deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

Abstract

Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. Human blastoids can be generated in large numbers, making them suitable for high-throughput screening, which often involves analyzing vast numbers of images. However, automated methods for evaluating and characterizing blastoid morphology are still underdeveloped. We developed a deep-learning model capable of recognizing and classifying blastoid brightfield images into five distinct quality categories. The model processes 53.2 images per second with an average accuracy of 87%, without signs of overfitting or batch eHects. By integrating a Confidence Rate (CR) metric, the accuracy was further improved to 97%, with low-CR images flagged for human review. In a comparison with human experts, the model matched their accuracy while significantly outperforming them in throughput. We demonstrate the value of the model in two real-world applications: (1) systematic assessment of the eHect of lysophosphatidic acid (LPA) concentration on blastoid formation, and (2) evaluating the impact of dimethyl sulfoxide (DMSO) on blastoids for drug screening. In the applications involving over 10,000 images, the model identified significant eHects of LPA and DMSO, which may have been overlooked in manual assessments. The deepBlastoid model is publicly available and researchers can train their own model according to their imaging conditions and blastoid culture protocol. deepBlastoid thus oHers a precise, automated approach for blastoid classification, with significant potential for advancing mechanism research, drug screening, and clinical in vitro fertilization (IVF) applications.