Improved Planning Model for Patient

Note: This application has been filled. Background Individualizing ovarian stimulation and optimizing workflow in in vitro fertilization (IVF) remains a major clinical and logistical challenge. Treatment outcome and stimulation length are influenced by a wide range of patient-specific factors, including age, ovarian reserve, body mass index (BMI), hormonal profiles, stimulation protocol, medication dose, and previous cycle response. Despite the use of different stimulation protocols based primarily on age and ovarian reserve, the actual duration of stimulation remains unpredictable in approximately 40% of IVF cycles. This unpredictability has significant consequences for both patients and clinical workflows. Patients often require multiple ultrasound visits to monitor follicular development and adjust medication, increasing treatment burden and stress. At the same time, IVF laboratory workflows are affected by large fluctuations in the number of oocyte pick-ups (OPUs) per day, ranging from one to ten procedures. Such variability complicates staffing, resource allocation, and scheduling, and may ultimately affect treatment efficiency and outcomes. Currently, no data-driven approach is available to accurately predict stimulation length and the timing of ovum pick-up at the start of treatment. The absence of predictive models limits the ability to provide patient-centered planning and results in inefficient use of clinical and laboratory resources. Recent work has demonstrated that IVF laboratory performance and workflow characteristics are associated with treatment outcomes, highlighting the potential value of predictive planning tools to improve both efficiency and quality of care [1]. Approach The objective of this project is to develop an AI-driven predictive model that estimates the timing of ovum pick-up for individual IVF patients with an accuracy of one to two days. The model will integrate heterogeneous data sources, inclu...