SemanticMD Cloud

The SemanticMD Cloud Developer Hub

Welcome to the SemanticMD Cloud developer hub. You'll find comprehensive guides and documentation to help you start working with SemanticMD Cloud as quickly as possible, as well as support if you get stuck. Let's jump right in!

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AI for TB Detection Example

In this tutorial, we'll walk through the basic steps to integrate algorithms on SemanticMD Cloud into your own applications and clinical workflow. For this example, we will use a pre-trained algorithm that can process Chest X-rays and output the probability the patient has TB.

1. Retrieving Server Setup

Locate the IP Address, Port Number and AE Title of the DICOM destination you would like to send data for running the AI for TB algorithm. In this example, any chest X-ray sent to this server will be processed by the algorithm.

For SemanticMD customers, we provide a DICOM server with every account and the IP address can be found under Admin Settings.

For this tutorial, you can use the settings listed below.

IP Address:
Port: 4242

2. Sending DICOM Data

Once you have a retrieving server, you can use your own PACS or DICOM gateway to send data to the above destination. If you need help using a PACS or DICOM gateway, contact support and we can set up those applications for you.

In production, we recommend setting up automated rules to route data to this server. By default, we host multiple algorithms at a single destination server and based on the DICOM modality, we apply different processing algorithms.

For this example, you can navigate to the web uploader for our DICOM server here and upload a chest X-ray DICOM image.

3. Viewing Results

You can upload a DICOM image to the receiving server and view the results here in real-time:

For this example, we have a demo site using WebSockets to display the results of the algorithm, but in production what we can do is send a DICOM Secondary Capture (SC) Image, DICOM Structured Report (SR), or HL7 message to your PACS/EHR for rendering and saving results.

AI for TB Detection Example

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