BioE Ph.D. Defense Presentation- Likhit Nayak

Advisor:  Rudolph L. Gleason, Ph.D. (Woodruff School of Mechanical Engineering, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology)

Thesis Committee:

J. Brandon Dixon, Ph.D.  (Woodruff School of Mechanical Engineering, Georgia Institute of Technology)

May D. Wang, Ph.D. (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology)

Wilbur A. Lam, M.D., Ph.D. (Wallace H. Coulter Department of Biomedical Engineering, Emory School of Medicine & Georgia Institute of Technology)

Michael J. Weiler, Ph.D. (CEO, LymphaTech Inc.)

Efficient longitudinal modeling of the 3D shape of women during gestation and its value in assessing the risk of cephalopelvic disproportion (CPD).

Cephalopelvic disproportion (CPD) is a mismatch in the size of the maternal pelvis and the fetus, which often leads to obstructed labor. Most cases of CPD require C-section for successful delivery and in low resource settings like Ethiopia, there is a lack of adequate facilities with the infrastructure or the expertise to perform a C-section. Currently, obstructed labor is known to account for 11 – 22 % of maternal deaths in Ethiopia. Early assessment of the risk of CPD would enable women in these settings to access the proper healthcare services and improve overall maternal health. This thesis aims to develop an algorithm that would use longitudinal shape modeling to analyze, in real-time, 3D scans of pregnant women and assess the risk of CPD-related obstructed labor at the earliest possible stages of gestation. The longitudinal shape model would be trained on 3D scans of pregnant women across different periods of gestation and would be optimized to run on devices with low computational power. The prognostic value of the model for assessing the risk of CPD would be compared to anthropometric measurements. This model is envisioned to be used by nurses and midwife personnel as part of point-of-care tools for routine antenatal care in low-resource settings.

MS Teams Meeting ID: 258 046 490 033

Passcode: WGaQw6