The shortage of radiologists and the burnout of physicians create an urgent demand for immediate solutions. In this project, we will build the open-source human-centered medical imaging diagnosis tool (called i-RadioDiagno) which adds the prior knowledge of radiologists (e.g., radionics) into the deep learning algorithms to enable automatic or semi-automatic generation of diagnosis notes based on medical images. i-RadioDiagno will be integrated and tested in the clinical practice to enable radiologists and machines to work together by providing feedback loops to improve accuracy and adaptive learning. It will be built on Amazon SageMaker and Apache MXNet on AWS.
i-RadioDiagno is an open-source tool built on Amazon SageMaker Platform that integrates the existing radiomic feature extraction tools, and embeds it into the workflow of clinical decision support system (CDSS) to facilitate the diagnosis process of radiologists.
The user-friendly interface of i-RadioDiagno will allow radiologists to view the radiomic features and calculation in a visual manner, and their diagnosis procedure will become just several mouse clicks based on the pick lists generated by i-RadioDiagno and the diagnosis notes can be created semi-automatically.
By using i-RadioDiagno, all the information or footsteps during the diagnosis process of radiologists are captured, stored, and turned into high-quality labels for medical images which are critical for the AI-powered medical imaging diagnosis.
In this project, we will develop the open source tool called i-RadioDiagno which has the following components:
1) Radiomic feature extraction
2) Image to report model
3) feedback loop through clinical practice
