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Yin W, Chen L, Huang X, Huang C, Wang Z, Bian Y, Wan Y, Zhou Y, Han T, Yi M. A self-supervised spatio-temporal attention network for video-based 3D infant pose estimation. Med Image Anal 2024; 96:103208. [PMID: 38788327 DOI: 10.1016/j.media.2024.103208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 04/02/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
General movement and pose assessment of infants is crucial for the early detection of cerebral palsy (CP). Nevertheless, most human pose estimation methods, in 2D or 3D, focus on adults due to the lack of large datasets and pose annotations on infants. To solve these problems, here we present a model known as YOLO-infantPose, which has been fine-tuned, for infant pose estimation in 2D. We further propose a self-supervised model called STAPose3D for 3D infant pose estimation based on videos. We employ multi-view video data during the training process as a strategy to address the challenge posed by the absence of 3D pose annotations. STAPose3D combines temporal convolution, temporal attention, and graph attention to jointly learn spatio-temporal features of infant pose. Our methods are summarized into two stages: applying YOLO-infantPose on input videos, followed by lifting these 2D poses along with respective confidences for every joint to 3D. The employment of the best-performing 2D detector in the first stage significantly improves the precision of 3D pose estimation. We reveal that fine-tuned YOLO-infantPose outperforms other models tested on our clinical dataset as well as two public datasets MINI-RGBD and YouTube-Infant dataset. Results from our infant movement video dataset demonstrate that STAPose3D effectively comprehends the spatio-temporal features among different views and significantly improves the performance of 3D infant pose estimation in videos. Finally, we explore the clinical application of our method for general movement assessment (GMA) in a clinical dataset annotated as normal writhing movements or abnormal monotonic movements according to the GMA standards. We show that the 3D pose estimation results produced by our STAPose3D model significantly boost the GMA prediction performance than 2D pose estimation. Our code is available at github.com/wwYinYin/STAPose3D.
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Affiliation(s)
- Wang Yin
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China; Neuroscience Research Institute, Peking University and Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, Beijing 100083, China
| | - Linxi Chen
- Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Xinrui Huang
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | | | - Zhaohong Wang
- Peking University Third Hospital, Beijing 100191, China
| | - Yang Bian
- Peking University First Hospital, Beijing 100034, China
| | - You Wan
- Neuroscience Research Institute, Peking University and Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, Beijing 100083, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Tongyan Han
- Department of Pediatrics, Peking University Third Hospital, Beijing 100191, China.
| | - Ming Yi
- Neuroscience Research Institute, Peking University and Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, Beijing 100083, China.
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Einspieler C, Bos AF, Spittle AJ, Bertoncelli N, Burger M, Peyton C, Toldo M, Utsch F, Zhang D, Marschik PB. The General Movement Optimality Score-Revised (GMOS-R) with Socioeconomically Stratified Percentile Ranks. J Clin Med 2024; 13:2260. [PMID: 38673533 PMCID: PMC11050782 DOI: 10.3390/jcm13082260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024] Open
Abstract
Background: The general movement optimality score (GMOS) quantifies the details of general movements (GMs). We recently conducted psychometric analyses of the GMOS and developed a revised scoresheet. Consequently, the GMOS-Revised (GMOS-R) instrument necessitated validation using new percentile ranks. This study aimed to provide these percentile ranks for the GMOS-R and to investigate whether sex, preterm birth, or the infant's country of birth and residence affected the GMOS-R distribution. Methods: We applied the GMOS-R to an international sample of 1983 infants (32% female, 44% male, and 24% not disclosed), assessed in the extremely and very preterm period (10%), moderate (12%) and late (22%) preterm periods, at term (25%), and post-term age (31%). Data were grouped according to the World Bank's classification into lower- and upper-middle-income countries (LMICs and UMICs; 26%) or high-income countries (HICs; 74%), respectively. Results: We found that sex and preterm or term birth did not affect either GM classification or the GMOS-R, but the country of residence did. A lower median GMOS-R for infants with normal or poor-repertoire GMs from LMICs and UMICs compared with HICs suggests the use of specific percentile ranks for LMICs and UMICs vs. HICs. Conclusion: For clinical and scientific use, we provide a freely available GMOS-R scoring sheet, with percentile ranks reflecting socioeconomic stratification.
