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Image capture and interpretation of Rapid Diagnostic Test results using OpenCV and machine learning techniques

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RDT Capture

This RDT Scanner prototype is designed for image capture and interpretation of Rapid Diagnostic Test results using OpenCV and machine learning techniques. Field testing is currently ongoing, with an initial focus on malaria RDTs. The app is designed to be used in tandem with Muso's community health app, which was built using the community health toolkit. The user is sent from Muso's CHW app to the RDT Capture app and back using Android Intents, as in this demo:

The current version is available in the Play Store. The app was co-designed by the UbiComp lab at the University of Washington, Muso, and Medic Mobile with financial support from the Bill and Melinda Gates Foundation. Initial prototyping is taking place within the Muso-led community health innovation network in Mali.

Please note that the app has not yet been clinically validated.

Publishing

Create a git tag starting with v, e.g. v1.2.3 and push the tag to GitHub.

Creating this tag will trigger a Travis CI to build, sign, and version a new release. The release-ready APKs are available for side-loading from GitHub Releases and are uploaded to the Google Play Console in the "alpha" channel. To release to the public, click "Release to Production" or "Release to Beta" via the Google Play Console.

License

The software is provided under BSD-3-Clause. Contributions to this project are accepted under the same license.

In the United States, or any other jurisdictions where they may apply, the following additional disclaimer of warranty and limitation of liability are hereby incorporated into the terms and conditions of the BSD-3-Clause open source license:

No warranties of any kind whatsoever are made as to the results that You will obtain from relying upon the covered code (or any information or content obtained by way of the covered code), including but not limited to compliance with privacy laws or regulations or clinical care industry standards and protocols. Use of the covered code is not a substitute for a health care provider’s standard practice or professional judgment. Any decision with regard to the appropriateness of treatment, or the validity or reliability of information or content made available by the covered code, is the sole responsibility of the health care provider. Consequently, it is incumbent upon each health care provider to verify all medical history and treatment plans with each patient.

Under no circumstances and under no legal theory, whether tort (including negligence), contract, or otherwise, shall any Contributor, or anyone who distributes Covered Software as permitted by the license, be liable to You for any indirect, special, incidental, consequential damages of any character including, without limitation, damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other damages or losses, of any nature whatsoever (direct or otherwise) on account of or associated with the use or inability to use the covered content (including, without limitation, the use of information or content made available by the covered code, all documentation associated therewith, and the failure of the covered code to comply with privacy laws and regulations or clinical care industry standards and protocols), even if such party shall have been informed of the possibility of such damages.

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Image capture and interpretation of Rapid Diagnostic Test results using OpenCV and machine learning techniques

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