An AI-based smart positioning web app for X-ray workflows.
PS : National winner 🏆 at Phillips Code to Care Challenge'21
Problem Statement: AI to evaluate the overall patient body position meeting clinical examination requirement.
The optimality of the CXR examinations reaches to 50%, and that is due to several reasons, most importantly related positioning error 53%, technical error with 30% and communication error with 17%
With estimates of average diagnostic error rates ranging from 3% to 5%, there are approximately 40 million diagnostic errors involving imaging annually worldwide.
- High X-Ray Exposure on patients due to multiple retakes.
- On an average takes 5-7 mins per X-Ray image acquisition.
- Requires a minimum of 2 technicians and a radiologist for every X-Ray acquisition.
Solution: A smart positioning collimator that reduces manual work, exposure radiation and improves accuracy.
Patient image is acquired and sent to the Cloud for detecting positioning errors.
- Patient coordinates received are then assessed using Pose Detection.
- Based on pose detected, current position would be analysed using an AI PoseNet Algorithm.
- Optimal bucky height and collimation area is estimated based on patient’s physique.
- The overall feedback regarding positioning error is provided to both patient and the radiologist via a voice assistant.
- Helps in correcting positioning errors prior to image acquisition
- Reducing the time and additional dose associated with a repeat exposure while improving image consistency.(X-ray Retakes)
- Its automated features also decrease the time that a radiographer needs to be in close contact with patients for positioning — a huge benefit in the presence of infectious diseases.
- Realtime auto upgradation ,storage of images and further datasets for improving the trained models and research can be done in a cloud based architecture easily as compared to Hardware solutions.
- Can be integrated with Philips Radiology Smart Assistant for further analysing the x-ray acquired.
- A similar smart workflow can be implemented with Ultrasonic system detection.