The global pandemic has exposed serious flaws in supply chains, including critical ones for industries such as pharma and medical supplies. Shortages of personal protective equipment for health workers and ventilators in hospitals are the most prominent ones. To prevent this problem from occurring again when the next disaster strikes, governments should consider establishing a stress test for companies that provide critical goods and services that’s akin to the stress tests for banks that the U.S. government and European Union instituted after the 2008 financial crisis. This test should focus on the resilience of companies’ supply chains.
With the proliferation of commercial wearable devices, we are now able to obtain unprecedented insight into the ever-changing physical state of our bodies. These devices allow real-time monitoring of biosignals that can generate actionable information to enable optimized interventions to avoid injury and enhance performance. Combat and medical planners across all military services are keenly interested in harnessing wearable sensor advances to diagnose, predict, and improve warfighter health and performance. However, moving from civilian promise to military reality is complex, with unique requirements of hardware design, real-time networking, data management, cybersecurity, predictive model building, and decision science. Emerging technologies for military on-the-move monitoring will be highlighted, along with a discussion of an integrated open systems architecture approach for functional evolution.
Advances in optics, biological sensing, medical imaging technologies, high throughput genetic sequencing is leading to massive datasets, which need to be analyzed. However, current Artificial Intelligence algorithms usually require 1000’s of examples of well-annotated datasets for high accuracy classification. Fluorescent biomarkers are important indicators of disease such as oral cancer, but imaging them can require specialized and often-expensive devices. Medical images, if diagnosed early with biomarker images and expert knowledge, can be valuable to prevent occurrences of serious systemic illnesses. In this lecture, we will discuss two convolutional neural network classifiers trained with disease signatures and fluorescent biomarker images to identify biomarkers in white light images as a per-pixel binary classification task. Once trained, the classifiers predict the location and intensity of fluorescent biomarkers in white light images without requiring specialized biomarker imaging devices or expert intervention. This generalized approach can be useful in other domains where diagnostic biomarker predicting can augment expert knowledge using standard white light images.
Tricia Dinkel Manager of Partnerships & Engagement, MIT Startup Exchange
Manufacturing Work of the Future: Technology, Institutions, and Possibilities
2020 Future of Manufacturing - Elisabeth B Reynolds