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AVNRT Ablation Hero

How Johnson and Johnson Found Patterns Hidden in Ablation Data?

Executive summary

For Johnson and Johnson, analyzing cardiac ablation data had always been a puzzle. Each procedure generated mountains of raw files that were hard to align, normalize, and interpret. With HumbleBeeAI’s custom analytical framework, J&J turned this challenge into an opportunity. The solution automated data normalization, statistical analysis, and interactive 3D visualization, transforming complex procedural data into actionable insights that drove both efficiency and smarter decision making.

Introduction

As a global leader in healthcare, Johnson and Johnson is constantly searching for new ways to improve procedural outcomes and patient care. One area of focus was the analysis of cardiac ablation data from AVNRT procedures. Each case carried valuable information hidden inside electrophysiological recordings and biophysical metrics. But without a structured way to interpret this information across multiple patients, the insights remained locked away.

To unlock them, J&J needed more than spreadsheets. They needed a system that could standardize complexity, highlight patterns, and provide clear answers.

The Problem

The challenge was not the lack of data but the lack of structure.

  • Data alignment was broken: Each patient’s 3D mapping data used different coordinates, making cross-patient comparison nearly impossible.
  • Formats were inconsistent: XLSX and CSV files arrived with categorical mismatches, free-text annotations, and missing codes that required endless manual cleanup.
  • The scale was daunting: With multi-patient datasets growing rapidly, traditional methods were slow and unreliable.
  • Interpretation was difficult: Even when processed, 3D ablation data was overwhelming to visualize and compare in a way that made sense to clinical experts.

Without a streamlined solution, J&J faced long manual hours, inconsistent outputs, and missed opportunities to uncover the procedural insights that could improve patient outcomes.

The Solution

HumbleBeeAI responded with AVNRT Ablation Analysis with CARTONET, a custom framework built to transform raw, scattered data into clarity.

  • Integrated Data Processing and Normalization: All raw XLSX and CSV files were ingested into a unified system. By using UUID identifiers and reference-based normalization anchored on His signal coordinates, every dataset was aligned to a common spatial reference. This eliminated the core obstacle of cross-patient comparison.
  • Automated statistical analysis: The framework computed summary statistics such as power, temperature, impedance drop, and spatial distances. Box plots, percentiles, and comparative charts made trends visible at a glance, replacing manual calculation with automated precision.
  • Advanced 3D Visualization: With Plotly, HumbleBeeAI built interactive 3D models and LAO and RAO projections that revealed the spatial relationships between His signals, ablation sites, and outcome categories. Users could rotate views, filter by category, and quickly turn dense data clouds into clear visual narratives.

3D Visualization Interface

  • Predictive Modeling: The system went a step further by correlating pre-ablation features with outcomes. This gave J&J early insights into predictors of procedural success and laid the foundation for more refined strategies in the future.

Result

The results were transformative.

  • Efficiency skyrocketed as automated pipelines replaced manual preprocessing, cutting down time from data collection to insight generation.
  • Accuracy improved through standardized formats and structured calculations, making cross-patient comparisons both reliable and consistent.
  • Insights became actionable. Interactive visualizations and clear statistical summaries revealed patterns that were previously invisible, helping clinical experts identify outcome predictors with confidence.
  • The predictive framework created a foundation for long-term learning, opening doors for J&J to refine procedural strategies and advance patient care.

In short, what was once raw and chaotic became structured, interpretable, and deeply valuable.

Conclusion

By partnering with HumbleBeeAI, Johnson and Johnson turned the challenge of AVNRT ablation analysis into a breakthrough. A once-fragmented dataset became a powerful source of insight, driving efficiency, accuracy, and predictive capability.

This project shows what happens when advanced analytics meets real clinical complexity. For J&J, it was more than a technical upgrade. It was a step toward better decisions, better strategies, and ultimately better patient outcomes.

Ready to uncover insights hidden in your most complex data? Book a demo with HumbleBeeAI and see how we can help.