Electrocardiogram (ECG) is a noninvasive, inexpensive, and convenient test for diagnosing cardiovascular diseases and assessing the risk of cardiovascular events. Although there are clear standardized operations and procedures for ECG examination, the interpretation of ECG by even trained physicians can be biased due to differences in diagnostic experience. In recent years, artificial intelligence has become a powerful tool to automatically analyze medical data by building deep neural network models, and has been widely used in the field of medical image diagnosis such as CT, MRI, ultrasound and ECG. This article mainly introduces the application progress of deep neural network models in ECG diagnosis and prediction of cardiovascular diseases, and discusses its limitations and application prospects.
ObjectiveThis study investigates the adherence to ethical principles in doctoral dissertations focused on human as the research subject, aiming to provide a foundation for enhancing ethical awareness among medical doctoral candidates. MethodsUtilizing the Chinese database of doctoral dissertations, a total of 1 733 relevant papers published in 2021 were collected. The study compared ethical considerations among double first-class universities, other high-ranking institutions, different university types, various disciplines, diverse training orientations, enrollment types, and medical doctoral dissertations from different regions. ResultsIn 2021, among Chinese medical doctoral dissertations involving human as the research subject, 73.34% mentioned ethical considerations, and 86.27% mentioned informed consent. Dissertations reporting ethical approval descriptions, approval numbers, ethical approvals, and informed consent constituted only 2.19%. Notably, 12.52% of medical doctoral dissertations failed to incorporate ethical considerations and informed consent details in their content. ConclusionThe ethical awareness of medical doctoral candidates in China and the reporting of ethical information in their dissertations require urgent enhancement and improvement.
Non-small cell lung cancer is one of the cancers with the highest incidence and mortality rate in the world, and precise prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning as a breakthrough technology of artificial intelligence has shown good performance and great potential in the application of non-small cell lung cancer prognosis model. The research on the application of deep learning in survival and recurrence prediction, efficacy prediction, distant metastasis prediction, and complication prediction of non-small cell lung cancer has made some progress, and it shows a trend of multi-omics and multi-modal joint, but there are still shortcomings, which should be further explored in the future to strengthen model verification and solve practical problems in clinical practice.