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Enhanced Markerless Tracking of Infant General Movements in Standard Videos Through Lightning Pose Compared to
Insights
Lightning Pose (LP) offers superior accuracy in markerless infant pose estimation compared to DeepLabCut (DLC), improving early detection of neurological impairments like cerebral palsy (CP). This technology aids in monitoring General Movements (GMs) for better infant neurodevelopmental assessment.
Area of Science:
- Biomedical Engineering
- Developmental Neuroscience
- Medical Technology
Background:
- Monitoring General Movements (GMs) in infants (6-20 weeks post-term) is crucial for assessing neurodevelopmental quality and predicting neurological impairments, including cerebral palsy (CP).
- Early interventions are most effective during this period due to infant brain plasticity.
- Markerless pose-estimation technologies are advancing the ability to track infant movements from clinical videos.
Purpose of the Study:
- To compare the reliability and applicability of the Lightning Pose (LP) platform against the DeepLabCut (DLC) environment for markerless pose-estimation and tracking of infant anatomical landmarks.
- To evaluate the performance of Resnet-152, trained on both LP and DLC platforms, in identifying 24 clinically relevant anatomical landmarks in unseen video frames.
- To assess the accuracy of LP and DLC in tracking infant movements using an unsupervised performance assessment algorithm.
Main Methods:
- Comparison of LP and DLC platforms using Resnet-152 for pose-estimation on clinical videos of infants.
- Identification and tracking of 24 anatomical landmarks in over 3200 unseen video frames from two full-term male infants.
- Utilized an unsupervised performance assessment algorithm to quantify accuracy, comparing body part identification rates and cosine similarity index between platforms.
Main Results:
- Lightning Pose (LP) demonstrated superior performance in body part identification, achieving 99.75% accuracy compared to DeepLabCut (DLC)'s 97.80%.
- Both platforms were trained on nearly identical datasets, with an average cosine similarity index of 0.95.
- LP's enhanced accuracy in tracking infant anatomical landmarks was validated on a comprehensive set of unseen video frames.
Conclusions:
- The Lightning Pose (LP) platform significantly outperforms DLC in markerless pose-estimation for infant anatomical landmarks.
- LP's superior accuracy represents a key advancement for developing accessible technology for automated monitoring of infant General Movements (GMs).
- This technology has the potential to improve early identification of infants at elevated risk for neurological conditions, facilitating timely therapeutic interventions.
Abstract:
Monitoring abnormal or absent General Movements (GMs) in infants between 6-20 weeks post-term age serves as a reliable prognostic tool for assessing neurodevelopmental quality and determining the risk of neurological impairments, such as cerebral palsy (CP), in early infancy. Early therapeutic interventions during this timeframe are most effective due to the brain's plasticity. Building on our previous work, this paper compares the reliability and applicability of the Lightning Pose (LP) platform to our prior attempts using the DeepLabCut (DLC) environment for the reliable markerless pose-estimation and tracking of infants' anatomical landmarks in clinically recorded videos from handheld devices (e.g., iPads). Here, we specifically compare the capabilities of Resnet-152, trained in both platforms, for identifying a novel clinically useful set of 24 anatomical landmarks across a comprehensive unseen set of video frames, tracking a baby's natural movement during validation recordings. Using our novel unsupervised performance assessment algorithm on over 3200 unseen frames from two full-term male infants, we show that LP outperforms DLC in body parts identification, achieving 99.75% accuracy compared to DLC's 97.80%, despite nearly identical training sets extracted by both algorithms (average cosine similarity index of 0.95).Clinical relevance-The superior performance of LP in this study represents a significant stride toward developing a cost-effective and widely accessible technology for automatically monitoring of infants' GMs and identifying those at elevated risk of developing neurological conditions in early infancy.
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