https://github.com/milerius/tender_samet

https://github.com/milerius/tender_samet

Last synced: about 1 year ago
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• Host: GitHub
• URL: https://github.com/milerius/tender_samet
• Owner: Milerius
• License: mit
• Created: 2019-03-30T10:53:46.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T00:47:53.000Z (over 3 years ago)
• Last Synced: 2025-03-24T03:55:25.012Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 32.9 MB
• Stars: 1
• Watchers: 0
• Forks: 4
• Open Issues: 41
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Smart lung disease diagnosis and personalized follow-up

## Tagline

**AI² – Smart lung diagnosis and personalized follow-up**

![logo](assets/Logo.png)

## 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.)

## Link to the one page article

[Click here](one_page_article.md) for the more detailed approach

## Additional Resources

![text](assets/Smart_lung_disease_diagnosis_and_personalized_follow-up.png)

### XRay Analysis algorithm details

![algorithm](assets/algorithm_diagram.png)

**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.

## Technical Specifications

[Click here](technical_specifications.md) for the technical specifications

## Installations & Reproduction Detailed Instructions

[Click here](INSTALLATION.md) to access installation instructions

[Click here](REPRODUCIBILITY.md) to access reproducibility instructions

## Team Members

![Team](assets/AI_Hackathon_dream_team.JPG)

Benedikt Jordan [Linkedin](https://www.linkedin.com/in/benedikt-jordan-9b068b9a/), Benedikt.jordan@posteo.de

Anass Bellachehab [Linkedin](https://www.linkedin.com/in/anass-bellachehab-a89baa8a/), ans.bellache@gmail.com

Allwyn Joseph [Linkedin](https://www.linkedin.com/in/allwyn-joseph/), allwyn@azmed.co

Roman Sztergbaum [Linkedin](https://www.linkedin.com/in/roman-sztergbaum), [Github](https://github.com/Milerius), rmscastle@gmail.com

Noémie Héroin [Linkedin](https://www.linkedin.com/in/noemie-heroin), noemie.heroin@gmail.com