Automated Biometry

About Us

The Automated Fetal Head Classification and Segmentation System is a groundbreaking innovation in fetal ultrasound imaging. Using machine learning algorithms, such as ALEXNET and UNET, our system achieves 96% accuracy in estimating gestational age within a one-week margin. This innovation addresses the global shortage of skilled sonographers and reduces procedure time, delivering precise results independent of operator expertise.

Our mission is to revolutionize maternal care, especially in low- and middle-income countries (LMICs), by providing an affordable, accurate, and automated tool for fetal biometry.

Meet the Team

Dr. Khalid Rasheed

Founder & Principal Investigator

Expert in Medical Imaging, Machine Learning, and Computer Vision, with over 15 years of experience.

Clive Madamombe

CEO & Co-Founder

Muhammad Yasir Junaid

Lead Software Developer

Senior Software Engineer specializing in Java, C++, and high-performance systems.

Umme Hamna

AI/ML Specialist

Expert in AI/ML, DSP, and embedded systems with experience in computer vision solutions.

Muhammad Rafay

Systems Engineer

Specialist in robotics, FPGA systems, and AI integration for hardware-software synergy.

Our Research

Our innovation has been featured in various peer-reviewed journals and conferences, showcasing its potential to transform maternal healthcare. Key highlights include:

• 96% accuracy in estimating gestational age using ALEXNET and UNET models.
• Validated on 10,000 labeled ultrasound images and 1,000 DICOM videos from Aga Khan University Hospital.
• Significant reduction in procedure time and dependency on sonographer skills.
• Published and supported by TU Dresden and SZABIST.

Contact Us

Interested in collaborating or learning more about our innovation? Reach out to us!

Email: info@automatedbiometry.com

Email: khalid.rasheed82@gmail.com

Phone: +92 321 2330512

Address: Karachi, Pakistan