In this episode, we explore cutting-edge research aimed at tackling one of the leading causes of firefighter line-of-duty deaths: sudden cardiac events. Host [Your Name] speaks with Dr. Andy Tam (NIST) and Dr. Dillon Dzikowicz (University of Rochester) about their groundbreaking project combining AI-driven ECG analysis with wearable technology. Their goal? A real-time, portable monitoring system that can detect dangerous heart rhythms in firefighters before it’s too late.

The conversation covers the science behind ischemic heart events, the challenges of collecting high-quality ECG data during firefighting, the role of machine learning in interpreting those signals, and the path from public research to a usable, life-saving product. You’ll also hear some lighter moments, including a debate about aliens and the quirks of wearable devices for tattooed users.

CONTACT DILLION:

dillon_dzikowicz@urmc.rochester.edu

0:00 – 3:50 | Introduction & Guest Backgrounds

Host introduces the episode’s focus: AI detecting abnormal heart rhythms in firefighters.

Meet Dr. Andy Tam (mechanical engineering, machine learning, firefighting technology)

Meet Dr. Dillion Dzikowicz (registered nurse, PhD, cardiovascular research in firefighters)

3:51 – 4:13 | The “Wheel of Stupid Questions” Intro

Acknowledging the show’s tradition of opening with fun, offbeat questions.

4:24 – 8:02 | Stupid Question: Do You Believe in Aliens?

Andy: Yes, as a mix of curiosity and belief.

Dillion: No — prefers evidence-based conclusions.

8:02 – 11:05 | The Problem: Sudden Cardiac Death in Firefighters

100+ firefighter deaths annually in the U.S. from cardiac events

Past interventions: diet, exercise, rehab — but missing the unique on-duty risk window

Shift toward real-time monitoring during actual firefighting

11:06 – 15:13 | Pathophysiology & Detection Goals

Ischemic-induced arrhythmias as primary target

ST segment changes as a key indicator

Predictive potential beyond real-time alerts

15:13 – 18:49 | Machine Learning 101 for ECG Interpretation

Training AI to “think” like a cardiologist

Filtering noise from movement artifacts

Importance of firefighter-specific datasets

18:50 – 21:49 | Wearable Device Development

Moving from bulky Holter monitors to modern wearables

Choosing chest-strap placement over wrist devices for reliability

FDA-cleared continuous ECG with ischemia-specific lead

21:50 – 22:50 | Wearables & Tattoos

Unique challenges in signal detection through tattooed skin

Clinical validation study includes tattooed subjects

22:51 – 27:01 | Software + Hardware Collaboration

Balancing AI development with firefighter comfort & usability

Open questions about when/where to wear devices (on shift vs. during calls)

Volunteer vs. career firefighter considerations

27:02 – 32:32 | Data Collection & Validation

Current study: monitors worn during structural fire training

Avoiding alarm fatigue with careful algorithm tuning

Combining hospital abnormal-event data with real-world firefighter data

32:33 – 39:20 | Model Performance & Future Applications

Accuracy: 95% with Holter data, 92% with wearable data

Potential expansion to police, military, EMS

Goal: device-agnostic algorithms for broad accessibility

39:20 – 45:05 | From Research to Product

Regulatory hurdles: FDA approval for “software as a medical device”

Public funding and the bridge between science and business

Focus remains on saving lives over commercialization

45:06 – 46:07 | Call for Participants

Recruiting volunteer, wildland, and career firefighters (18+) for ongoing studies

Contact details provided in episode description and social media posts