ct/chest

About

The full read of the chest CT study: the CT pulmonary angiography series is analysed and the result contains all sections the study supports.

Endpoint ct/chest/interpret · version 1.0.0 · $0.15 per request · the bare id ct/chest 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/ct/chest/interpret \
  --header "Authorization: Bearer $MEDRUN_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "input": {
    "files": [
      "file_01K7CTCHEST000000000000000"
    ],
    "options": {
      "report": false
    }
  }
}')
REQUEST_ID=$(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_01K7CTCHEST000000000000000"
  ],
  "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).

• pulmonary-embolism Pulmonary embolism: Pulmonary arterial opacification checked first (not assessable without it); pulmonary embolism present or absent; every clot outlined in 3D with its lobe, side and volume; the most proximal level (central or peripheral), the total clot burden, and the RV/LV ratio of 1.0 or more flag with the ESC 2019 risk-class guidance.
• rv-lv-ratio RV/LV ratio: Right and left ventricular diameters, the RV/LV ratio and the main pulmonary artery diameter.
• lung-nodules Lung nodules: Nodule detection, 3D outline, diameter and volume, type and the Fleischner 2017 management.
• triage Emergency triage: Fast flags for pulmonary embolism, pneumothorax, aortic dissection or large aneurysm, and large effusions.
• lung-rads Lung-RADS: Screening LDCT category, nodule growth against the prior and the S modifier.
• cancer-risk Lung-cancer risk: 1–6-year lung-cancer risk from a screening LDCT.
• emphysema Emphysema: Lobe-wise low-attenuation area (LAA-950) and distribution.
• ild Interstitial lung disease: ILD features, extent and the UIP-pattern category on HRCT.
• infection Infection extent: Consolidation and ground-glass outlines with involvement per lobe.
• airways Airways: Airway tree, wall thickness and broncho-arterial ratio for bronchiectasis and wall thickening.
• pleural-effusion Pleural and pericardial effusion: Effusion outline, side, volume and density.
• pneumothorax Pneumothorax: Pneumothorax detection, side, volume and tension signs.
• rib-fractures Rib fractures: Rib fracture detection with rib numbering, type and flail segment.
• coronary-calcium Coronary calcium: Agatston-equivalent coronary calcium score and category on non-gated chest CT.
• segment-lungs Lung, lobe and vessel segmentation: Lungs, five lobes, pulmonary arteries, veins and airways as masks with volumes.
• report Draft report: Opt-in draft report (findings, impression, limitations) written from the structured result; every number is validated against it.

OpenAPI document: https://api.medrun.ai/v1/models/ct/chest/interpret/openapi.json