X-Ray Assistant

What does it do?

The X-Ray Assistant helps physicians create radiographic reports agile, versatile, and customizable. You can use templates based on anatomical images and voice recognition technology to make your report faster.

The technology stack used.

Programming Language

The software was built using Python as a programming language because it has high introspection. In computer programming, introspection is the ability to determine the type of an object at runtime. It is one of Python’s strengths. Everything in Python is an object and we can examine those objects. So we were able to build virtual human bones, organs as heart, and tissues.

Speech Recognition

To convert speech into text using an application programming interface (API) called Speech Recognition powered by Google’s AI technologies was used. The API is able to Transcribe your content in real-time or from stored files using advanced deep learning neural network algorithms for automatic speech recognition.

Framework

To build the graphical user interface (GUI) for allows users to interact with the X-Ray Assistant through graphical icons and audio indicator was used wxPython that is a cross-platform GUI toolkit for the Python programming language. It allows Python programmers to create programs with a robust, highly functional graphical user interface, simply and easily. It is implemented as a set of Python extension modules that wrap the GUI components of the popular wxWidgets cross-platform library, which is written in C++.

Like Python and wxWidgets, wxPython is Open Source, which means that it is free for anyone to use and the source code is available for anyone to look at and modify. Currently Supported platforms are Microsoft Windows, Mac OS X and macOS, and Linux or other Unix-like systems with GTK2 or GTK3 libraries. In most cases, the native widgets are used on each platform to provide a 100% native look and feel for the application.

About X-Ray Assistant first steps

The application was developed during my course of improvement in radiology and diagnostic imaging, because after a wide search for a versatile and free tool, I couldn't find, with the practical features that I had in mind, that at the same time integrate anatomy and reporting. So the idea for a virtual assistant was born in my mind and heart.

Acknowledgment

My sincere thanks to Robin Dunn for his work and disposition of this fantastic and intuitive cross-platform GUI toolkit for Python, Tim Roberts for their patience and readiness to answer my questions during the software development, and Dr. Hamza Mu for help publish the project.