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全食管切除下咽胃吻合治疗颈段和高位胸上段食管癌

腹腔镜下经胆道镜钬激光碎石治疗难治性胆管结石

目的探讨腹腔镜下经胆道镜钬激光碎石治疗难治性胆管结石的可行性和疗效。 方法回顾性分析我院2009年6月至2014年12月期间18例腹腔镜下经胆道镜钬激光碎石治疗难治性胆管结石（无法在内镜下乳头括约肌切开取石术取出者）的临床资料。 结果成功手术18例，手术时间60～200 min，平均130 min。一次性取净结石16例，2例女性患者分别有四川、安徽生活居住史，术后造影在左、右肝管内再次发现絮状结石，予熊去氧胆酸口服，随访至今，其中1例结石消失，另外1例仍有结石表现。术后均无胆管出血、胆瘘、胆管狭窄等并发症发生。 结论对于难治性的胆管结石，腹腔镜下经胆道镜钬激光碎石治疗是一种安全、有效的方法。

Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.

Status, problems and countermeasures of artificial intelligence application in medical education

Human society has entered the age of artificial intelligence(AI). Medical practice and education are undergoing profound changes. The government strongly advocates the application of AI in the field of education and it has been incorporated into the national strategy. The integration of medical education and AI technology is changing the paradigm of modern medical education. This paper introduces the current application status of AI in medical education, and analyzes the existing problems and proposes corresponding resolutions, so as to lay a foundation for promoting the integration of medical education and AI.