Ge Precision Healthcare LLC

Rohit Pardasani

Hariharan Ravishankar

Abhijit Vishwas Patil

John Michael Jordan

Renee L Vitullo

Siddharth Ajith

Hrithik Auchar

Methods and systems are provided for generating respiratory support recommendations. In one embodiment, a method includes extracting imaging features from patient imaging information for a patient, extracting non-imaging features from patient clinical data of the patient, entering the imaging features and the non-imaging features to a joint model trained to output respiratory support recommendations as a function of the imaging features and the non-imaging features, and displaying one or more respiratory support recommendations output by the joint model.

Systems and methods for respiratory support recommendations

Techniques are described for performing fetal heart rate (FHR) analytics using machine learning techniques. According to an embodiment, computer-implemented method comprises training a machine learning model using a supervised machine learning process to identify patterns in training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data. The method further comprises receiving new cardiotocograph data for a fetus and mother in real-time over a period of labor and applying the machine learning model to the new cardiotocograph data as it is received to identify the patterns in the new cardiotocograph data.

Deep learning based fetal heart rate analytics

Automated assessment for a fetus may be applied based on imaging data obtained during medical imaging examination of the fetus, with the applying including processing imaging data corresponding to a plurality of a cross-section imaging slices corresponding to a limb of the fetus, where the processing includes for each imaging slice: automatically generating a predicted outer mask for an outer contour of the limb based on application of a first pre-trained model to imaging data corresponding to the imaging slice; and automatically generating a segmentation of fat-lean mask for the imaging slice based on application of a second pre-trained model to both of the imaging data corresponding to the imaging slice and the generated predicted output mask; and applying based on the processing of the imaging data corresponding to the plurality of a cross-section imaging slices: a fractional limb volume assessment; and a fat-lean mass assessment.

Growing community of inventors

Bengaluru, India

Rohit Pardasani