Smart lung disease diagnosis and personalized follow-up

Tagline

AI² – Smart lung diagnosis and personalized follow-up

Summary

Our innovative solution aims to leverage AI (machine learning) on two streams (AIxAI=AI²), lung patient diagnostic, and hospital administrative workflow, to both streamline specialist workload and patient experience.

We are improving these processes by:

using a prediction model based on lung X-rays, to determine a lung patient pathology
automating coordination between diagnosis and booking assistant tools (CRM), to propose a relevant personalized follow-up appointment with a lung specialist

We also remove friction points in the process by using user-friendly app for the patient (e.g. to set reminders, localization of appointment, etc.)

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Additional Resources

XRay Analysis algorithm details

Ressources: CheXpert: Irvin, Jeremy, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, und Katie Shpanskaya. „CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison“. arXiv preprint arXiv:1901.07031, 2019.

ResNet: He, Kaiming, Xiangyu Zhang, Shaoqing Ren, und Jian Sun. "Deep residual learning for image recognition". In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778, 2016.

We tried the VGG network, but it didn´t perform as well: Simonyan, Karen, und Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556, 2014.