AI Models for Chest Radiograph Analysis: Internal Clinical Trial vs. Gold Standards

Main Article Content

Mr. Pritam Dhalla
Mr. Soham Pal
Mr. Amar Saish

Abstract

Our vision is to develop an AI-based software which is capable of analyzing frontal PA chest X-rays for disease diagnosis, detection, and prediction through the analysis of several different X-ray manifestations and findings which are inter-correlated to arrive to a final result. We utilize state of-the-art AI, thereby facilitating in empowering of the world through our med-tech ecosystem. It is being developed with the intention of enhanced cardiopulmonary care, bridging the gaps in healthcare, by giving conclusive and comprehensive diagnosis at lower costs and reducing the number of unnecessary diagnostic tests which, often, serve as the main cause of the delay between diagnosis and treatment.


Why X-rays? X-rays have been chosen as the input because it is Non-invasive mode of diagnostic test, affordable, accessible and has much lesser radiation exposure compared to other imaging methods of CT, MRI, PET.

Article Details

How to Cite
Dhalla, M. P., Pal, M. S., & Saish, M. A. (2024). AI Models for Chest Radiograph Analysis: Internal Clinical Trial vs. Gold Standards. International Journal of Medical Science and Clinical Research Studies, 4(03), 499–503. https://doi.org/10.47191/ijmscrs/v4-i03-24
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References

I. What is cardiomegaly?: Symptoms, Causes, Diagnosis and treatment. (2022b, October 27). MedPark Hospital. https://www.medparkhospital.com/en-US/disease-and-treatment/what-is-cardiomegaly#:~:text=Cardiomegaly%20or%20an%20enlarged%20heart%20is%20usually%20not%20an%20emergency,neck%2C%20arms%2C%20and%20back.

II. Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., Mak, R. H., Tamimi, R. M., Tempany, C. M., Swanton, C., Hoffmann, U., Schwartz, L. H., Gillies, R. J., Huang, R. Y., & Aerts, H. J. W. L. (2019). Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: a cancer journal for clinicians, 69(2), 127–157. https://doi.org/10.3322/caac.21552

III. American Lung Association. (n.d.). Learn about pneumonia. https://www.lung.org/lung-health-diseases/lung-disease-lookup/pneumonia/learn-about-pneumonia#:~:text=Pneumonia%20is%20an%20infection%20of,to%20get%20into%20your%20bloodstream.Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.

IV. Nguyen, H. Q., Lam, K., Le, L. T., Pham, H. H., Tran, D. Q., Nguyen, D. B., Le, D. D., Pham, C. M., Tong, H. T. T., Dinh, D. H., Do, C. D., Doan, L. T., Nguyen, C. N., Nguyen, B. T., Nguyen, Q. V., Hoang, A. D., Phan, H. N., Nguyen, A. T., Ho, P. H., Ngo, D. T., … Vu, V. (2022). VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations. Scientific data, 9(1), 429. https://doi.org/10.1038/s41597-022-01498-wSpector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullende

V. Amin, H., & Siddiqui, W. J. (2022, November 20). Cardiomegaly. StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK542296/#:~:text=Introduction,radiograph%20or%20a%20computed%20tomography.

VI. Professional, C. C. M. (n.d.). Pneumonia. Cleveland Clinic. https://my.clevelandclinic.org/health/diseases/4471-pneumonia

VII. Çallı, E., Sogancioglu, E., Van Ginneken, B., Van Leeuwen, K. G., & Murphy, K. (2021). Deep learning for chest X-ray analysis: A survey. Medical Image Analysis, 72, 102125. https://doi.org/10.1016/j.media.2021.102125

VIII. Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022). Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare (Basel, Switzerland), 10(3), 541. https://doi.org/10.3390/healthcare10030541