# SCH: Simulation Optimization of Cardiac Surgical Planning

> **NIH NIH R01** · UNIVERSITY OF TENNESSEE KNOXVILLE · 2024 · $289,466

## Abstract

Many patients take surgical interventions to fight the battle against heart disease. Surgical successes are
critical to the patients’ health and their family well-being. For e.g., atrial fibrillation (AF) is the most
common arrhythmia in elder population. Catheter ablation is an established treatment for AF, which
sequentially creates incision lines to block faulty electrical pathways. However, there are large variations
in surgical outcomes. Modern healthcare systems are investing heavily in sensing and computing
technology to increase information visibility and cope with disease complexity. Massive data are readily
available in the surgical environment. Realizing the full data potential for optimal decision support
depends on the advancement of information processing and computational modeling methodologies.
Our long-term goal is to advance the frontier of precision cardiology by developing new sensor-based
modeling and simulation optimization methodologies. The objective of this project is to optimize AF
ablation by integrating simulation-enabled planning with physics-augmented machine learning of sensor
signals from patients who underwent AF ablation. This objective will be accomplished by pursuing 3
specific aims: 1) Physics-augmented artificial intelligence (AI) for cardiac modeling – This approach will
assimilate heterogeneous sensing data and incorporate electrophysiology prior knowledge into deep
learning to increase the robustness of decision making under uncertainty, thereby driving computer
simulation into clinical applications; 2) Optimal sensing and sequential learning of space-time AF
dynamics – This approach will provide quantitative knowledge of disease mechanisms instead of
subjective knowledge that is difficult to translate (or transfer), thereby reducing healthcare disparity due to
the availability of human experts in rural areas; 3) Integrating sensor-based learning and simulation
optimization for surgical planning - This approach will integrate physics-augmented modeling (Aim 1) and
sensor-based learning (Aim 2) with simulation optimization to improve the clinical practice towards
data-driven & simulation-guided surgical planning. This project will make a major breakthrough towards
precision cardiology by (i) going beyond the current practice of largely expert-based or ad hoc decisions,
(ii) capturing underlying complexities in space-time cardiac dynamics, and (iii) integrating physics-based
modeling, sensor-based learning, and simulation-based planning for surgical decision support.

## Key facts

- **NIH application ID:** 10924039
- **Project number:** 5R01HL172292-02
- **Recipient organization:** UNIVERSITY OF TENNESSEE KNOXVILLE
- **Principal Investigator:** Hui Yang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $289,466
- **Award type:** 5
- **Project period:** 2023-09-07 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10924039

## Citation

> US National Institutes of Health, RePORTER application 10924039, SCH: Simulation Optimization of Cardiac Surgical Planning (5R01HL172292-02). Retrieved via AI Analytics 2026-07-29 from https://api.ai-analytics.org/grant/nih/10924039. Licensed CC0.

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