Data Collection Protocol for the Development of CHLOE-OQ, an AI Model for Assessing Oocytes Quality.

The CHLOE-OQ Data Collection Protocol is designed to support the reliable and robust development of the CHLOE technology-based application, ensuring their accuracy and effectiveness in assessing oocyte quality. This protocol facilitates the entire AI model development lifecycle, including training, verification, and validation, to enhance the performance and reliability of machine learning algorithms integrated into the CHLOE application. Additionally, the protocol aims to refine the machine learning stage of the already released OQ applications by incorporating new data and improving model performance over time. A key component of this process includes simulated use assessments, which are conducted to evaluate the usability, accuracy, and overall functionality of CHLOE applications in real-world clinical settings by means of a questionnaire for embryologist using CHLOE. These assessments ensure that the technology is not only scientifically sound but also user-friendly, making it easier for embryologists to integrate CHLOE applications into their workflow efficiently. By adhering to this structured data collection protocol, the CHLOE application can continuously evolve, maintaining high standards of performance and usability in the assessment of oocyte quality.