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Affiliation(s)
- Christa Einspieler
- Interdisciplinary Developmental Neuroscience—iDN, Division of Phoniatrics, Medical University of Graz, 8010 Graz, Austria
| | - Arend F. Bos
- Division of Neonatology, Department of Pediatrics, Beatrix Children’s Hospital, University Medical Center Groningen, University of Groningen, 9712 GZ Groningen, The Netherlands
| | - Alicia J. Spittle
- Department of Physiotherapy, Melbourne School of Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia;
| | - Natascia Bertoncelli
- Neonatal Intensive Care Unit, Department of Medical and Surgical Sciences of Mothers, Children and Adults, University Hospital of Modena, 41124 Modena, Italy;
| | - Marlette Burger
- Physiotherapy Division, Department of Health and Rehabilitation Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa;
| | - Colleen Peyton
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA;
| | - Moreno Toldo
- Department of Medical Rehabilitation, Kiran Society for Rehabilitation and Education of Children with Disabilities, Varanasi 221011, India;
| | - Fabiana Utsch
- Reabilitação Infantil, Rede SARAH de Hospitais de Reabilitação, Belo Horizonte 30510-000, Brazil;
| | - Dajie Zhang
- Interdisciplinary Developmental Neuroscience—iDN, Division of Phoniatrics, Medical University of Graz, 8010 Graz, Austria
- Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Ruprecht-Karls University, 69115 Heidelberg, Germany
| | - Peter B. Marschik
- Interdisciplinary Developmental Neuroscience—iDN, Division of Phoniatrics, Medical University of Graz, 8010 Graz, Austria
- Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Ruprecht-Karls University, 69115 Heidelberg, Germany
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Leibniz-ScienceCampus Primate Cognition, 37075 Göttingen, Germany
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden
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Park MW, Shin HI, Bang MS, Kim DK, Shin SH, Kim EK, Lee ES, Shin HI, Lee WH. Reduction in limb-movement complexity at term-equivalent age is associated with motor developmental delay in very-preterm or very-low-birth-weight infants. Sci Rep 2024; 14:8432. [PMID: 38600352 PMCID: PMC11006919 DOI: 10.1038/s41598-024-59125-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 04/08/2024] [Indexed: 04/12/2024] Open
Abstract
Reduced complexity during the writhing period can be crucial in the spontaneous movements of high-risk infants for neurologic impairment. This study aimed to verify the association between quantified complexity of upper and lower-limb movements at term-equivalent age and motor development in very-preterm or very-low-birth-weight infants. Video images of spontaneous movements at term-equivalent age were collected from very-preterm or very-low-birth-weight infants. A pretrained pose-estimation model and sample entropy (SE) quantified the complexity of the upper- and lower-limb movements. Motor development was evaluated at 9 months of corrected age using Bayley Scales of Infant and Toddler Development, Third Edition. The SE measures were compared between infants with and without motor developmental delay (MDD). Among 90 infants, 11 exhibited MDD. SE measures at most of the upper and lower limbs were significantly reduced in infants with MDD compared to those without MDD (p < 0.05). Composite scores in the motor domain showed significant positive correlations with SE measures at most upper and lower limbs (p < 0.05). The results show that limb-movement complexity at term-equivalent age is reduced in infants with MDD at 9 months of corrected age. SE of limb movements can be a potentially useful kinematic parameter to detect high-risk infants for MDD.
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Affiliation(s)
- Myung Woo Park
- Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Hyung-Ik Shin
- Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Moon Suk Bang
- Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- National Traffic Injury Rehabilitation Hospital, Yangpyeong, Republic of Korea
| | - Don-Kyu Kim
- Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Seung Han Shin
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ee-Kyung Kim
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Eun Sun Lee
- Department of Pediatrics, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Hyun Iee Shin
- Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
- Biomedical Research Institute, Chung-Ang University Hospital, Seoul, Republic of Korea.
