This study explored the use of deep learning to automate follicle detection and measurement in ultrasound cine-loops. A 3D neural network model was trained on clinical data and performed well in identifying and outlining follicles without manual input. Its accuracy was close to that of human experts, particularly for larger follicles during key stimulation days. The approach offers a practical and efficient alternative to manual follicle assessment in everyday IVF practice.