Company: PFSA
Filing Date: 2025-10-29
Form Type: 424B3
Source: 0001213900-25-103174
Chunk: 205

Company: Profusa, Inc.
Filing Date: 2025-10-29
Form: 424B3
Chunk 205
---
 Data indicate that the sensors were responsive
to glucose during the study period and provided a significant body of data for algorithm development and evaluation. Performance analysis
of the data yielded an aggregate mean absolute relative difference (MARD) of 11.7% for sessions from 7 – 90 days post-injection.
The consensus error grid and cumulative MARD distribution are presented below, representing 163 sessions from 37 patients that passed
the data quality filters, resulting in 2406 paired blood glucose reference points. Data quality filters that were developed and automated
during the algorithm development exclude problematic traces including poorly placed readers over the sensor, unexpected fluorescence contamination
of the skin surface over the sensor, and poor adhesion of the reader over the sensor yielding low signal to noise.

<div align='center'>116</div>

The figure above shows a standard
Consensus Error grid, which is a tool used to evaluate the accuracy of blood glucose meters. It visualizes the paired data points that
passed the quality filters. This includes 163 sessions of data collection approximately 6 hours in duration from 37 patients over
the course of 90-days. Measured signals are calibrated using 3-point blood glucose calibration in each session. Each dot on the figure
shows the relationship between the Profusa reported blood glucose value (Y-axis) compared with the corresponding reference measured blood
glucose value (X-axis). The colors of the dots distinguish patients. The grid is divided into zones signifying the degree of risk posed
by the incorrect measurement: zone A represents no effect on clinical action; zone B represents altered clinical action — little
or no effect on clinical outcome; zone C represents altered clinical action — likely to affect clinical outcome; zone
D represents altered clinical action — could have significant medical risk; and zone E represents altered clinical action — could
have dangerous consequences.

The pair of figures above shows
an alternative visualization of the data presented above. For each data collection session, the system accuracy (MARD) is computed. The
per-session MARD distribution shows performance across the 163 sessions. “Held-out test set” refers to the machine learning
technique. Following the FDA’s recommendations for utilizing machine learning, the algorithm used to process the data was not used
in its training.

<div align='center'>117</div>

Glucose predictions modeled from held-out clinical sessions

Performance beyond 90 days was also evaluated in two separate groups — days 91 – 150 and days 151 – 275.

The figures