TCS Research and Innovation
TCS Research
TCS Research: Inventing to build sustainable futures
Highlights
- The cardiac digital twin uses AI and multimodal data to enable precision cardiovascular care across prevention, diagnosis, therapy, and recovery. It goes beyond the one-size-fits-all approach in healthcare and addresses the limitations by personalising risk assessment and treatment at scale.
- It integrates wearables and causal AI models, such as Kolmogorov-Arnold Networks, to support early risk stratification and forecasting, reducing the first-time incidence of cardiac events.
- The cardiac digital twin incorporates biometric signals, such as ECGs and echocardiograms, to enhance diagnosis, improve diagnostic precision, and reduce the need for invasive testing.
- For therapy, it supports in silico surgical and drug simulations to increase procedural success and optimise medication choices.
- During recovery, continuous sensing and predictive models help lower 30-day hospital readmission rates by anticipating device failures.
On this page
Why do cardiovascular diseases need precision care?
Cardiovascular diseases (CVDs) have the highest mortality rate among non-communicable diseases (NCDs). The high prevalence of cardio-metabolic disorders is due to a sedentary lifestyle, high stress, substance abuse, environmental pollution, and poor food choices. The presence of comorbidities like diabetes and hypertension further fuels these. Cardiovascular diseases are a focus area for the healthcare industry and the public health bodies because of the high risk of heart attacks and sudden cardiac deaths (SCD). The challenge is identifying who is at risk, when that risk is rising, treating it once it appears, and determining which intervention is most likely to work for that individual.
How can an AI-powered digital twin reduce the risk of sudden cardiac death?
The current one-size-fits-all model of healthcare is proving inadequate to handle the growing volume of chronic diseases. So, how do we redefine the future of healthcare to deliver precision care at scale?
A cardiac digital twin can function as the missing operating layer for precision cardiovascular care, enabling clinicians to shift from reactive treatment to proactive, patient-specific decisions. A cardiac digital twin is a virtual, continuously updated model of a person’s heart that combines clinical, imaging, wearable and lifestyle data to simulate risk, predict outcomes and guide care decisions. Looking at the various stages of a chronic disease and exploring the use of an AI-powered digital twin can help better understand how this gap can be addressed at each stage.
Prevention and screening
In the early stages, cardiovascular diseases are mainly asymptomatic, increasing the risk of adverse events such as heart attacks. Hence, a comprehensive but personalised risk model is required for early detection. As per incidents reported in the media, the incidence of people collapsing on treadmills or during workouts is increasing at an alarming rate due to SCD. Simple Commercial off-the-shelf (COTS) wearable devices, combined with a one-time clinic visit, can be integrated with the cardiac digital twin to provide a multimodal cardiac risk score. Hence, it becomes the primary safety net. Causal AI models such as Kolmogorov-Arnold Networks (KANs), can be used to forecast event risk and build patient trajectories that can be acted upon. For providers, this means early detection and patient engagement, thereby improving both outcomes and adherence. For payers, this translates to a major reduction in the overall cost of care. Most importantly, for patients, this means a lower probability of morbidity and mortality due to cardiovascular diseases, which has one of the highest premature mortalities of all non-communicable diseases (NCDs).
Diagnosis
The role of an AI-powered digital twin in diagnosis centres on improved detection and triage. Digital twins enable better highlighting of regions of interest (RoI) in ECG for patients with paroxysmal atrial fibrillation (Afib) or anatomical segmentation of cardiac structures for patients with structural abnormalities. The twin is also capable of synthesis using generative AI, thereby potentially reducing the number of diagnostic tests needed. This makes diagnosis both more precise and affordable. For both payers and providers, this means improvement in value-based care metrics, patient/member satisfaction, and clinical outcomes. For patients, this means higher-quality, more precise care, leading to better therapeutic outcomes and an overall improvement in vitality and longevity.
Therapy and intervention
Cardiac interventions are often complex procedures with multiple unknowns, leading to high variability in outcomes. Surgeries and frequent readmissions prolong the downstream implications. The twin can serve as a simulation playground, allowing surgeons and physicians to perform “what-if” analysis of treatment and intervention options, powered mainly by Physics-informed neural networks (PINNs), and to visualise their outcomes. This improves the overall surgical success rate, reduces physician burnout, and helps choose the right medication, for example, beta-blockers v/s calcium channel blockers for their patients. For payers and providers, this translates into lower care costs, higher success rates, and greater customer satisfaction. More importantly, for patients, it means fewer alterations in therapy and more chances of therapeutic success like better surgical outcomes and better effects of medication. This leads to improved satisfaction and trust in the treatment, and less stress and anxiety for patients undergoing surgery and acute care.
Recovery and long-term care
Post-intervention care is critical for patients with cardiovascular diseases because they have a high readmission rate due to failure of stents, valves, and pacemakers. Data from wearables or on-device sensors, such as wearable defibrillators, can be used along with a cardiac digital twin to monitor the patient’s journey. Time-to-failure (TTF) models can be used within the twin to predict failure conditions in advance and take proactive action. This has major benefits for providers and medtech, as it reduces readmission and failure rates, whereas for payers, it translates into fewer claims to process. CVDs are often lifelong companions for patients, and long-term lifestyle restrictions cause depression, leading to poor adherence. Having an engaging and encouraging system for long-term care leads to greater emotional resilience, better adherence, and hence improved outcomes.
How can digital twins identify the key triggers of cardiovascular diseases?
Cardiovascular diseases are a focus area of multiple government initiatives and policymaking efforts, given their high prevalence. To make data-driven decisions in these workflows, the digital twin is scaled to cohort levels that examine drivers such as pollution, stress and anxiety, and substance abuse, which are usually overlooked in care journeys. By using causal AI models such as KANs in population-level risk analysis, policies can be developed to target the key drivers and major contributors, enabling a judicious and efficient allocation of public health resources.
Table 1: Digital twin interventions along the cardiovascular care pathway
Can AI and digital twins transform cardiac care at scale?
Digital twins are the future of healthcare, navigating the journey from illness to wellness to enable proactive, preventive, and precision care. As illustrated above, with multimodal data and science-backed AI, the cardiac digital twin can transform care across all four stages of the patient journey, from screening to long-term care. The same twin can also be used to improve public health and policymaking.
The digital twin in cardiology can be extended across multiple scales from the cellular to the system level. The concept can also be applied to other organ systems, such as the brain, musculoskeletal system, and digestive system, and perhaps to patient-level whole-body twins. The next step we envision is systemic interoperability: system-agnostic patient twins that can be ‘plugged in’ into any provider network and operate seamlessly, improving patient health.
For cardiac care, this shift could mean moving from treating risk after it becomes visible to understanding and acting on it before it turns critical.