MIA

Analyzing Chest Radiography images utilizing PyTorch and OpenCV, with insights from relevant research papers

Project Organization

├── data
│   ├── external                <- Data from third party sources.
│   ├── interim                 <- Intermediate data that has been transformed.
│   ├── processed               <- The final, canonical data sets for modeling.
│   └── raw                     <- The original, immutable data dump.
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├── docs                        <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                      <- Trained and serialized models, model predictions, or model summaries
│   └── Carmine400i70a.h5       <- 70% accuracy for detecting pneumonia on posteroanterior xrays
│
├── notebooks                   <- Jupyter notebooks. Naming convention is a number (for ordering),
│                                   the creator's initials, and a short `-` delimited description, e.g.
│                                   `1.0-jqp-initial-data-exploration`.
│
├── references                  <- Data dictionaries, manuals, and all other explanatory materials.
│   └── research_papers         <- Academic research papers in pdf format
│
├── reports                     <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures                 <- Generated graphics and figures to be used in reporting
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├── src                         <- Source code for use in this project.
│   ├── __init__.py             <- Makes src a Python module
│   │
│   ├── data                    <- Scripts to download or generate data
│   │   └── make_dataset.py     <- Scripts to turn raw data into features for modeling
│   │
│   ├── features                <- Scripts to turn raw data into features for modeling
│   │   ├── build_features.py   <- Script to create 800 image dataset of diagnosing pneumonia
│   │   └── create_sliced.py    <- Script to create sliced csv dataset
│   │
│   ├── models                  <- Scripts to train models and then use trained models to make
│   │   │                          predictions
│   │   ├── predict_model.py    <- Script to use Carmine400 model to predict pneumonia on an image
│   │   └── train_model.py      <- Script to train Carmine400 model on 400 images to detect pneumonia on chest xrays
│   │
│   ├── tests                   <- Scripts to run automated test
│   │   ├── carmine_test.py     <- Script to test carmine models
│   │   └── tangerine.py        <- Script to test tangerine models
│   │
│   └── visualization           <- Scripts to create exploratory and results oriented visualizations
│       ├── exploratory.py      <- Script to do exploratory data analysis
│       ├── image_count.py      <- Script to count number of images in "raw/img"
│       └── visualize.py        <- Script to visualize chest xray with openCV
│
├── LICENSE
├── Makefile                    <- Makefile with commands like `make data` or `make train`
├── README.md                   <- The top-level README for developers using this project.
│
│
├── requirements.txt            <- The requirements file for reproducing the analysis environment, e.g.
│                                   generated with `pip freeze > requirements.txt`
│
├── setup.py                    <- makes project pip installable (pip install -e .) so src can be imported
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└── tox.ini                     <- tox file with settings for running tox; see tox.readthedocs.io

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Dataset Description

• BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
• The findings are mapped onto standard Unified Medical Language System (UMLS) terminology and they cover a wide spectrum of thoracic entities, contrasting with the much more reduced number of entities annotated in previous datasets.
• Images are stored in high resolution and entities are localized with anatomical labels in a Medical Imaging Data Structure (MIDS) format.
• In addition, 23 images were annotated by a team of expert radiologists to include semantic segmentation of radiographic findings.
• Moreover, extensive information is provided, including the patient’s demographic information, type of projection and acquisition parameters for the imaging study, among others.
• These iterations of the database include 21342 CR, 34829 DX and 7918 CT studies.
• Link: https://github.com/BIMCV-CSUSP/BIMCV-COVID-19

Insights and Conclusions

• AI, CAD, DL systems alone seem to have a higher Sensitivity but lower Specificity than Raidologists

• General Practitioners benefit most from CADs

• CADs lower Turn around Time and boost overall accuracy when used by doctors

• Explainable AI and Causal Inference will need to be worked on as DL systems are usually black boxes

• Could add different parameters to model such as biomarkers, NLR (neutrophil-to-lymphocyte ratio), Basic Fibroblast Growth Factor (bFGF), Insulin-like Growth Factor (IGF-R), age, blood pH, to boost accuracy

Research Citations

An, J. Y., Hwang, E. J., Nam, G., Lee, S. H., Park, C. M., Goo, J. M., & Choi, Y. R. (2024). Artificial Intelligence for assessment of endotracheal tube position on chest radiographs: Validation in patients from two institutions. American Journal of Roentgenology, 222(1). https://doi.org/10.2214/ajr.23.29769

Shin, H. J., Kim, M. H., Son, N., Han, K., Kim, M. J., Kim, Y. C., Park, Y. S., Lee, E. H., & Kyong, T. (2023). Clinical implication and prognostic value of Artificial-Intelligence-Based results of chest radiographs for assessing clinical outcomes of COVID-19 patients. Diagnostics, 13(12), 2090. https://doi.org/10.3390/diagnostics13122090

Hwang, E. J., Kim, K. B., Kim, J. Y., Lim, J., Nam, J. G., Choi, H., Kim, H., Yoon, S. H., Goo, J. M., & Park, C. M. (2021). COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system. PLOS ONE, 16(6), e0252440. https://doi.org/10.1371/journal.pone.0252440

Jang, S. B., Lee, S. H., Lee, D., Park, S., Kim, J. K., Cho, J. W., Cho, J., Kim, K. B., Park, B., Park, J., & Lim, J. (2020). Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study. PLOS ONE, 15(11), e0242759. https://doi.org/10.1371/journal.pone.0242759

Pan, Y., Chen, Q., Chen, T., Wang, H., Zhu, X., Fang, Z., & Lü, Y. (2019). Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays. European Spine Journal, 28(12), 3035–3043. https://doi.org/10.1007/s00586-019-06115-w

Hong, S., Hwang, E. J., Kim, S., Song, J., Lee, T., Jo, G. D., Choi, Y., Park, C. M., & Goo, J. M. (2023). Methods of Visualizing the results of an Artificial-Intelligence-Based Computer-Aided Detection System for chest radiographs: Effect on the diagnostic performance of radiologists. Diagnostics, 13(6), 1089. https://doi.org/10.3390/diagnostics13061089

Lee, J. H., Ahn, J. S., Chung, M. J., Jeong, Y. J., Kim, J. H., Lim, J., Kim, J. Y., Kim, Y. J., Lee, J. E., & Kim, E. Y. (2022). Development and validation of a Multimodal-Based Prognosis and Intervention Prediction model for COVID-19 patients in a multicenter cohort. Sensors, 22(13), 5007. https://doi.org/10.3390/s22135007

Hwang, E. J., Kim, H., Yoon, S. H., Goo, J. M., & Park, C. M. (2020). Implementation of a Deep Learning-Based Computer-Aided detection system for the interpretation of chest radiographs in patients suspected for COVID-19. Korean Journal of Radiology, 21(10), 1150. https://doi.org/10.3348/kjr.2020.0536

Libraries and Frameworks Used

Pytorch | Scikit-learn | Statsmodels | Pandas | OpenCV | Cookiecutter | Streamlit

To-be added

GCP | Statistics | Multithreading | Causal Inference | PGMs | Explainable AI | Prefect | AUC | Specificity | Sensitivity | NPV | PPV | Confusion Matrix | Metrics | Docker

Project based on the cookiecutter data science project template. #cookiecutterdatascience