Flow meter and related method

An image-based system with infrared backlighting and controller analysis addresses inaccuracies in gravity-fed infusion systems, providing precise flow rate monitoring and regulation using spline fitting and physics-based estimation, enhancing accuracy and reducing complexity and cost.

WO2026096577A1 Publication Date: 2026-05-07DEKA PRODUCTS LP
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Patent Information

Application Number
PCT/US2025/053030
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2025-02-04
Filing Date
2025-10-29
Publication Date
2026-05-07

AI Technical Summary

Technical Problem

Existing gravity-fed infusion systems suffer from inaccuracies in flow rate measurement due to variations in drop size and non-linear compression characteristics, and they lack precise flow regulation without mechanical pumps, which are costly and power-consuming.

Method used

An image-based system using a transparent drip chamber with infrared backlighting and aligned image sensors, combined with a controller that analyzes drop formation and meniscus trends through spline fitting and physics-based estimation, to provide real-time flow rate monitoring and regulation, employing multi-lumen inserts and safety valves for precise control.

Benefits of technology

Achieves precise and accurate flow rate measurement and regulation in gravity-driven infusion systems, maintaining compliance with medical safety standards and reducing complexity and cost compared to mechanical pumps.

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Abstract

This disclosure relates to a gravity‑driven infusion system that uses image‑based monitoring to regulate flow. A contrasting, infrared‑backlit wall and camera capture pendant drops and meniscus levels within a transparent drip chamber. The controller defines a baseline referenced to the spout or meniscus and fits sparse spline points on the drop perimeter to derive geometric functionals, such as neck width and centroid height. Temporal changes of these functionals map directly to instantaneous flow without explicit volume integration. An optional Young–Laplace model provides a physics‑based boundary and confidence metric. A meniscus trend yields a low‑frequency flow estimate. Confidence‑weighted fusion controls a flow‑control valve that compresses a multi‑lumen insert. An independent safety occluder and watchdog ensure fail‑safe shutdown. A medication library stores fluid‑aware calibration. Multi‑source embodiments orchestrate multiple controllers with virtual head‑height equalization and verified handoffs. It performs pre‑infusion checks, logs uncertainty, and supports tilt compensation too. Continuous stream detection triggers alarms.
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