Pet eggs undergo a series of distinct developmental phases as they transition from fertilized egg cells to living organisms. The majority of this natural progression is governed by physical mechanisms, with various animal species following a similar trajectory. However, there exist variations in the specifics, both within larvae of the same species and across different species. For example, the pace at which various larval stages are completed may vary. These deviations in fetal development are believed to play a significant role in evolution by potentially generating new characteristics, fostering biodiversity, and driving biological adaptations.
Understanding animal fetal development is therefore essential for grasping evolutionary processes. The question arises: how can variations in embryonic development, such as the timing of developmental stages, be accurately and objectively documented? Researchers at Konstanz University, led by systems biologist Patrick Müller, are pioneering the use of artificial intelligence (AI) methods to address this challenge. In their recent publication in Characteristics Methods, they introduce an innovative approach that transcends species boundaries to efficiently capture the pace of developmental processes and identify characteristic stages autonomously.
Every zygote is unique.
Examinations of embryos at different developmental stages under a microscope, coupled with detailed descriptions, have significantly contributed to our current knowledge of embryogenesis and developmental stages in animals. As a result of this extensive research, reference guides with standardized illustrations of embryonic stages are now accessible for certain animal species.
Nevertheless, when observed under a microscope, larvae may not always resemble the illustrations in reference materials. Furthermore, the transitions between each stage are often more gradual than abrupt.
Systems researcher Patrick Müller
Thus, delineating an embryo into distinct developmental stages can be challenging and somewhat subjective, particularly for experts.
The unpredictability of larval development further complicates matters. Müller suggests that various factors, such as temperature, can influence the timing of fetal development. The AI-assisted methodology developed by him and his team represents a significant breakthrough. By leveraging over 3 million images of healthy fish embryos, the researchers trained their Twin Network for a secondary application. Subsequently, the developmental age of diverse zebrafish embryos could be instantly determined using the resultant IoT model.
Accurate, Informative, and Purposeful
The researchers demonstrated that AI can efficiently and autonomously identify critical stages of zebrafish embryonic development and individual developmental phases without human intervention. In their study, the researchers utilized the AI system to compare embryo developmental stages and investigate the temperature sensitivity of zebrafish fetal development. Despite being trained on images of normally developing embryos, the AI could detect irregularities that may occur spontaneously in a percentage of eggs or due to environmental toxins.
This methodology was then extended to other animal species, including sticklebacks and the evolutionary distinct insect Caenorhabditis elegans.
Our Twin Network-based approach can assess the fetal development of various avian species concerning both time and stages, provided the necessary image data is available. According to Müller, our system operates objectively and consistently even in the absence of specific analytical data for the animal species.
Hence, this technique holds great promise for exploring the development and evolution of previously undiscovered animal species.