| | - Woo Hyung Lee
- Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Wang P, Fang E, Zhao X, Feng J. Nomogram for soiling prediction in postsurgery hirschsprung children: a retrospective study. Int J Surg 2024; 110:1627-1636. [PMID: 38116670 PMCID: PMC10942236 DOI: 10.1097/js9.0000000000000993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE The aim of this study was to develop a nomogram for predicting the probability of postoperative soiling in patients aged greater than 1 year operated for Hirschsprung disease (HSCR). MATERIALS AND METHODS The authors retrospectively analyzed HSCR patients with surgical therapy over 1 year of age from January 2000 and December 2019 at our department. Eligible patients were randomly categorized into the training and validation set at a ratio of 7:3. By integrating the least absolute shrinkage and selection operator [LASSO] and multivariable logistic regression analysis, crucial variables were determined for establishment of the nomogram. And, the performance of nomogram was evaluated by C-index, area under the receiver operating characteristic curve, calibration curves, and decision curve analysis. Meanwhile, a validation set was used to further assess the model. RESULTS This study enrolled 601 cases, and 97 patients suffered from soiling. Three risk factors, including surgical history, length of removed bowel, and surgical procedures were identified as predictive factors for soiling occurrence. The C-index was 0.871 (95% CI: 0.821-0.921) in the training set and 0.878 (95% CI: 0.811-0.945) in the validation set, respectively. And, the AUC was found to be 0.896 (95% CI: 0.855-0.929) in the training set and 0.866 (95% CI: 0.767-0.920) in the validation set. Additionally, the calibration curves displayed a favorable agreement between the nomogram model and actual observations. The decision curve analysis revealed that employing the nomogram to predict the risk of soiling occurrence would be advantageous if the threshold was between 1 and 73% in the training set and 3-69% in the validation set. CONCLUSION This study represents the first efforts to develop and validate a model capable of predicting the postoperative risk of soiling in patients aged greater than 1 year operated for HSCR. This model may assist clinicians in determining the individual risk of soiling subsequent to HSCR surgery, aiding in personalized patient care and management.
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Affiliation(s)
| | | | | | - Jiexiong Feng
- Department of Pediatric Surgery, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Center of Hirschsprung Disease and Allied Disorders, Wuhan, People’s Republic of China
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Letzkus L, Pulido JV, Adeyemo A, Baek S, Zanelli S. Machine learning approaches to evaluate infants' general movements in the writhing stage-a pilot study. Sci Rep 2024; 14:4522. [PMID: 38402234 PMCID: PMC10894291 DOI: 10.1038/s41598-024-54297-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 02/11/2024] [Indexed: 02/26/2024] Open
Abstract
The goals of this study are to describe machine learning techniques employing computer-vision movement algorithms to automatically evaluate infants' general movements (GMs) in the writhing stage. This is a retrospective study of infants admitted 07/2019 to 11/2021 to a level IV neonatal intensive care unit (NICU). Infant GMs, classified by certified expert, were analyzed in two-steps (1) determination of anatomic key point location using a NICU-trained pose estimation model [accuracy determined using object key point similarity (OKS)]; (2) development of a preliminary movement model to distinguish normal versus cramped-synchronized (CS) GMs using cosine similarity and autocorrelation of major joints. GMs were analyzed using 85 videos from 74 infants; gestational age at birth 28.9 ± 4.1 weeks and postmenstrual age (PMA) at time of video 35.9 ± 4.6 weeks The NICU-trained pose estimation model was more accurate (0.91 ± 0.008 OKS) than a generic model (0.83 ± 0.032 OKS, p < 0.001). Autocorrelation values in the lower limbs were significantly different between normal (5 videos) and CS GMs (5 videos, p < 0.05). These data indicate that automated pose estimation of anatomical key points is feasible in NICU patients and that a NICU-trained model can distinguish between normal and CS GMs. These preliminary data indicate that machine learning techniques may represent a promising tool for earlier CP risk assessment in the writhing stage and prior to hospital discharge.
