Meet our SPARK Fellows
Introducing Cohort 8
Alex Barker, PhD
Automated Deep Learning Approach to Enable Detection of In-utero Disease
Email Address:alex.barker@cuanschutz.edu
Automated Deep Learning Approach to Enable Detection of In-utero Disease
This project aims to automate and improve the accuracy of fetal weight estimation and anatomical measurements using 3D MRI imaging, addressing the limitations of traditional 2D ultrasound methods. The approach has shown promising results across multiple MRI vendor platforms and will be refined further towards our goal of achieving FDA approval and clinical use.
Learn more about Dr. BarkerCohort 7
Cohort 6
Alex Barker, PhD
Automated Deep Learning Approach to Enable Detection of In-utero Disease
Email Address:alex.barker@cuanschutz.edu
Automated Deep Learning Approach to Enable Detection of In-utero Disease
This project aims to automate and improve the accuracy of fetal weight estimation and anatomical measurements using 3D MRI imaging, addressing the limitations of traditional 2D ultrasound methods. The approach has shown promising results across multiple MRI vendor platforms and will be refined further towards our goal of achieving FDA approval and clinical use.
Learn more about Dr. BarkerCohort 5
Alex Barker, PhD
Automated Deep Learning Approach to Enable Detection of In-utero Disease
Email Address:alex.barker@cuanschutz.edu
Automated Deep Learning Approach to Enable Detection of In-utero Disease
This project aims to automate and improve the accuracy of fetal weight estimation and anatomical measurements using 3D MRI imaging, addressing the limitations of traditional 2D ultrasound methods. The approach has shown promising results across multiple MRI vendor platforms and will be refined further towards our goal of achieving FDA approval and clinical use.
Learn more about Dr. Barker