Transforming Research into Market-Ready AI in Healthcare Domain

Business Challenge

Our customer aimed to bring a Machine Learning (ML) product to market, designed to detect 15 types of cardiac arrhythmias. This product, based on the research work of a Ph.D. scholar, aimed to refine models for detecting arrhythmias such as Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Premature Ventricular Complexes (PVC), and Atrial Fibrillation with high accuracy.

To achieve this, FDA certification was essential. However, challenges arose during the certification process:

The application needed to be tested on public datasets recommended by the FDA. Some of the models were initially trained on these same datasets, necessitating retraining on new datasets to comply with certification standards.

Achieving the required accuracy benchmarks for all models, including data denoising and arrhythmia detection, was critical for meeting FDA requirements.

The customer required strategic advisory support to refine their models, ensure compliance with regulatory standards, and navigate the complexities of the FDA certification process.

Our advisory engagement focused on providing strategic guidance and expertise to address these challenges, enabling the ML system's alignment with regulatory expectations and empowering the customer to secure FDA approval effectively.

Scope of Our Advisory Work

Our advisory engagement was focused on enabling the customer to achieve FDA certification for their Machine Learning (ML) product in healthcare domain. Leveraging the research work of a Ph.D. scholar, we provided strategic guidance and technical expertise to overcome the following challenges:

Dataset Alignment

Recommended and supported the retraining of models on new public datasets to ensure compliance with FDA testing requirements, avoiding overlap with datasets previously used for training.

Model Accuracy Improvement

Provided advisory inputs to enhance the accuracy of all models, including data denoising and arrhythmia detection, to meet FDA benchmarks.

FDA Certification Strategy

Advised the customer on the FDA certification process, including aligning model evaluation reports with regulatory standards.

Implementation and Guidance

Offered actionable recommendations for preprocessing, model refinements, and hyperparameter tuning to achieve target outcomes.

Collaboration Facilitation

Supported stakeholder communication, including engaging with the research team and academicians who contributed to the development of the product. This ensured alignment between their insights and the technical progress required to achieve certification goals.

This advisory-led approach ensured the customer was well-equipped to navigate the regulatory landscape and bring a market-ready product to fruition.

Advisory Approach

NStarX adopted a structured, hands-on, and experimentation-driven advisory model to support the customer in addressing their challenges effectively:

Discovery Phase

Conducted a thorough review of existing research and models, collaborating closely with the academic research team to understand their work and identify potential gaps. Hands-on experimentation was carried out to uncover inconsistencies and validate dataset quality and preprocessing requirements.

Strategic Guidance

Highlighted inconsistencies through comprehensive experiment results, providing actionable recommendations for customizing preprocessing pipelines and retraining models on public datasets. This ensured alignment with industry benchmarks and regulatory expectations.

Engagement Framework

Experimentation-Driven Advisory: Rather than relying solely on document reviews, NStarX conducted hands-on experiments to offer practical, evidence-backed insights, adding significant value to the advisory process.

Sprint-Based Planning: Recommended a six two-week sprint approach for structured progress monitoring and iterative improvements.

Collaborative Interactions: Facilitated regular interactions with the customer and the academic research team to ensure transparency and inclusivity. This approach built client confidence and ensured that stakeholder inputs were consistently incorporated into the advisory process.

Performance Optimization: Advised on detailed model evaluations and hyperparameter tuning to enhance model stability and accuracy across arrhythmia types.

This hands-on, experimentation-driven approach ensured not only scientific rigor and alignment with regulatory standards but also delivered true value by providing practical, transparent, and actionable guidance.

Technology & Frameworks

Technologies

TensorFlow 2.x, Scikit-Learn, Python 3.x, Wavelet Analysis for signal processing, and advanced encoders and decoders for model architecture refinement.

Model Architectures

Convolutional Neural Networks (CNNs) and complex denoising techniques to improve signal preprocessing and arrhythmia detection accuracy.

Domain Resources

Leveraged research papers on arrhythmia analysis and AI/ML research focusing on advanced arrhythmia detection and classification techniques. FDA guidelines documents provided the guiding light.

Business Outcomes

The advisory engagement delivered transformative results for our customer, bridging cutting-edge research with real-world healthcare applications and preparing a market-ready product for FDA certification:

Enhanced Model Performance

Recommended alternate datasets for retraining, addressing critical gaps in dataset alignment. Through hands-on experimentation, models achieved industry-standard benchmarks for arrhythmia detection, including data denoising and accurate classification for PVC and Atrial Fibrillation.

Inconsistency Resolution and Result Optimization

Highlighted inconsistencies in the original research results for specific models and proposed tailored approaches to align with FDA certification standards. This ensured the client achieved the desired accuracy levels.

Technology Recommendations

Suggested the adoption of state-of-the-art ML architectures, such as Transformers and Advanced encoders/decoders, to elevate model performance and meet certification requirements.

Collaboration and Transparency

Iteratively involved the research team in the evaluation of findings alongside the client, fostering a transparent and inclusive approach. The recommendations were seconded by the research team, further validating the advisory outcomes.

Accelerated Product Readiness

Within a short span of three months, the advisory engagement enabled the client to understand path towards achieving readiness for FDA certification, providing all necessary technical and strategic details previously lacking.

Definitive and Conclusive Advisory

Where prior teams were unable to deliver actionable insights to realign research outputs to a market-ready product, NStarX’s advisory work provided clear, conclusive recommendations, empowering the client to confidently move forward with their FDA certification process.

This case study illustrates NStarX’s expertise in delivering actionable, evidence-based advisory, blending technical excellence with strategic guidance, and transforming academic research into FDA-ready AI solutions in healthcare.