Web-Based Video Analysis and Visualization of Magnetic Resonance Imaging Reports for Enhanced Patient Understanding

Authors

  • D. Shanthi
  • G. Ashok
  • Bontha Vennela
  • K. Harika Reddy
  • P. Deekshitha
  • U. Bala Sri Bhavya Nandini

Keywords:

MRI analysis, Medical Imaging, AI in healthcare, Image Processing, Google Custom Search API, OCR, NLP in Medical

Abstract

Magnetic Resonance Imaging (MRI) is an age-old diagnostic imaging technique in modern medicine, but the technical nature of MRI reports makes patients incapable of understanding their own healthcare information. In this paper, an AI-based web portal fills this gap by translating and interpreting MRI reports in natural language processing and multimedia content. The system begins with the image or PDF upload process with the content text extracted with Tesseract OCR and PyMuPDF. Regular Expressions and NLP models—that include facebook/bart-large-cnn for summarization and KeyBERT for keyword extraction—are used by the system to break down medical jargon into plain language. Enrichment is also encouraged with the use of the Google Custom Search API to identify key words and related medical references. Final interpretation is also offered by way of interactive audio-visual content by virtue of the use of gTTS for voice-over and moviepy for video creation. The process encourages patient understanding, provides multilingual accessibility, and allows more open healthcare communication.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Pradeeban Kathiravelu, Nishchal Singi. "Visualizing Scanner Utilization From MRI Metadata and Clinical Data." IEEE Computer, 2023.

Vishakha Pareek, Shreyansh Sharma. "Patient 3D Data Visualization with AR- based Interactive Technology for Brain MRI." IEEE Conference Publication, 2023.

Engin Video Mendi, Coskun Bayrak. "A Web-Based Indexing Environment." IEEE Conference Publication, 2010.

Medical M.J. McAuliffe, F.M. Lalonde. "Medical Image Processing, Analysis and Visualization in Clinical Research." IEEE Conference Publication, 2001.

Naeema Ziyad, Salih Yoosaf. "Visualizing MRI & CT ScansUsing Mixed Reality." IEEE Xplore, 2023.

Oda O. Nedrejord, Vajira Thambawita. "Vid2Pix- A Framework for Generating High-Quality Synthetic Videos." IEEE International Symposium on Multimedia, 2020.

MedVidCL Dataset: This dataset comprises 6,617 videos categorized into 'medical instructional', 'medical non-instructional', and 'non-medical' classes, designed to aid in medical video classification tasks.

MedVidQA Dataset: Containing 3,010 health-related questions with corresponding video timestamps, this dataset supports research in medical visual answer localization

HealthVidQA Datasets: These datasets, HealthVidQA-CRF and HealthVidQA-Prompt, are designed for answering health-related questions using medical videos, facilitating research in medical visual answer localization.

Deepak Gupta, Kush Attal, Dina Demner-Fushman. "A Dataset for Medical Instructional Video Classification and Question Answering." arXiv preprint arXiv:2201.12888, 2022

Syed Abdul Mateen, Niharika Malvia, Syed Abdul Khader, Danny Wang, Deepti Srinivasan, Chi-Fu Jeffrey Yang, Lana Schumacher, Sandeep Manjanna. "Thoracic Surgery Video Analysis for Surgical Phase Recognition." arXiv preprint arXiv:2406.09185, 2024.

Deepak Gupta, Kush Attal, Dina Demner-Fushman. "Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches." arXiv preprint arXiv:2309.12224, 2023. [13]

[13]David C. Wong, Stefan Williams. "Artificial Intelligence Analysis of Videos to Augment Clinical Assessment: An Overview." Journal of Medical Artificial Intelligence, vol. 1, no. 1, pp. 1-10, 2024 .

"A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques." Applied Sciences, vol. 13, no. 11, article 6582, 2023.

Medical Imaging Datasets: A curated list of medical imaging datasets available on GitHub, useful for research and development in medical image analysis.

P. K. Bolisetty and Midhunchakkaravarthy, "Comparative Analysis of Software Reliability Prediction and Optimization using Machine Learning Algorithms," 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN), Bidar, India, 2025, pp. 1-4, doi: 10.1109/ICISCN64258.2025.10934209.

D. Shanthi, R. K. Mohanty and G. Narsimha, "Application of machine learning reliability data sets", Proc. 2nd Int. Conf. Intell. Comput. Control Syst. (ICICCS), pp. 1472-1474, 2018.

D Shanthi, "Smart Water Bottle with Smart Technology", Handbook of Artificial Intelligence, Benthem Science Publishers, Pg. no: 204-219, 2023

Downloads

Published

2025-05-13

How to Cite

1.
D. Shanthi DS, G. Ashok GA, Vennela B, Reddy KH, P. Deekshitha PD, Nandini UBSB. Web-Based Video Analysis and Visualization of Magnetic Resonance Imaging Reports for Enhanced Patient Understanding. J Neonatal Surg [Internet]. 2025May13 [cited 2025Sep.19];14(23S):280-5. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/5733

Issue

Vol. 14 No. 23S (2025): Journal of Neonatal Surgery

Section

Original Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.