https://github.com/connectomicslab/ai-assisted-aneurysm-detection
This repository contains the code used for the paper "Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study"
https://github.com/connectomicslab/ai-assisted-aneurysm-detection
Last synced: 5 months ago
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This repository contains the code used for the paper "Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study"
- Host: GitHub
- URL: https://github.com/connectomicslab/ai-assisted-aneurysm-detection
- Owner: connectomicslab
- License: apache-2.0
- Created: 2025-03-15T10:00:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-22T18:24:50.000Z (about 1 year ago)
- Last Synced: 2025-09-10T05:31:55.849Z (9 months ago)
- Language: Python
- Size: 289 KB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study
This repository contains the code for the paper: "Assessing workflow impact and clinical utility of AI-assisted brain
aneurysm detection: a multi-reader study".
The goal of the work was to assess the diagnostic performance of two radiologists for the task of brain aneurysm detection under
two different settings: 1) Unassisted: normal reading as in clinical routine; 2) AI-assisted: using a
CAD support system. Additionally, we investigated how the AI CAD tool impacts the clinical workflow.
## Installation/Softwares
The results of the paper were obtained with python 3.9 and a Windows OS. Reproducibility for different configurations is not guaranteed.
For the R scripts, we used RStudio 2022.07.2. For the creation of the overlay dicom series, we used MeVisLab 3.4.2.
### Setup venv/conda environment
To run the python scripts:
1) Clone the repository
2) Create a venv/conda environment. If you are not familiar with pip/conda environments, please check out the [official documentation](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
Alternatively, feel free to use your favorite [IDE](https://en.wikipedia.org/wiki/Integrated_development_environment) such as [PyCharm](https://www.jetbrains.com/pycharm/download/#section=linux) or [Visual Studio Code](https://code.visualstudio.com/) to set up an environment.
3) Activate your environment:
```python
$ source myenv/bin/activate # if using venv OR
$ conda activate /miniconda3/envs/myenv # if using conda or anaconda
```
4) Install all required packages with:
```python
$ pip install -r requirements.txt
```
## Data
The majority of the dataset used for this study can be downloaded from this
[OpenNEURO link](https://openneuro.org/datasets/ds003949).
The files containing the results of the two readings, both for the junior and senior radiologists,
are located inside the directory `READINGS`.
## Usage
### Overlay Series Generation
The code used to generate the DICOM overlay series where the segmentations are overlayed on the TOF-MRA volumes is
called `d20221006_export_fused_images.mlab` and is located inside the directory `mevislab_overlay`.
### Sensitivity and Specificity Analyses
To code used to run the McNemar's tests for the sensitivity and specificity analyses presented in the paper is
located in the directory `sensitivity_specificity_analysis_R`
### Reading time
The script used to compare the reading times of the two radiologists with and without the assistance
of the CAD is called `compare_timing_between_readings.py` and is located inside the directory `reading_time`.
The files containing the results of the two readings (which include the reading times) are located inside
the directory `READINGS`.
### Confidence scores
All the scripts related to the confidence scores are located in the directory `confidence_score`.
To script used to create the barplots that display the confidence scores is `d20240916_confidence_scores_barplots.py`.
To script used to run the XYZ test to compare the distributions of confidence scores is `d20240317_compare_confidence_scores.py`
## How to cite
If you're using our dataset/model, or comparing performances with the ones presented in this work,
please cite the two following publications:
[1] Di Noto, T., Marie, G., Tourbier, S., Alemán-Gómez, Y., Esteban, O., Saliou, G., ... & Richiardi, J. (2023). Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge. Neuroinformatics, 21(1), 21-34.
and
TODO: add (med)-arxiv once it's public