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Affiliation(s)
- Lisa Letzkus
- Department of Pediatrics, University of Virginia Children's Hospital, PO Box 800828, Charlottesville, VA, 22908, USA.
| | - J Vince Pulido
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Abiodun Adeyemo
- Department of Pediatrics, University of Virginia Children's Hospital, PO Box 800828, Charlottesville, VA, 22908, USA
| | - Stephen Baek
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Santina Zanelli
- Department of Pediatrics, University of Virginia Children's Hospital, PO Box 800828, Charlottesville, VA, 22908, USA
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Chun S, Jang S, Kim JY, Ko C, Lee J, Hong J, Park YR. Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks-Based Deep Learning: Development and Validation Study. JMIR Form Res 2024; 8:e51996. [PMID: 38381519 PMCID: PMC10918544 DOI: 10.2196/51996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 12/22/2023] [Accepted: 01/02/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Accurate and timely assessment of children's developmental status is crucial for early diagnosis and intervention. More accurate and automated developmental assessments are essential due to the lack of trained health care providers and imprecise parental reporting. In various areas of development, gross motor development in toddlers is known to be predictive of subsequent childhood developments. OBJECTIVE The purpose of this study was to develop a model to assess gross motor behavior and integrate the results to determine the overall gross motor status of toddlers. This study also aimed to identify behaviors that are important in the assessment of overall gross motor skills and detect critical moments and important body parts for the assessment of each behavior. METHODS We used behavioral videos of toddlers aged 18-35 months. To assess gross motor development, we selected 4 behaviors (climb up the stairs, go down the stairs, throw the ball, and stand on 1 foot) that have been validated with the Korean Developmental Screening Test for Infants and Children. In the child behavior videos, we estimated each child's position as a bounding box and extracted human keypoints within the box. In the first stage, the videos with the extracted human keypoints of each behavior were evaluated separately using a graph convolutional networks (GCN)-based algorithm. The probability values obtained for each label in the first-stage model were used as input for the second-stage model, the extreme gradient boosting (XGBoost) algorithm, to predict the overall gross motor status. For interpretability, we used gradient-weighted class activation mapping (Grad-CAM) to identify important moments and relevant body parts during the movements. The Shapley additive explanations method was used for the assessment of variable importance, to determine the movements that contributed the most to the overall developmental assessment. RESULTS Behavioral videos of 4 gross motor skills were collected from 147 children, resulting in a total of 2395 videos. The stage-1 GCN model to evaluate each behavior had an area under the receiver operating characteristic curve (AUROC) of 0.79 to 0.90. Keypoint-mapping Grad-CAM visualization identified important moments in each behavior and differences in important body parts. The stage-2 XGBoost model to assess the overall gross motor status had an AUROC of 0.90. Among the 4 behaviors, "go down the stairs" contributed the most to the overall developmental assessment. CONCLUSIONS Using movement videos of toddlers aged 18-35 months, we developed objective and automated models to evaluate each behavior and assess each child's overall gross motor performance. We identified the important behaviors for assessing gross motor performance and developed methods to recognize important moments and body parts while evaluating gross motor performance.
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Affiliation(s)
- Sulim Chun
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sooyoung Jang
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin Yong Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chanyoung Ko
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - JooHyun Lee
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - JaeSeong Hong
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
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Passmore E, Kwong AL, Greenstein S, Olsen JE, Eeles AL, Cheong JLY, Spittle AJ, Ball G. Automated identification of abnormal infant movements from smart phone videos. PLOS DIGITAL HEALTH 2024; 3:e0000432. [PMID: 38386627 PMCID: PMC10883563 DOI: 10.1371/journal.pdig.0000432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/17/2023] [Indexed: 02/24/2024]
Abstract
Cerebral palsy (CP) is the most common cause of physical disability during childhood, occurring at a rate of 2.1 per 1000 live births. Early diagnosis is key to improving functional outcomes for children with CP. The General Movements (GMs) Assessment has high predictive validity for the detection of CP and is routinely used in high-risk infants but only 50% of infants with CP have overt risk factors when they are born. The implementation of CP screening programs represents an important endeavour, but feasibility is limited by access to trained GMs assessors. To facilitate progress towards this goal, we report a deep-learning framework for automating the GMs Assessment. We acquired 503 videos captured by parents and caregivers at home of infants aged between 12- and 18-weeks term-corrected age using a dedicated smartphone app. Using a deep learning algorithm, we automatically labelled and tracked 18 key body points in each video. We designed a custom pipeline to adjust for camera movement and infant size and trained a second machine learning algorithm to predict GMs classification from body point movement. Our automated body point labelling approach achieved human-level accuracy (mean ± SD error of 3.7 ± 5.2% of infant length) compared to gold-standard human annotation. Using body point tracking data, our prediction model achieved a cross-validated area under the curve (mean ± S.D.) of 0.80 ± 0.08 in unseen test data for predicting expert GMs classification with a sensitivity of 76% ± 15% for abnormal GMs and a negative predictive value of 94% ± 3%. This work highlights the potential for automated GMs screening programs to detect abnormal movements in infants as early as three months term-corrected age using digital technologies.
