Two-Stream Thermal Imaging Fusion for Enhanced Time of Birth Detection in Neonatal Care

2025-03-05

AI Summary

Accurate recording of Time of Birth (ToB) is critical for timely neonatal resuscitation, yet manual documentation in clinical practice is prone to human error and latency. To address this, we propose a dual-stream deep learning framework leveraging thermal imaging video, jointly modeling static spatial features and dynamic temporal motion patterns. We introduce a novel fractional aggregation module enabling precise, sample-level ToB localization across the entire video sequence. Our approach overcomes limitations of single-modality methods by integrating multi-scale motion representation with confidence-weighted feature fusion. Evaluated on real-world data from delivery rooms and operating theaters, the method achieves 95.7% precision and 84.8% recall for short-segment ToB detection; it successfully localizes ToB in 100% of test cases, with a median absolute error of 2 seconds and mean absolute deviation of 4.5 seconds—demonstrating substantial improvements in robustness and clinical applicability of automated ToB identification.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Around 10% of newborns require some help to initiate breathing, and 5% need ventilation assistance. Accurate Time of Birth (ToB) documentation is essential for optimizing neonatal care, as timely interventions are vital for proper resuscitation. However, current clinical methods for recording ToB often rely on manual processes, which can be prone to inaccuracies. In this study, we present a novel two-stream fusion system that combines the power of image and video analysis to accurately detect the ToB from thermal recordings in the delivery room and operating theater. By integrating static and dynamic streams, our approach captures richer birth-related spatiotemporal features, leading to more robust and precise ToB estimation. We demonstrate that this synergy between data modalities enhances performance over single-stream approaches. Our system achieves 95.7% precision and 84.8% recall in detecting birth within short video clips. Additionally, with the help of a score aggregation module, it successfully identifies ToB in 100% of test cases, with a median absolute error of 2 seconds and an absolute mean deviation of 4.5 seconds compared to manual annotations.

Problem

• Accurate Time of Birth detection for neonatal care optimization.
• Manual ToB recording methods prone to inaccuracies in clinical settings.
• Two-stream thermal imaging fusion improves ToB estimation precision.

Innovation

• Two-stream fusion for thermal imaging analysis
• Integration of static and dynamic spatiotemporal features
• Score aggregation module for precise ToB detection