Accurate and Interpretable Postmenstrual Age Prediction via Multimodal Large Language Model
This addresses the need for transparent AI in perinatal neuroscience, offering a solution for clinicians to assess neonatal development more reliably, though it is incremental as it adapts existing multimodal large language models.
The paper tackled the problem of predicting postmenstrual age from neonatal brain MRI with both accuracy and interpretability, achieving a prediction error with a 95% confidence interval of 0.78 to 1.52 weeks while generating clinically relevant explanations.
Accurate estimation of postmenstrual age (PMA) at scan is crucial for assessing neonatal development and health. While deep learning models have achieved high accuracy in predicting PMA from brain MRI, they often function as black boxes, offering limited transparency and interpretability in clinical decision support. In this work, we address the dual challenge of accuracy and interpretability by adapting a multimodal large language model (MLLM) to perform both precise PMA prediction and clinically relevant explanation generation. We introduce a parameter-efficient fine-tuning (PEFT) strategy using instruction tuning and Low-Rank Adaptation (LoRA) applied to the Qwen2.5-VL-7B model. The model is trained on four 2D cortical surface projection maps derived from neonatal MRI scans. By employing distinct prompts for training and inference, our approach enables the MLLM to handle a regression task during training and generate clinically relevant explanations during inference. The fine-tuned model achieves a low prediction error with a 95 percent confidence interval of 0.78 to 1.52 weeks, while producing interpretable outputs grounded in developmental features, marking a significant step toward transparent and trustworthy AI systems in perinatal neuroscience.