Transforming Global health with AI (TOGAI)

Transforming Global health with AI (TOGAI) is a research group within NAAMII led by Bishesh Khanal. This group focuses on theoretical and applied research in Machine learning (ML) that has the potential to transform global health. We closely collaborate with other themes within NAAMII and institutions outside NAAMII and explore a wide range of topics; all of these topics have a core machine learning component and global health applications. Our topics range from fundamental biological questions involving geometric deep learning for genomics and proteomics applied to diseases such as Tuberculosis (in collaboration with Bionformatics team) to low-cost smartphone-based diagnostic devices (in collaboration with computer vision team, and other institutions such as KIAS). We also strive to push the boundaries of ML theory such as semi-supervised learning and geometric deep learning etc. that have potential to tackle challenging problems in healthcare with a focus on applications that are directly relevant to Low-Income Countries (LICs). We believe that there are several unmet clinical needs of resource-constrained regions such as rural areas of Nepal that could benefit from the development of  advanced ML algorithms. We actively seek to find such problems by immersing and interacting in this environment. The advancement of the technology should prioritize devices or tools that will be low cost so that the solutions are accessible to the poor people who are unable to afford expensive health care costs. Priority areas: Ultrasound, X-rays, smart-phone, Bioinformatics, EEG, and ECG.

Latest Related Publications

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Kiran Raj Pandey, Anup Subedee, Bishesh Khanal, Bhagawan Koirala
COVID-19 control strategies and intervention effects in resource limited settings: a modeling study
PLOS ONE, 2021
Bibtex

@article{pandey2021covid,
  title={COVID-19 control strategies and intervention effects in resource limited settings: a modeling study},
  author={Pandey, Kiran Raj and Subedee, Anup and Khanal, Bishesh and Koirala, Bhagawan},
  journal={Plos one},
  volume={16},
  number={6},
  pages={e0252570},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA}
}

E Skelton, J Matthew, Y Li, Bishesh Khanal, JJ Cerrolaza Martinez, N Toussaint, C Gupta, C Knight, B Kainz, JV Hajnal, M Rutherford
Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison
Radiography (Journal), 2021
Bibtex

@article{SKELTON2021519,
title = {Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison},
journal = {Radiography},
volume = {27},
number = {2},
pages = {519-526},
year = {2021},
issn = {1078-8174},
doi = {https://doi.org/10.1016/j.radi.2020.11.006},
url = {https://www.sciencedirect.com/science/article/pii/S1078817420302352},
author = {E. Skelton and J. Matthew and Y. Li and B. Khanal and J.J. {Cerrolaza Martinez} and N. Toussaint and C. Gupta and C. Knight and B. Kainz and J.V. Hajnal and M. Rutherford},
keywords = {Clinical evaluation, Fetal imaging, Quality assessment, Ultrasound},
}

Bidur Khanal, Lavsen Dahal, Prashant Adhikari, Bishesh Khanal
Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression
Medical Imaging, Computer Vision, Machine Learning, Artificial Intelligence Computational Methods and Clinical Applications for Spine Imaging. MICCAI 2019 CSI Workshop & Challenge, Shenzen, China , 2019
Bibtex

@article{khanal2019automatic,
  title={Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression},
  author={Khanal, Bidur and Dahal, Lavsen and Adhikari, Prashant and Khanal, Bishesh},
  url={arXiv preprint arXiv:1910.14202},
  year={2019},
  maintitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019},
  booktitle = {Computational Methods and Clinical Applications for Spine Imaging. CSI Workshop and Challenge}
}

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