F^2-Assist: Multi-Phase Fetal Growth Forecast and Report Generation from Ultrasound Examination

Forecasting fetal growth from sequential ultrasound examinations is essential for personalized prenatal care. Existing medical vision-language models (MLLMs) are limited to single-phase/organ evaluations and qualitative reasoning, neglecting longitudinal history and precise continuous biometric values. To address this gap, we introduce the novel task of multi-phase fetal growth forecasting and report generation. To support this task, we first present GrowthFetus, the largest multi-phase, multi-organ fetal ultrasound dataset to date, containing 9,280 examinations from 2,000 fetuses. Based on this dataset, we propose F$^2$-Assist, a unified MLLM framework with three key components: (i) a Cross-Phase Organ Alignment module for for heterogeneous multi-organ feature fusion across phases, (ii) a History-Aware Temporal Encoding module for modeling irregular temporal dynamics, and (iii) a Growth Parameter Adapter that encodes continuous biometric values as differentiable tokens for numerically precise reasoning. Extensive experiments show that F$^2$-Assist achieves temporally coherent predictions and clinically consistent reports, significantly outperforming state-of-the-art MLLMs. Our study establishes a practical framework for longitudinal ultrasound analysis, bridging growth forecasting and report generation in a unified model.