RESUMO
Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.
RESUMO
AIMS: Although some evidence suggests that omega-3 polyunsaturated fatty acids (PUFAs) supplementation influences enzymes involved in forming homocysteine (Hcy) and improving hyperhomocysteinemia, these findings are still contradictory in humans. The aim of this systematic and meta-analysis study was to investigate the effects of omega-3 supplementation on Hcy using existing randomized controlled trials (RCTs). DATA SYNTHESIS: Available databases, including PubMed/MEDLINE, Web of Science, Scopus, Cochrane Library, and Embase, were searched to find relevant RCTs up to June 2021. The effect size was expressed as weighted mean difference (WMD) and 95% confidence interval (CI). CONCLUSION: A total of 20 RCT studies with 2676 participants were included in this article. Our analyses have shown that omega-3 supplementation significantly reduced plasma Hcy levels (WMD: 1.34 µmol/L; 95% CI: 1.97 to -0.72; P < 0.001) compared to the control group. The results of subgroup analysis showed that omega-3 supplementation during the intervention <12 weeks and with a dose ≥3 gr per day causes a more significant decrease in Hcy levels than the intervention ≥12 weeks and at a dose <3 gr. In addition, omega-3 supplements appear to have more beneficial effects in individuals with high levels of normal Hcy. This meta-analysis showed that omega-3 supplementation significantly improved Hcy. However, further studies are needed to confirm the findings.