{"id":37679062,"url":"https://github.com/connectomicslab/ai-assisted-aneurysm-detection","last_synced_at":"2026-01-16T12:21:46.801Z","repository":{"id":283875345,"uuid":"948939099","full_name":"connectomicslab/AI-Assisted-Aneurysm-Detection","owner":"connectomicslab","description":"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\"","archived":false,"fork":false,"pushed_at":"2025-03-22T18:24:50.000Z","size":296,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-09-10T05:31:55.849Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/connectomicslab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-15T10:00:23.000Z","updated_at":"2025-03-22T18:24:53.000Z","dependencies_parsed_at":"2025-03-22T19:28:37.848Z","dependency_job_id":null,"html_url":"https://github.com/connectomicslab/AI-Assisted-Aneurysm-Detection","commit_stats":null,"previous_names":["connectomicslab/ai-assisted-aneurysm-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/connectomicslab/AI-Assisted-Aneurysm-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/connectomicslab%2FAI-Assisted-Aneurysm-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/connectomicslab%2FAI-Assisted-Aneurysm-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/connectomicslab%2FAI-Assisted-Aneurysm-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/connectomicslab%2FAI-Assisted-Aneurysm-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/connectomicslab","download_url":"https://codeload.github.com/connectomicslab/AI-Assisted-Aneurysm-Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/connectomicslab%2FAI-Assisted-Aneurysm-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28478570,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T11:59:17.896Z","status":"ssl_error","status_checked_at":"2026-01-16T11:55:55.838Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-01-16T12:21:46.723Z","updated_at":"2026-01-16T12:21:46.785Z","avatar_url":"https://github.com/connectomicslab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study\n\u003cp float=\"middle\"\u003e\n  \u003cimg src=\"https://github.com/connectomicslab/AI-Assisted-Aneurysm-Detection/blob/main/images/AI_assisted_scenario.png\" width=\"700\"/\u003e\n\u003c/p\u003e\n\nThis repository contains the code for the paper: \"Assessing workflow impact and clinical utility of AI-assisted brain\naneurysm detection: a multi-reader study\".\n\nThe goal of the work was to assess the diagnostic performance of two radiologists for the task of brain aneurysm detection under\ntwo different settings: 1) Unassisted: normal reading as in clinical routine; 2) AI-assisted: using a \nCAD support system. Additionally, we investigated how the AI CAD tool impacts the clinical workflow.\n\n## Installation/Softwares\nThe results of the paper were obtained with python 3.9 and a Windows OS. Reproducibility for different configurations is not guaranteed.\n\nFor the R scripts, we used RStudio 2022.07.2. For the creation of the overlay dicom series, we used MeVisLab 3.4.2.\n\n### Setup venv/conda environment\nTo run the python scripts:\n1) Clone the repository\n2) 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).\nAlternatively, 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.\n3) Activate your environment:\n```python\n$ source myenv/bin/activate  # if using venv OR\n$ conda activate /miniconda3/envs/myenv  # if using conda or anaconda\n```\n4) Install all required packages with:\n```python\n$ pip install -r requirements.txt\n```\n\n## Data\nThe majority of the dataset used for this study can be downloaded from this\n[OpenNEURO link](https://openneuro.org/datasets/ds003949).\nThe files containing the results of the two readings, both for the junior and senior radiologists,\nare located inside the directory `READINGS`.\n\n## Usage\n### Overlay Series Generation\nThe code used to generate the DICOM overlay series where the segmentations are overlayed on the TOF-MRA volumes is \ncalled `d20221006_export_fused_images.mlab` and is located inside the directory `mevislab_overlay`.\n### Sensitivity and Specificity Analyses\nTo code used to run the McNemar's tests for the sensitivity and specificity analyses presented in the paper is\nlocated in the directory `sensitivity_specificity_analysis_R`\n### Reading time\nThe script used to compare the reading times of the two radiologists with and without the assistance\nof the CAD is called `compare_timing_between_readings.py` and is located inside the directory `reading_time`.\nThe files containing the results of the two readings (which include the reading times) are located inside\nthe directory `READINGS`.\n### Confidence scores\nAll the scripts related to the confidence scores are located in the directory `confidence_score`. \nTo script used to create the barplots that display the confidence scores is `d20240916_confidence_scores_barplots.py`.\nTo script used to run the XYZ test to compare the distributions of confidence scores is `d20240317_compare_confidence_scores.py`\n\n\n## How to cite\nIf you're using our dataset/model, or comparing performances with the ones presented in this work,\nplease cite the two following publications:\n\n        [1] Di Noto, T., Marie, G., Tourbier, S., Alemán-Gómez, Y., Esteban, O., Saliou, G., ... \u0026 Richiardi, J. (2023). Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge. Neuroinformatics, 21(1), 21-34.\n\nand\n        TODO: add (med)-arxiv once it's public\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fconnectomicslab%2Fai-assisted-aneurysm-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fconnectomicslab%2Fai-assisted-aneurysm-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fconnectomicslab%2Fai-assisted-aneurysm-detection/lists"}