Doctoral Student
Satvika Bharadwaj is currently is working on a machine learning-driven algorithm to stratify osteoporotic patients with impaired bone remodeling and is employing algorithms on a combination of image features, including intensity, texture, fractal, and shape descriptors, to classify osteoporotic patients into different bone turnover categories to aid in personalized treatment decisions.
She additionally is applying computational pathology to improve risk classification of breast cancer patients and strengthening prognostic markers through the examination of image characteristics, aiming to overcome the drawbacks of current workflow impacted by inconsistent observer interpretations and high expenses of the Nottingham grading system and Oncotype DX (ODx).
Satvika previously worked as a research associate at the Indian Institute of Science in Bangalore, India and focused on developing a display-less and portable ultrasound system for prenatal care in rural India. The work involved developing and implementing algorithms for 3D fetal volume reconstruction from 2D ultrasound, improving volume-filling algorithms, and devising Obstetric Scanning Protocols for complete fetal volume capture, as well asl conducting comprehensive studies on factors affecting volume quality, including probe stability, pixel-to-voxel reconstruction, scan speed, duration variation, and ultrasound probe contact.
As a graduate student at the University of Cincinnati in Cincinnati, Ohio, Satvika's work focused on empowering disaster relief operations by developing ground-penetrating radar to detect pelvic fractures in collaboration with Cincinnati Children’s Hospital Medical Center and UC’s Biomedical and Civil Department.
An ML-driven algorithm to stratify osteoporotic patients with impaired bone remodeling • Employing algorithms on a combination of image features, including intensity, texture, fractal, and shape descriptors, to classify osteoporotic patients into different bone turnover categories and aid in personalized treatment decisionsAn ML-driven algorithm to stratify osteoporotic patients with impaired bone remodeling • Employing algorithms on a combination of image features, including intensity, texture, fractal, and shape descriptors, to classify osteoporotic patients into different bone turnover categories and aid in personalized treatment decisions
Satvika holds a masters degree in biomedical engineering and an undergraduate engineering degree in medical electronics engineering.