0.91-0.94). This rate was 0.93 (95% CI: 0.89-0.96) for anatomical landmarks based on 7 studies and 0.92 (95% CI: 0.90-0.94) for lesions according to 12 reports. Moreover, the pooled accuracy of detection and segmentation tasks for artificial intelligence was 0.93 (95% CI: 0.91-0.94) and 0.92 (95% CI: 0.85-0.95) based on 14 and 5 surveys, respectively. Conclusions Excellent accuracy was observed for the detection and segmentation objectives of artificial intelligence using oral and maxillofacial CBCT images. These systems have the potential to streamline oral and dental healthcare services.