Pipeline Integration
Bring automated CPET interpretation into the data pipeline
Oxynet is designed to process CPET time-series programmatically and return structured interpretation outputs. The next step is to move automated interpretation closer to where CPET data are acquired, processed, and used.
From exported files to continuous data flows.
No manual CSV or XLS transfer required.
A test is recorded, and the interpretation follows, with nobody deciding one file at a time to run it.
Current State
Today: interpretation often starts after export
File-based analysis is effective for research, validation, and individual testing. At scale, however, export and manual transfer create an unnecessary boundary between CPET acquisition and automated interpretation.
CPET Device
Exercise laboratory
Manufacturer Software
Data acquisition & display
Test Completed
CPET time-series recorded
CSV / XLS Export
File-based data handoff
Manual File Handling
Local storage, email, shared drives
External Platform / Upload
Data re-entry or file upload
Oxynet
Automated interpretation layer
Structured Outputs
Thresholds, intensity domains, metrics
Oxynet currently enters the workflow after data have been exported and manually handled. That is necessary today, but not architecturally required.
The Goal
Interpretation as part of the pipeline
Existing acquisition and data-management workflows can remain unchanged. Oxynet can operate as an interpretation layer, receiving CPET time-series programmatically and returning structured outputs for downstream systems.
CPET Device
Exercise laboratory
Manufacturer Software / Core Lab
Acquisition & data infrastructure
CPET Data Pipeline
Existing infrastructure
Existing workflow
Continues unchanged
↓ as before
Oxynet layer
Oxynet API
Automated interpretation layer
Automated Interpretation
VT1, VT2, intensity domains
Structured Outputs
Programmatic response
Clinical / Research / Software
Downstream systems
Designed to integrate, not replace.
Oxynet does not replace acquisition systems, manufacturer software, or existing clinical workflows. It adds an automated interpretation layer that operates on the same CPET time-series.
No manual CSV or XLS transfer required.
When integrated at the data-pipeline level, CPET time-series reach Oxynet programmatically, without any intermediate export or manual file handling.
How It Works
Three steps, one integration point
01
CPET time-series in
Breath-by-breath or other supported CPET time-series are transferred programmatically from an existing data system to the Oxynet API.
02
Oxynet interpretation
Oxynet parses the vendor file as exported: twenty formats, each with its own unit conventions and clock quirks, then measures the physiology in it.
03
Structured outputs out
Thresholds, per-breath intensity domains, derived quantities, oscillation analysis and signal-integrity checks are returned as structured JSON for review, storage, visualisation or downstream analysis.
Oxynet is reachable over REST, over MCP for AI assistants, or via the Python package for local research pipelines. The API documentation lists every endpoint and the twenty vendor formats detected automatically. Oxynet's outputs are not intended to replace clinical review. They provide a consistent, reproducible measurement baseline.
Collaboration
Do you operate a CPET data pipeline?
We are interested in working with organisations that acquire, process, or manage CPET data at scale, including exercise testing core laboratories, clinical trial networks, hospitals, research infrastructures, software providers, and CPET technology partners.
Core Laboratories
Exercise testing centres managing continuous or high-volume CPET data flows.
Clinical Trial Networks
Multicentre studies requiring consistent, automated CPET interpretation across sites.
Software & Device Partners
CPET software providers and device manufacturers exploring interpretation integration.
Research Infrastructures
Academic and clinical networks evaluating automated interpretation in real-world workflows.
We are particularly interested in prospective integration, continuous data flows, multicentre validation, and evaluating automated interpretation within real-world CPET workflows.