EMBRYO

Mission

To revolutionize prenatal care by providing machine learning tools that enable early, accurate and quantitative analysis of embryonic development.

The challenge

Early-stage embryonic anomalies are difficult to detect because ultrasound images are noisy and low in contrast, and clinical interpretation is subjective. This leads to delays in diagnosis and limited screening precision. Quantitative methods are needed to extract subtle morphological patterns and deviations in early gestation so clinicians can intervene sooner and improve outcomes.

The solution

We develop a system that applies machine learning to ultrasound images to enhance quality, detect anatomical landmarks and classify patterns. Convolutional and transformer-based models are trained on diverse datasets to denoise scans, extract relevant features and flag atypical growth. This turns qualitative ultrasound into a computational diagnostic tool, offering clinicians earlier, more accurate insights.