MantisStripTester

MantisStripTester is a tool for analysing colour strip tests (dipsticks). This prototype was developed as part of Health Hack 2019. We developed a smart phone app which can be used to interpret the result of a dipstick.

Problem overview

It is a common practice in developing countries for a medical laboratory professionals and clinicians to use a dipstick to detect and interpret a suspected disease like malaria using the naked eye.

This visual interpretation has been shown to be unreliable or inconsistent, because it could be interpreted differently by different testers. For example, 100 to 1,000 copies of DNA per microlitre could be interpreted as either positive or negative depending on the person viewing it. Also, the interpretation of the dipstick lines cannot tell the health professionals an estimate of the amount of the disease that is present in the body fluid before, during and after treatment of the disease.

This sort of problem could lead to inaccurate diagnosis for the patient, and inability of the clinicians to monitor treatment in order to determine a successful outcome. The lack of treatment follow-up poses a challenge to assess antibiotics sensitivity or resistance from specific infectious disease.

Potential difficulties

The main problem would be developing a tool which could rhobustly classify images of the test strips under different lighting conditions and variations between different devices (phone models).

There are a range of existing solutions to this problem, some involving the use of additional hardware or focussing on controlling the environmental conditions. Further reading:

Our solution

For processing the images, we developed two solutions:

ClassificationTests contains files for training a artificial neural network. The network required labeled images to be trained on, ClassificationTests/Images/ contains images which have been classified as either positive or negative. The network can be trained on these iamges and also augmented images (random rotations, scaling, skew, distortions, ...).
ImageProcessing contains a solution based on traditional image processing. The method looks at the integrated intensity of the two colour lines. By looking at the different colour components it is possible to classify the two strips.

For the user interface we developed a react-native app that can run on iOS and Android, this is contained in the mantisUi directory. We had difficulty connecting the trained neural network to the react-native ui. There are a range of packages for linking but there is a lack of good documentation or tutorials. In order to get a working proto-type by the end of the weekend, we developed an Android-only version tfliteAndroid. The Android version currently doesn't print the result to the screen, instead it outputs to the developer console. docs/ui_plan.jpg shows a potential UI layout, this is partially implemented in the react-native version of the app. [docs/Introduction and Result.docx](Introduction and Result.docx) contains text for the user interface.

Future direction

In order to take this project forward, the following needs to be done:

Finalise the user interface, including add the output text to the app
Generate a labled data set with strip intensities, use these to train a neural network to quantify the strip intensities.
Try other image processing techniques (maybe checkout ImageJ)
Once the app is working, generate additional training data in a variety of different circumstances by using the app to collect video of strips with pre-determined intensities.
Integrate tensorflow lite (or another alternative) with react-native or develop a iOS version of the app with tensorflow

Contributors

Ameh James (Problem Owner)
Ali Abdelghany
Bavani Tam
Divjyot Singh
Fei Yang
Harsh Pandya
Isaac Tucker
Kelsey McGahan
Matthew Martin
Pritii Tam Wai Yin
Randall Fernando
Roxana Perez
Isaac Lenton

License

All the code for this weekend was released under the MIT license. See LICENSE for details.

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