What factors are contributing to the increased false positive rate in Viz.ai's LVO detection algorithm this quarter?

Introduction

The increased false positive rate in Viz.ai's LVO detection algorithm this quarter is a critical issue that requires immediate attention. As we analyze this product problem, I'll follow a systematic framework to identify, validate, and address the root cause while considering both immediate and long-term implications for our AI-powered stroke detection system.

My approach will involve a thorough examination of the algorithm's performance, data inputs, and potential external factors. We'll start by clarifying the context, then rule out basic external factors before diving deep into the product's user journey, metric breakdown, and hypothesis formation. Finally, we'll conduct a root cause analysis and propose a validation and resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent update to the algorithm. Has there been any change to the LVO detection model or its training data in the past quarter?

Why it matters: Recent changes could directly impact the false positive rate. Expected answer: Yes, there was an update to improve sensitivity. Impact on approach: If confirmed, we'd focus on the update's impact and potential rollback.

• Considering the nature of medical imaging, I'm curious about the input data quality. Have there been any changes in the imaging equipment or protocols used by hospitals feeding data into Viz.ai?

Why it matters: Changes in input data quality could affect algorithm performance. Expected answer: No significant changes reported by partner hospitals. Impact on approach: If changes occurred, we'd investigate data preprocessing and normalization.

• Given the critical nature of stroke detection, I'm wondering about the threshold settings. Has there been any adjustment to the confidence threshold for LVO detection alerts?

Why it matters: Lower thresholds could increase sensitivity but also false positives. Expected answer: Threshold was slightly lowered to catch more potential strokes. Impact on approach: We'd analyze the trade-off between sensitivity and specificity.

• Considering potential seasonal factors, I'm curious about the patient demographics. Has there been a significant shift in the patient population characteristics this quarter?

Why it matters: Changes in patient demographics could affect algorithm performance. Expected answer: No notable shifts in patient demographics. Impact on approach: If shifts occurred, we'd investigate algorithm bias and retraining needs.