Digital twins are real time virtual objects that mimic and characterise the physical object in its entirety.
By Ravi Ramaswamy
Digital twins are real time virtual objects that mimic and characterise the physical object in its entirety. By capturing data from various sources and coupling them with Machine Learning (ML) algorithms and adding the computational power of Artificial Intelligence (AI), these models help analyse data, learn from it and enables optimisation of performance. It can also have the capability to predict failures and future behaviours.
As Henk van Houten, CTO, Royal Philips describes it “A model of a physical object – a ‘twin’ – enables you to monitor its status, diagnose issues and test solutions remotely. It is a dynamic virtual representation of a device, which is continuously fed with data from embedded sensors and software. This gives an accurate real-time status of the physical device.”
The global digital twin market, as per Grandview Research, is expected to reach $26.07 billion by 2025. Industries that would find potential usage for Digital twins include healthcare and life sciences, aerospace, defense, automotive, transportation, manufacturing, energy and utilities. These industries are particularly keen in adopting the technology the expectation being it will enhance efficiency, augment productivity, ensure cost-efficient operations, and streamline the processes.
The economic value of digital twins will vary widely, depending on the commercial monetisation models that drive them. It is expected that these technologies will help drive productivity around asset utilisation, downtime reduction as also lowering overall maintenance costs by early identification of potential breakdowns. Some examples of application:
Identify maintenance needs before they arise: Examination cancellations and unforeseen workflow disruptions are critical issues for both hospitals and patients. The challenge, then, is to identify potential problems before they occur, so you can schedule maintenance at a time when the equipment is not in use. Through proactive remote monitoring services, one can track and analyae these log messages for early warning signs of impending technical issues.
Digital Personal Avatars will incorporate a patient’s cellular, molecular, genetic and clinical information. By knowing a patient’s genetic and molecular make-up in advance, doctors can determine whether a particular medication is likely to help and what dosage to use. The heart was the first organ to be precisely modeled this way, but digital twins of other organs, including the brain, are being developed. Eventually, we’ll have complete whole-body digital twins of individual patients – in effect, digital personal avatars.
The applications are wide-ranging. Cancer surgeons will be able to evaluate precisely how tumours are positioned in relation to healthy tissues. Orthopedic surgeons will be able to use advanced 3D images to visualise the topography of complex fractures.
Privacy and security: Consent management and ethical sharing of data within the ecosystem including management of patient records.
Infrastructure investments to create digital twins: platform / componentised architecture / cloud deployment should help. Business models will have to be put in place to ensure equitable share for multi users.
Competency building in creating and deploying digital twins.
To sum it all, the future looks extremely promising for this technology. It is labelled as one of the “exponential technologies” we are dealing with – the output is disproportionally large compared to the given inputs.
(The writer is Senior Director of Health Systems, Philips Innovation Campus and Chairperson of Healthcare WG, IET IoT Panel)