The medical imaging landscape is constantly evolving, with new AI-powered tools being developed to improve diagnostic accuracy and clinical decision-making. However, the process of deploying these tools in clinical research and trials can be time-consuming and complicated. The Medimsight platform is designed to streamline this process, allowing researchers and developers to create, test, and deploy AI tools quickly and efficiently.
Creating an App on Medimsight: Medimsight platform allows for easy integration of AI tools using XML for the user interface and Python scripts to handle the processing. To create an app, you'll need an XML file to define the UI and a Python script to execute the algorithm.
- XML for the UI: The XML file defines the user interface for selecting input data and specifying output results. It includes fields such as input type, class, name, and ID, which are used to customize the app's functionality and appearance.
- Python Script: The Python script is responsible for processing the input data, running the AI algorithm, and generating the output results. It can utilize popular libraries like ITK and Docker to perform complex tasks, such as loading DICOM images, applying filters, and running containerized algorithms.
For example, to use ITK for processing DICOM images in your Python script, you can write the following code snippet:
import itk
# Load a DICOM image
def loadDICOM(path):
readerType = itk.ImageSeriesReader[itk.Image[itk.F, 3]]
reader = readerType.New()
dicomIO = itk.GDCMImageIO.New()
reader.SetImageIO(dicomIO)
reader.SetFileNames(itk.GDCMSeriesFileNames.New().SetDirectory(path).GetFileNames())
reader.Update()
return reader.GetOutput()
# Apply a median filter
def apply_median_filter(image):
median_filter = itk.MedianImageFilter.New()
median_filter.SetInput(image)
median_filter.Update()
return median_filter.GetOutput()
input_image_path = mdsutil.get_input("input_image")
input_image = loadDICOM(input_image_path)
output_image = apply_median_filter(input_image)
The Medimsight platform's seamless integration with cloud-based resources, such as Google Cloud, enables rapid deployment and testing of AI tools. Developers can easily upload their XML and Python files, and the platform will take care of the rest, including generating user interfaces and handling data storage and processing. This accelerates the development process and ensures that AI tools are available for use in clinical research and trials as quickly as possible.
The Medimsight platform offers a fast and efficient way to develop, test, and deploy AI-powered tools in medical imaging for clinical research and trials. By streamlining the process and providing a user-friendly interface with powerful libraries like ITK, Medimsight enables developers and researchers to focus on creating innovative solutions to improve patient care and clinical decision-making. Get started with the Medimsight platform today and bring your AI tools to the market quickly and efficiently.
