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Importance Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective To develop and assess the external validity of a novel deep learning–based method to predict CP based on videos of infants’ spontaneous movements at 9 to 18 weeks’ corrected age. Design, Setting, and Participants This prognostic study of a deep learning–based method to predict CP at a corrected age of 12 to 89 months involved 557 infants with a high risk of perinatal brain injury who were enrolled in previous studies conducted at 13 hospitals in Belgium, India, Norway, and the US between September 10, 2001, and October 25, 2018. Analysis was performed between February 11, 2020, and September 23, 2021. Included infants had available video recorded during the fidgety movement period from 9 to 18 weeks’ corrected age, available classifications of fidgety movements ascertained by the general movement assessment (GMA) tool, and available data on CP status at 12 months’ corrected age or older. A total of 418 infants (75.0%) were randomly assigned to the model development (training and internal validation) sample, and 139 (25.0%) were randomly assigned to the external validation sample (1 test set). Exposure Video recording of spontaneous movements. Main Outcomes and Measures The primary outcome was prediction of CP. Deep learning–based prediction of CP was performed automatically from a single video. Secondary outcomes included prediction of associated functional level and CP subtype. Sensitivity, specificity, positive and negative predictive values, and accuracy were assessed. Results Among 557 infants (310 [55.7%] male), the median (IQR) corrected age was 12 (11-13) weeks at assessment, and 84 infants (15.1%) were diagnosed with CP at a mean (SD) age of 3.4 (1.7) years. Data on race and ethnicity were not reported because previous studies (from which the infant samples were derived) used different study protocols with inconsistent collection of these data. On external validation, the deep learning–based CP prediction method had sensitivity of 71.4% (95% CI, 47.8%-88.7%), specificity of 94.1% (95% CI, 88.2%-97.6%), positive predictive value of 68.2% (95% CI, 45.1%-86.1%), and negative predictive value of 94.9% (95% CI, 89.2%-98.1%). In comparison, the GMA tool had sensitivity of 70.0% (95% CI, 45.7%-88.1%), specificity of 88.7% (95% CI, 81.5%-93.8%), positive predictive value of 51.9% (95% CI, 32.0%-71.3%), and negative predictive value of 94.4% (95% CI, 88.3%-97.9%). The deep learning method achieved higher accuracy than the conventional machine learning method (90.6% [95% CI, 84.5%-94.9%] vs 72.7% [95% CI, 64.5%-79.9%]; P Conclusions and Relevance In this prognostic study, a deep learning–based method for predicting CP at 9 to 18 weeks’ corrected age had predictive accuracy on external validation, which suggests possible avenues for using deep learning–based software to provide objective early detection of CP in clinical settings.

ObjectivesTo determine whether videos taken by parents of their infants’ spontaneous movements were in accordance with required standards in the In-Motion-App, and whether the videos could be remotely scored by a trained General Movement Assessment (GMA) observer. Additionally, to assess the feasibility of using home-based video recordings for automated tracking of spontaneous movements, and to examine parents’ perceptions and experiences of taking videos in their homes.DesignThe study was a multi-centre prospective observational study.SettingParents/families of high-risk infants in tertiary care follow-up programmes in Norway, Denmark and Belgium.MethodsParents/families were asked to video record their baby in accordance with the In-Motion standards which were based on published GMA criteria and criteria covering lighting and stability of smartphone. Videos were evaluated as GMA ‘scorable’ or ‘non-scorable’ based on predefined criteria. The accuracy of a 7-point body tracker software was compared with manually annotated body key points. Parents were surveyed about the In-Motion-App information and clarity.ParticipantsThe sample comprised 86 parents/families of high-risk infants.ResultsThe 86 parent/families returned 130 videos, and 121 (96%) of them were in accordance with the requirements for GMA assessment. The 7-point body tracker software detected more than 80% of body key point positions correctly. Most families found the instructions for filming their baby easy to follow, and more than 90% reported that they did not become more worried about their child’s development through using the instructions.ConclusionsThis study reveals that a short instructional video enabled parents to video record their infant’s spontaneous movements in compliance with the standards required for remote GMA. Further, an accurate automated body point software detecting infant body landmarks in smartphone videos will facilitate clinical and research use soon. Home-based video recordings could be performed without worrying parents about their child’s development.Trials registration numberNCT03409978.

Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1 424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance., Published in Computerized Medical Imaging and Graphics (CMIG)

This study investigated the explanatory power of a sensor fusion of two complementary methods to explain performance and its underlying mechanisms in ski jumping. A differential Global Navigation Satellite System (dGNSS) and a markerless video-based pose estimation system (PosEst) were used to measure the kinematics and kinetics from the start of the in-run to the landing. The study had two aims, firstly, the agreement between the two methods was assessed using 16 jumps by athletes of national level from 5 m before the take-off to 20 m after, where the methods had spatial overlap. The comparison revealed a good agreement from 5 m after the take-off, within the uncertainty of the dGNSS (±0.05m). The second part of the study served as a proof of concept of the sensor fusion application, by showcasing the type of performance analysis the systems allows. Two ski jumps by the same ski jumper, with comparable external conditions, were chosen for the case study. The dGNSS was used to analyse the in-run and flight phase, while the PosEst system was used to analyse the take-off and the early flight phase. The proof-of-concept study showed that the methods are suitable to track the kinematic and kinetic characteristics that determine performance in ski jumping and their usability in both research and practice.

Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. However, with the progresses in the field, more complex and inefficient models have also been introduced, which have caused tremendous increases in computational demands. To cope with these complexity and inefficiency challenges, we propose a novel convolutional neural network architecture, called EfficientPose, which exploits recently proposed EfficientNets in order to deliver efficient and scalable single-person pose estimation. EfficientPose is a family of models harnessing an effective multi-scale feature extractor and computationally efficient detection blocks using mobile inverted bottleneck convolutions, while at the same time ensuring that the precision of the pose configurations is still improved. Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost. The results from our experiments, using the challenging MPII single-person benchmark, show that the proposed EfficientPose models substantially outperform the widely-used OpenPose model both in terms of accuracy and computational efficiency. In particular, our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets., Comment: Published in Applied Intelligence Journal (APIN)

The authors conducted a retrospective study on the outcome after multilevel spine fusion in elderly patients. Seventy-two out of 80 patients were available after a mean follow-up period of 29.4 months. There were 47 females and 25 males. Their mean age at operation was 68.7 years, which means that many complaints may have been due to an underlying osteoporosis, unresponsive to surgical treatment, and exposing to loosening of the implants. The outcome was indeed rather poor: only 50% of the patients were satisfied. VAS and ODI improved slightly, but not significantly. Implant loosening was the main complication: it occurred in 35 patients, but necessitated re-operation in only 8. Adjacent segment degeneration (ASD) occurred in 26 patients, and necessitated re-operation in 17. This study should be a warning against an interventionist attitude in older patients with so-called spondylosis, where osteoporosis should be excluded and, if present, should be treated as a first step.

In the treatment of multilevel degenerative disorders of the lumbar spine, spondylodesis plays a controversial role. Most patients can be treated conservatively with success. Multilevel lumbar fusion with instrumentation is associated with severe complications like failed back surgery syndrome, implant failure, and adjacent segment disease (ASD). This retrospective study examines the records of 70 elderly patients with degenerative changes or instability of the lumbar spine treated between 2002 and 2007 with spondylodesis of more than two segments. Sixty-four patients were included; 5 patients had died and one patient was lost to follow-up. We evaluated complications, clinical/radiological outcomes, and success of fusion. Flexion-extension and standing X-rays in two planes, MRI, and/or CT scans were obtained pre-operatively. Patients were assessed clinically using the Oswestry disability index (ODI) and a Visual Analogue Scale (VAS). Surgery performed was dorsolateral fusion (46.9%) or dorsal fusion with anterior lumbar interbody fusion (ALIF; 53.1%). Additional decompression was carried out in 37.5% of patients. Mean follow-up was 29.4±5.4 months. Average patient age was 64.7±4.3 years. Clinical outcomes were not satisfactory for all patients. VAS scores improved from 8.6±1.3 to 5.6±3.0 pre- to post-operatively, without statistical significance. ODI was also not significantly improved (56.1±22.3 pre- and 45.1±26.4 post-operatively). Successful fusion, defined as adequate bone mass with trabeculation at the facets and transverse processes or in the intervertebral segments, did not correlate with good clinical outcomes. Thirty-five of 64 patients (54%) showed signs of pedicle screw loosening, especially of the screws at S1. However, only 7 of these 35 (20%) complained of corresponding back pain. Revision surgery was required in 24 of 64 patients (38%). Of these, indications were adjacent segment disease (16 cases), pedicle screw loosening (7 cases), and infection (one case). At follow-up of 29.4 months, patients with radiographic ASD had worse ODI scores than patients without (54.7 vs. 36.6; P less than 0.001). Multilevel fusion for degenerative disease still has a high rate of complications, up to 50%. The problem of adjacent segment disease after fusion surgery has not yet been solved. This study underscores the need for strict indication guidelines to perform lumbar spine fusion of more than two levels.