MSAFNet: A multi-scale attention fusion network for automated neonatal pain facial expression recognition with clinical validation.

Authors

Yan Zhang, Xuehan Qiu, Yongqiao Shang

Published in

Scientific reports. Volume 16. Issue 1. Jul 30, 2026. Epub Jul 30, 2026.

Abstract

Accurate, timely pain assessment in neonates remains a stubborn yet critical challenge in neonatal intensive care units (NICUs), where conventional observational scales suffer from subjectivity, intermittent use, and uneven inter-rater reliability. We propose a Multi-Scale Attention Fusion Network (MSAFNet) for automated neonatal pain facial expression recognition, and validate it in an authentic clinical setting. Drawing on two public collections together with a prospectively gathered NICU corpus, we assembled 9,485 annotated facial images from 153 neonates; after strict subject-level partitioning, training-set-only augmentation (MixUp, CutOut, plus conventional geometric and photometric perturbations) expanded the training pool to over 25,000 samples while the validation and test sets remained un-augmented. The MSAFNet architecture combines a modified lightweight ResNet-34 backbone with landmark-guided local feature branches, multi-scale parallel dilated convolution pathways, channel-spatial dual attention, and an adaptive softmax-weighted fusion mechanism, targeting both subtle muscle contractions and holistic configurational change in neonatal faces. On the held-out test set MSAFNet reached 94.3% accuracy, 94.1% precision, 94.1% recall, 94.2% F1-score, and an AUC of 0.978, outperforming ResNet-50, EfficientNet-B0, and domain-specific baselines. A prospective clinical validation covering 62 neonates and 287 procedural pain episodes yielded substantial agreement with expert nurse consensus (Cohen's Kappa = 0.84, ICC = 0.91), with a per-frame inference latency of approximately 0.3 s. Usability ratings from nursing staff were favourable, supporting the role of MSAFNet as a supplementary monitoring aid rather than a replacement for clinical judgement. Taken together, the findings suggest that MSAFNet can serve as a practical decision-support tool for continuous, objective neonatal pain assessment in real NICU environments.

PMID:
42533023
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.

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