Skepticism, cerebral palsy, and the General Movements Assessment

Identifying Opportunities for Early Detection of Cerebral Palsy

This study aimed to evaluate assessment and referral practices for the early detection and diagnosis of children at risk for or with cerebral palsy (CP) by health care and education providers in Maryland and Delaware. A secondary aim was to identify barriers for using early detection tools and identify opportunities for change to support early diagnosis and improve care. Seventy-two participants answered ≥ 50% of the survey questions. Most were occupational or physical therapists (86%) working in early intervention (61%). Eighty-eight percent indicated awareness that CP can be diagnosed by 12 months. Though 86% stated they typically suspect a diagnosis of CP between 0 and 12 months, only 19% reported that their patients received a CP diagnosis < 12 months. The Developmental Assessment of Young Children (73%) and the Peabody Developmental Motor Scales-2 (59%) were used most. Many respondents indicated never using magnetic resonance imaging (70%), the General Movements Assessment (87%), or the Hammersmith Infant Neurological Exam (69%). Participants identified clinical signs and symptoms prompting a referral for the diagnostic assessment of CP, most commonly stiffness in legs (95%), excessive head lag (93%), and persistent fisting (92%). Policy and organizational change, clinician education, and training are needed to support the implementation of CP early detection guidelines.

Implementation of Early Detection and Intervention for Cerebral Palsy in High-Risk Infant Follow-Up Programs: U.S. and Global Considerations

Early detection and intervention for cerebral palsy is best practice for all high-risk infants according to international guidelines, consensus statements and research-supported evidence. It allows support for families and optimization of developmental trajectories into adulthood. All phases of implementation of CP early detection can be found across the world in high-risk infant follow-up programs, demonstrating feasibility and acceptability through standardized implementation science. The largest clinical network for CP early detection and intervention in the world has now sustained an average age at detection less than 12 months corrected age for more than 5 years. Targeted referrals and interventions for CP can now be offered to patients in optimal periods of neuroplasticity, and new therapies researched as the age of detection decreases. Implementation of guidelines and incorporation of rigorous CP research studies both allow high-risk infant follow-up programs to fulfill their mission of improving outcomes of those with the most vulnerable developmental trajectories from birth.

A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework's classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features.

Network Implementation of Guideline for Early Detection Decreases Age at Cerebral Palsy Diagnosis

BACKGROUND AND OBJECTIVES

Early diagnosis of cerebral palsy (CP) is critical in obtaining evidence-based interventions when plasticity is greatest. In 2017, international guidelines for early detection of CP were published on the basis of a systematic review of evidence. Our study aim was to reduce the age at CP diagnosis throughout a network of 5 diverse US high-risk infant follow-up programs through consistent implementation of these guidelines.

METHODS

The study leveraged plan-do-study-act and Lean methodologies. The primary outcome was age at CP diagnosis. Data were acquired during the corresponding 9-month baseline and quarterly throughout study. Balancing measures were clinic no-show rates and parent perception of the diagnosis visit. Clinic teams conducted strengths, weaknesses, opportunities, and threats analyses, process flow evaluations, standardized assessments training, and parent questionnaires. Performance of a 3- to 4-month clinic visit was a critical process step because it included a Hammersmith Infant Neurologic Examination, a General Movements Assessment, and standardized assessments of motor function.

RESULTS

The age at CP diagnosis decreased from a weighted average of 19.5 (95% confidence interval 16.2 to 22.8) to 9.5 months (95% confidence interval 4.5 to 14.6), with P = .008; 3- to 4-month visits per site increased from the median (interquartile range) 14 (5.2-73.7) to 54 (34.5-152.0), with P < .001; and no-show rates were not different. Parent questionnaires revealed positive provider perception with improvement opportunities for information content and understandability.

CONCLUSIONS

Large-scale implementation of international guidelines for early detection of CP is feasible in diverse high-risk infant follow-up clinics. The initiative was received positively by families and without adversely affecting clinic operational flow. Additional parent support and education are necessary.

Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

BACKGROUND

Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings.

METHODS

The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time-frequency decomposition of the movement trajectories of the infant's body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9-15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging.

RESULTS

The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%).

CONCLUSION

The CIMA model may be a clinically feasible alternative to observational GMA.