angio/coronary

About

The full read of the study: every XA run is grouped, inventoried and analysed; the result contains all sections the input supports.

Endpoint angio/coronary/interpret · version 1.0.0 · $0.12 up to 600 frames · $0.24 up to 1,500 frames · $0.36 above 1,500 frames per request · the bare id angio/coronary is an alias.

1. Calling the API#

Set up your API key#

Create a key in Settings → API Keys and set it as MEDRUN_KEY in your runtime.

export MEDRUN_KEY="YOUR_API_KEY"

Submit a request#

Every call is asynchronous: submitting returns a request with its id and status, and you follow it until the result is ready.

response=$(curl --request POST \
  --url https://api.medrun.ai/v1/run/angio/coronary/interpret \
  --header "Authorization: Bearer $MEDRUN_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "input": {
    "files": [
      "file_01K6R4X2Q9Y7T3V5W8Z0A1B2C3"
    ],
    "options": {
      "report": false
    }
  }
}')
REQUEST_ID=$(echo "$response" | jq -r .id)

Stream progress#

Stages stream as server-sent events (received, queued, preprocessing, inference, report, completed). Critical findings are flagged first, before the full result.

curl --no-buffer \
  --url https://api.medrun.ai/v1/requests/$REQUEST_ID/events \
  --header "Authorization: Bearer $MEDRUN_KEY" \
  --header "Accept: text/event-stream"

2. Authentication#

The API uses API keys. Send Authorization: Bearer $MEDRUN_KEY with every call. Test keys (mr_test_…) run against the mock backend for free.

API key#

3. Queue#

Fetch request status#

Statuses run queued → preparing → running and end in succeeded, failed, canceled, rejected or expired.

curl --request GET \
  --url https://api.medrun.ai/v1/requests/$REQUEST_ID \
  --header "Authorization: Bearer $MEDRUN_KEY"

Get the result#

When the request has succeeded, output holds the result envelope. See the result format.

curl --request GET \
  --url https://api.medrun.ai/v1/requests/$REQUEST_ID \
  --header "Authorization: Bearer $MEDRUN_KEY" | jq .output

4. Files#

files is a flat list: DICOM files, folders, CD exports (DICOMDIR) and zip/tar archives. The platform groups them into series and runs.

Uploading files#

# 1. Create the upload (one PUT up to 100 MB; larger files get multipart part URLs)
upload=$(curl --request POST --url https://api.medrun.ai/v1/uploads \
  --header "Authorization: Bearer $MEDRUN_KEY" --header "Content-Type: application/json" \
  --data '{"filename": "study.zip", "bytes": 214000000, "media_type": "application/zip"}')
# 2. PUT the bytes to upload.url (with upload.headers)
curl --request PUT --upload-file study.zip "$(echo "$upload" | jq -r .upload.url)"
# 3. Complete it, then pass file.id in input.files
curl --request POST --url "$(echo "$upload" | jq -r .complete_url)" --header "Authorization: Bearer $MEDRUN_KEY"
FILE_ID=$(echo "$upload" | jq -r .file.id)

Hosted files (URL)#

You can also pass https URLs, for example signed S3 or GCS links. MedRun fetches them when the request starts.

5. Webhooks#

Add a webhook to the submit body. MedRun POSTs the request object, signed with Standard Webhooks headers, when the request ends. Configure secrets and see deliveries in Settings → Webhooks.

{
  "input": {
    "files": [
      "file_…"
    ]
  },
  "webhook": {
    "url": "https://example.org/medrun/webhook",
    "events": [
      "request.succeeded",
      "request.failed"
    ]
  }
}

6. Schema#

Input#

Example input:

{
  "files": [
    "file_01K6R4X2Q9Y7T3V5W8Z0A1B2C3"
  ],
  "options": {
    "report": false
  }
}

Output#

The output is the MedRun result envelope: sections[] with a status per section, then findings, measurements, scores, classifications, guidance, the optional report and artifacts (DICOM SR/SEG, overlays, PDF, FHIR).

• views Views: Each run's injected artery (LCA/RCA), positioner angles and projection, contrast and key frames.
• quality Quality: Adequacy flags (short run, panned gantry, missing calibration, segmentation fallback) carried into the report's limitations.
• segment-vessels Vessel segmentation: Temporally consistent coronary-tree mask on every frame, as DICOM SEG and an overlay video.
• segments Coronary segments: SYNTAX coronary segment labels (11 main segments) and the list of visible segments.
• stenosis Stenosis: Lesion list per segment with QCA %DS, MLD, reference diameter, length and an AI visual-equivalent grade; bands < 50 / 50–69 / ≥ 70 % (LM ≥ 50 %).
• dominance Dominance: Right, left or co-dominant circulation from the LCA and RCA runs.
• timi-flow TIMI flow: TIMI flow grade, corrected TIMI frame count and myocardial blush grade.
• lesion-features Lesion features: Calcification, thrombus, bifurcation (Medina), CTO, ostial location, tortuosity and ACC/AHA lesion type.
• syntax-score SYNTAX score: SYNTAX score with per-lesion breakdown and tertile.
• stent-sizing Stent sizing: Proximal and distal reference diameters, lesion length and a suggested stent size for a target lesion.
• pci-result PCI result: Pre/post-PCI comparison (residual %DS, acute gain, TIMI change, dissection).
• lvef LVEF estimate: Estimated LVEF from coronary runs when no LV gram or echo is available.
• report Draft report: Opt-in draft cath report (findings, impression, limitations) written from the structured result; every number is validated against it.

OpenAPI document: https://api.medrun.ai/v1/models/angio/coronary/interpret/openapi.json