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Affiliation(s)
- E Passmore
- Murdoch Children's Research Institute, Developmental Imaging, Melbourne, Australia
- University of Melbourne, Engineering and Information Technology, Melbourne, Australia
- University of Melbourne, Medicine, Dentistry & Health Sciences, Melbourne, Australia
- Royal Children's Hospital, Gait Analysis Laboratory, Melbourne, Australia
| | - A L Kwong
- University of Melbourne, Medicine, Dentistry & Health Sciences, Melbourne, Australia
- Murdoch Children's Research Institute, Victorian Infant Brain Studies, Melbourne, Australia
- Royal Women's Hospital, Newborn Research Centre, Melbourne, Australia
| | - S Greenstein
- Murdoch Children's Research Institute, Developmental Imaging, Melbourne, Australia
| | - J E Olsen
- Murdoch Children's Research Institute, Victorian Infant Brain Studies, Melbourne, Australia
- Royal Women's Hospital, Newborn Research Centre, Melbourne, Australia
| | - A L Eeles
- Murdoch Children's Research Institute, Victorian Infant Brain Studies, Melbourne, Australia
- Royal Women's Hospital, Newborn Research Centre, Melbourne, Australia
| | - J L Y Cheong
- University of Melbourne, Medicine, Dentistry & Health Sciences, Melbourne, Australia
- Murdoch Children's Research Institute, Victorian Infant Brain Studies, Melbourne, Australia
- Royal Women's Hospital, Newborn Research Centre, Melbourne, Australia
| | - A J Spittle
- University of Melbourne, Medicine, Dentistry & Health Sciences, Melbourne, Australia
- Murdoch Children's Research Institute, Victorian Infant Brain Studies, Melbourne, Australia
| | - G Ball
- Murdoch Children's Research Institute, Developmental Imaging, Melbourne, Australia
- University of Melbourne, Medicine, Dentistry & Health Sciences, Melbourne, Australia
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Gao Q, Yao S, Tian Y, Zhang C, Zhao T, Wu D, Yu G, Lu H. Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy. Nat Commun 2023; 14:8294. [PMID: 38097602 PMCID: PMC10721621 DOI: 10.1038/s41467-023-44141-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating the integrity of the developing nervous system and predicting motor dysfunctions, particularly in conditions such as cerebral palsy (CP). However, the necessity for highly trained professionals has hindered the adoption of GMA as an early screening tool in some countries. In this study, we propose a deep learning-based motor assessment model (MAM) that combines infant videos and basic characteristics, with the aim of automating GMA at the fidgety movements (FMs) stage. MAM demonstrates strong performance, achieving an Area Under the Curve (AUC) of 0.967 during external validation. Importantly, it adheres closely to the principles of GMA and exhibits robust interpretability, as it can accurately identify FMs within videos, showing substantial agreement with expert assessments. Leveraging the predicted FMs frequency, a quantitative GMA method is introduced, which achieves an AUC of 0.956 and enhances the diagnostic accuracy of GMA beginners by 11.0%. The development of MAM holds the potential to significantly streamline early CP screening and revolutionize the field of video-based quantitative medical diagnostics.
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Affiliation(s)
- Qiang Gao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Siqiong Yao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Yuan Tian
- Department of Health Management, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chuncao Zhang
- Department of Health Management, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Tingting Zhao
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Dan Wu
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China.
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China.
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Jiao Tong University, Shanghai, China.
