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Humanizing medtech: The dawn of a digital twin
Although there have been some initial successes in this area, the concept imposes particular requirements on two mammoth tasks in particular: the collection and exchange of data.
Theoretical scenarios in real time
Systems biology
Digital twin
A digital twin for everyone?
Neural networks
Personalized medicine is gaining ground
Reducing risks and saving money
Facilitating treatment decisions and relieving the burden on medical teams
What is a cohort analysis?
What is holding the twins back?
- <p>Women aged 50 and over are regularly called in for a mammogram – an X-ray of the breast – with the aim of detecting breast cancer at an early stage. Depending on individual circumstances, this screening might be too early, too late, or not necessary at all. “A digital twin allows us to make a better assessment of the situation,” says Professor Michael Uder, deputy medical director of Universitätsklinikum Erlangen. It also provides physicians with a new tool that allows them to tailor treatment precisely to the individual patient, says Uder.<sup>5</sup><p/> <p>One key advocate of the development of a digital twin for breast cancer screening is d.hip, an alliance made up of Siemens Healthineers, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsklinikum Erlangen, and Medical Valley. The aim is to give women the option of being accompanied by “digital sisters” in as little as around five years.</p>
Andrea Lutz is a journalist and business trainer specialising in medicine, technology and healthcare IT. She lives in Nuremberg, Germany. Doreen Pfeiffer studied journalism with a focus on medicine/bioscience and works as an editor at Siemens Healthineers.
- 1https://www.cell.com/fulltext/S0092-8674(15)01481-6, November 2015.
2Reinhard Laubenbacher, James P. Sluka, James A. Glazier: Using digital twins in viral infection. Science, 12 Mar 2021: Vol. 371, Issue 6534, pp. 1105-1106.
3Source: Hirsch-Kreinsen; Kubach, U.; Stark, R.; Wichert, G. von; Hornung, S.; Hubrecht, L.; Sedlmeir, J.; Steglich, S.: Themenfelder Industrie 4.0. Forschungs- und Entwicklungsbedarfe zur erfolgreichen Umsetzung von e 4.0 [“Industry 4.0 talking points. Research and development requirements for the successful implementation of e 4.0”]. Munich, 2019.
4Meyer, K.; Ostrenko, O.; Bourantas, G.; Morales-Navarrete, H.; Porat-Shliom, N.; Segovia-Miranda, F.; Nonaka, H.; Ghaemi, A.; Verbavatz, J.-M.; Brusch, L;. Sbalzarini, I.F.; Kalaidzidis, Y.; Weigert, R.; Zerial, M.: A Predictive 3D Multi-Scale Model of Biliary Fluid Dynamics in the Liver Lobule. Cell Systems 22, (2017) 277–290.