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Biomedical engineering researchers at John's Hopkins University are working to transform heart patient care.
约翰霍普金斯大学的生物医学工程研究人员正在努力改善心脏病患者的护理过程。
They can now create a personalized digital model of a patient's heart, a digital twin and use artificial intelligence to help better predict who is most at risk.
他们现在可以为病人的心脏创建一个个性化的数字模型，一个数码孪生体，并使用人工智能来更好地预测谁的发病风险最大。
We do contrast enhanced MRI of the heart and then we combine that contrast, enhanced MRI with all the clinical data that's known for the patient.
我们对心脏进行对照增强核磁共振成像，然后将共振成像与所有已知的病人临床数据结合起来。
This is combined with survival analysis and we can tell, over 10 years, what is the risk of a patient of having sudden cardiac death.
与生存分析相结合后，我们可以知道，10年以后，病人心脏性猝死的风险是多少。
Unlike segmented images most often used today, professor Natalia Trayanova says the whole images of the heart produce more accurate predictions of which patients need defibrillators.
与目前最常用的分割图像不同，纳塔莉亚教授表示，心脏的整体图像可以更准确地预测哪些患者需要除颤器。
We provide these deep learning algorithms that are multimodality, they represent the patient's condition much better.
我们提供了这些多模态的深度学习算法，它们能更好地代表病人的状况。
At the Sydney Kimmel Comprehensive Cancer Center at Johns Hopkins, Dr Victor Velculescu is leading research into developing new ways of detecting early stage lung and other cancers.
在约翰霍普金斯大学的悉尼金梅尔综合癌症中心，维克多·威尔克斯库博士正在领导一项研究，开发检测早期肺癌和其他癌症的新方法。
He and his team observed that cancer cells grow and replicate more chaotically than normal cells.
他和他的团队观察到癌细胞的生长和复制比正常细胞更混乱。
So when those cells die they leave behind telltale characteristics of fragments of DNA circulating in the blood called Cell-Free DNA, which carries clues about whether a person may have cancer.
因此，当这些细胞死亡时，它们会留下在血液中循环的DNA片段的特征，称为无细胞DNA，它携带着一个人是否患有癌症的线索。
The team developed a technology called DELFI which uses novel machine learning algorithms to analyze fragments of that Cell-Free DNA.
该团队开发了一种名为德尔菲的技术，该技术使用新颖的机器学习算法来分析无细胞DNA的片段。
We look in the blood, we identify molecules of DNA called Cell-Free DNA, and we look for the profile or the patterns of this Cell-Free DNA as a way to identify those individuals who have cancer versus those that don't.
我们会观察血液，识别被称为无细胞DNA的DNA分子，我们会寻找这种无细胞DNA的轮廓或模式，以此来识别哪些人患有癌症，哪些人没有。
Local Schools says improved blood tests could lead to greater cancer screenings worldwide.
当地学校表示，改进的血液检测可能会导致全球范围内更多的癌症筛查。
Julie Tabo VOA news, Baltimore Maryland.
VOA新闻，朱莉·塔博马里兰州巴尔的摩报道。
