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Background: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods: Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results: Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions: It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. [ABSTRACT FROM AUTHOR]

High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning method for chronic gastritis classification using gastric X-ray images. The proposed semi-supervised learning method based on tri-training can leverage unannotated data to boost the performance that is achieved with a small amount of annotated data. We utilize a novel learning method named Between-Class learning (BC learning) that can considerably enhance the performance of our semi-supervised learning method. As a result, our method can effectively learn from unannotated data and achieve high diagnostic accuracy for chronic gastritis. Graphical Abstract Gastritis classification using gastric X-ray images with semi-supervised learning. [ABSTRACT FROM AUTHOR]

Purpose: Early life exposure to famine may affect the susceptibility to metabolic disorders including dyslipidemia. However, few studies explored the association between them in Chinese population. We aimed to evaluate the association between Chinese famine (1959-1961) exposure during early life and the risk of dyslipidemia in adulthood.Methods: The study performed a historic cohort study and data were from China Health and Nutrition Survey conducted in 2009. A total of 4843 subjects born between 1941 and 1966 were categorized into fetal-infant exposed group (N = 433), childhood exposed group (N = 2132), adolescence exposed group (N = 1140), and unexposed group (N = 1138), respectively. Dyslipidemia was defined by Chinese adult dyslipidemia prevention guide (2016 edition). We compared fetal exposed group, childhood exposed group, and adolescence exposed group to unexposed group using logistic regression models to assess the effect of famine exposure on later dyslipidemia.Results: The prevalence of dyslipidemia among subjects in unexposed group, fetal exposed group, childhood exposed group, and adolescence exposed group was 56.40, 64.00, 63.90, and 63.90%, respectively. Compared with unexposed group, participants exposed to famine in fetal period (OR 1.34, 95% CI 1.05-1.70), childhood (OR 1.44, 95% CI 1.23-1.69), and adolescence (OR 1.41, 95% CI 1.17-1.71) had higher risks of dyslipidemia in adults after adjustment for potential confounders.Conclusion: Exposure to Chinese famine in early life was associated with increased risk of dyslipidemia in adulthood. Preventing undernutrition in early life is an appropriate recommendation to reduce the prevalence of later dyslipidemia. [ABSTRACT FROM AUTHOR]