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9
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Shin HI, Park MW, Lee WH. Spontaneous movements as prognostic tool of neurodevelopmental outcomes in preterm infants: a narrative review. Clin Exp Pediatr 2023; 66:458-464. [PMID: 37202346 PMCID: PMC10626027 DOI: 10.3345/cep.2022.01235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 04/24/2023] [Accepted: 05/05/2023] [Indexed: 05/20/2023] Open
Abstract
An estimated 15 million infants are born prematurely each year. Although the survival rate of preterm infants has increased with advances in perinatal and neonatal care, many still experience various complications. Since improving the neurodevelopmental outcomes of preterm births is a crucial topic, accurate evaluations should be performed to detect infants at high risk of cerebral palsy. General movements are spontaneous movements involving the whole body as the expression of neural activity and can be an excellent biomarker of neural dysfunction caused by brain impairment in preterm infants. The predictive value of general movements with respect to cerebral palsy increases with continuous observation. Automated approaches to examining general movements based on machine learning can help overcome the limited utilization of assessment tools owing to their qualitative or semiquantitative nature and high dependence on assessor skills and experience. This review covers each of these topics by summarizing normal and abnormal general movements as well as recent advances in automatic approaches based on infantile spontaneous movements.
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Affiliation(s)
- Hyun Iee Shin
- Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Myung Woo Park
- Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Woo Hyung Lee
- Department of Rehabilitation Medicine, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Korea
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10
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Gefen N, Weiss PL, Rigbi A, Rosenberg L. Lessons learned from a pediatric powered mobility lending program. Disabil Rehabil Assist Technol 2023:1-10. [PMID: 37897432 DOI: 10.1080/17483107.2023.2276232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 10/23/2023] [Indexed: 10/30/2023]
Abstract
PURPOSE To evaluate children's characteristics and impact of a powered wheelchair lending program including comparisons of diagnostic sub-groups, and validation of a predictive model of powered mobility proficiency. METHODS AND MATERIALS This retrospective study included 172 children who participated in the ALYN powered mobility lending program from 3/2009-7/2022. Demographics and functional levels were measured via questionnaires; driving proficiency was evaluated when the wheelchair was returned, and parents and children were interviewed following their participation in the program. RESULTS Two diagnostic groups were identified: cerebral palsy (CP) (n = 136, median = 9.75 yrs) and other neuromuscular diseases (NMD) (n = 30, median = 5.83 yrs). They differed significantly in the age they commenced PM training, the male/female ratio, walking ability and access mode. Fifty-seven percent of the participants with CP achieved powered mobility proficiency, a rate that was significantly lower than the 73% proficiency found for the NMD group. Four significant predictors were identified: communication, manual wheelchair operation, access mode and go-stop upon request. They predicted proficiency in approximately 80% of cases. Overall feedback from the parents and children indicated that their personal and family's quality of life improved as a result of their child's ability to use a powered wheelchair. CONCLUSIONS A lending program provides children with opportunities to improve mobility skills in an appropriate powered wheelchair. Children who can communicate verbally, propel a manual wheelchair, use a joystick and go-stop upon request are significantly more likely to become proficient drivers; however, many who were unable to complete these tasks also improved and even became proficient drivers.
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Affiliation(s)
- Naomi Gefen
- ALYN Hospital, Jerusalem, Israel
- PARC Research Center, ALYN Hospital, Jerusalem, Israel
| | - Patrice L Weiss
- PARC Research Center, ALYN Hospital, Jerusalem, Israel
- Dept. of Occupational Therapy, University of Haifa, Haifa, Israel
| | - Amihai Rigbi
- Faculty of Education, Beit Berl College, Kfar-Sava, Israel
| | - Lori Rosenberg
- School of Occupational Therapy, Hebrew University, Israel
- Ilanot Special Education School, Jerusalem, Israel
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11
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Kulvicius T, Zhang D, Nielsen-Saines K, Bölte S, Kraft M, Einspieler C, Poustka L, Wörgötter F, Marschik PB. Infant movement classification through pressure distribution analysis. COMMUNICATIONS MEDICINE 2023; 3:112. [PMID: 37587165 PMCID: PMC10432534 DOI: 10.1038/s43856-023-00342-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 08/01/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we propose an innovative non-intrusive approach using a pressure sensing device to classify infant general movements. Here we differentiate typical general movement patterns of the "fidgety period" (fidgety movements) vs. the "pre-fidgety period" (writhing movements). METHODS Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4 to 16 weeks of post-term age. 1776 pressure data snippets, each 5 s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present or absent. Multiple neural network architectures were tested to distinguish the fidgety present vs. fidgety absent classes, including support vector machines, feed-forward networks, convolutional neural networks, and long short-term memory networks. RESULTS Here we show that the convolution neural network achieved the highest average classification accuracy (81.4%). By comparing the pros and cons of other methods aiming at automated general movement assessment to the pressure sensing approach, we infer that the proposed approach has a high potential for clinical applications. CONCLUSIONS We conclude that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions.
