Objective quantification of suckling biomechanics in infants with and without tongue-tie using hydraulic differential-pressure recording and machine learning

Vollständiger Abstract

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Introduction Objective assessment of infant suckling biomechanics remains limited despite decades of pressure-based measurement research, and tongue-tie (ankyloglossia) is still largely evaluated using semi-quantitative anatomical scoring. We developed a hydraulic differential-pressure device — the electronic baby bottle (EBB) — designed to record real-time net intraoral mechanical load during nutritive suckling through a fluid-filled sensing cavity with a controlled air inclusion coupled to a differential pressure transducer. This study aimed to: (1) describe the EBB measurement principle, (2) develop automated machine learning–based classification of effective suckling activity, and (3) evaluate whether quantitative biomechanical parameters derived from automated analysis demonstrate responsiveness one week after frenotomy. Methods Time-series recordings from iterative prototype testing were segmented into overlapping windows and manually annotated to train a supervised classifier using the ROCKET transform implemented in Python (sktime) with ridge classification and cross-validation. Performance was evaluated on an internal held-out test set and an independent external validation set of full-length recordings excluded from model development. Quantitative parameters were computed from automatically identified effective suckling bursts in 25 infants with restrictive tongue-tie recorded before and one week after frenotomy and compared with 10 control infants with normal tongue mobility. Results Automated classification achieved >99% accuracy on the internal test set and 92% segment-level classification accuracy on external validation recordings. Mean negative intraoral pressure during effective suckling bursts increased in 19/25 (76%) treated infants one week after frenotomy (paired t-test, p = 0.010), shifting toward control values. Discussion Hydraulic differential-pressure recording combined with time-series machine learning enables objective and reproducible quantification of nutritive suckling activity and detects short-term functional change following frenotomy in most infants with restrictive tongue-tie. This framework supports future work on normative reference ranges, standardized digital metrics, and data-driven diagnostic thresholds for infant feeding dysfunction.