Dear members,
As you know, the identification of urinary tract infection by culture is crucial for the diagnosis and treatment. However, traditional methods of culture identification can be time-consuming and expensive. Therefore, I am interested in exploring the potential of using AI and machine learning as a tool for predicting the presence of pathogenic bacteria in urine cultures based on urine dipsticks and sediment analysis.
Thank you for your time and consideration.
Julien
Hey Cabo Julien, what you're describing falls under the term image classification. For a quick overview see: https://datagen.tech/guides/image-classification/python/
These machine learning models are extremely widespread and fairly easy to train, if you have some python knowledge. You would need to create or find a dataset of example images to train the model on.
If you have further questions let me know.
Hi Christian,
Thank you for your answer.
However, we don't have images. We have the results of several parameters (quantitative or qualitative) that we want to match to a reference result (postive/negative).
Some told me to use Principal component analysis but we don't achieve to get a cut-off combining all the parameters.
Thank you again,
Julien Cabo
There are several open-source machine learning libraries and frameworks that you can use to build and train models for predicting urinary tract infection based on dipsticks and sediment analysis. Some popular options include:
Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides various tools for predictive data analysis. It includes several algorithms for classification, regression, and clustering, making it an excellent choice for building classification models for UTI prediction.
TensorFlow: TensorFlow is a popular machine learning library developed by Google. It is an excellent choice for building deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for UTI prediction.
Keras: Keras is a high-level neural network API written in Python that runs on top of TensorFlow. It is easy to use and provides an intuitive interface for building deep learning models.
Weka: Weka is a collection of machine learning algorithms for data mining tasks. It includes several algorithms for classification, clustering, and regression, making it an excellent choice for building UTI prediction models.
These tools are all free to use and have a large community of users, which means that you can find plenty of tutorials, examples, and support online.
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