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Cardiac problems in pregnant women are associated with a case-fatality of 40% in in India.  This is due to delays in assessment and referral of symptomatic patients from resource-limited hospitals because of non-availability of cardiologists and/or echocardiography facilities. Democratisation of echocardiography can save the lives of pregnant women with cardiac problems in low-and-middle income countries (LMICs). Hand-held ultrasound devices can facilitate this, however technological advancement or improved accessibility of the devices has not translated in their clinical use in LMICs where they are needed. To facilitate prompt diagnosis, we developed and validated a 4-step FoCUS Solutions (DOI: 10.1016/j.echo.2022.07.014) for echocardiography of pregnant women using low-cost hand-held cardiac ultrasound devices. The scale of its clinical use could be enhanced by Artificial Intelligence (AI) enabled image analysis and decision support software to trigger referrals in real-time. This can make a life-saving difference in resource-limited settings in India and other LMICs. We have ~7000 cardiac images from ~1000 pregnant women obtained using the system from 10 hospitals across India, and our image-bank is still growing. The research objectives are:

  1. Development of AI methods that could support referral decision making through FoCUS Solutions in low-resource settings.
  2. Development of an AI prototype for 2-3 cardiac parameters.

Potential AI methods will include: explainable AI, affordable AI, AI for low computational resources, etc.


The student will have also opportunity to attend research seminars offered at NDPH, the BDI and the Institute of Biomedical Engineering (IBME) as B. Papiez (co-supervisor) is a member of Imaging Hub at the IBME ( Attendance at seminars, workshops and courses provided by the Department and University will also be encouraged. There will be an opportunity to present research work at relevant international/national conferences, and publish papers from the research.


The ideal student would have:

  • a degree in computer science, statistics, engineering or a related discipline;
  • strong programming skills (preferable Python, or Matlab/C++ and willing to learn Python);
  • experience or interest in machine learning (Deep Learning) and medical image analysis;
  • experience or enthusiasm to work on clinically relevant problems.