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Affiliation(s)
- Tomas Kulvicius
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany.
- Department for Computational Neuroscience, Third Institute of Physics-Biophysics, Georg-August-University of Göttingen, Göttingen, Germany.
| | - Dajie Zhang
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
- iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Karin Nielsen-Saines
- Division of Pediatric Infectious Diseases, David Geffen UCLA School of Medicine, Los Angeles, CA, USA
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Curtin Autism Research Group, Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Marc Kraft
- Department of Medical Engineering, Technical University Berlin, Berlin, Germany
| | - Christa Einspieler
- iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Luise Poustka
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
| | - Florentin Wörgötter
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
- Department of Medical Engineering, Technical University Berlin, Berlin, Germany
| | - Peter B Marschik
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
- iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
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12
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Marschik PB, Kwong AKL, Silva N, Olsen JE, Schulte-Rüther M, Bölte S, Örtqvist M, Eeles A, Poustka L, Einspieler C, Nielsen-Saines K, Zhang D, Spittle AJ. Mobile Solutions for Clinical Surveillance and Evaluation in Infancy-General Movement Apps. J Clin Med 2023; 12:3576. [PMID: 37240681 PMCID: PMC10218843 DOI: 10.3390/jcm12103576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
The Prechtl General Movements Assessment (GMA) has become a clinician and researcher toolbox for evaluating neurodevelopment in early infancy. Given that it involves the observation of infant movements from video recordings, utilising smartphone applications to obtain these recordings seems like the natural progression for the field. In this review, we look back on the development of apps for acquiring general movement videos, describe the application and research studies of available apps, and discuss future directions of mobile solutions and their usability in research and clinical practice. We emphasise the importance of understanding the background that has led to these developments while introducing new technologies, including the barriers and facilitators along the pathway. The GMApp and Baby Moves apps were the first ones developed to increase accessibility of the GMA, with two further apps, NeuroMotion and InMotion, designed since. The Baby Moves app has been applied most frequently. For the mobile future of GMA, we advocate collaboration to boost the field's progression and to reduce research waste. We propose future collaborative solutions, including standardisation of cross-site data collection, adaptation to local context and privacy laws, employment of user feedback, and sustainable IT structures enabling continuous software updating.
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Affiliation(s)
- Peter B. Marschik
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Leibniz Science, Campus Primate Cognition, 37075 Göttingen, Germany; (P.B.M.)
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institute, 11330 Stockholm, Sweden
- iDN, Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
| | - Amanda K. L. Kwong
- Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- The Royal Women’s Hospital, Parkville, VIC 3052, Australia
- Department of Physiotherapy, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Nelson Silva
- iDN, Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
| | - Joy E. Olsen
- Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- The Royal Women’s Hospital, Parkville, VIC 3052, Australia
| | - Martin Schulte-Rüther
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Leibniz Science, Campus Primate Cognition, 37075 Göttingen, Germany; (P.B.M.)
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institute, 11330 Stockholm, Sweden
- Curtin Autism Research Group, Curtin School of Allied Health, Curtin University, Perth, WA 6102, Australia
- Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, 11861 Stockholm, Sweden
| | - Maria Örtqvist
- Neonatal Research Unit, Department of Women’s and Children’s Health, Karolinska Institute, 11330 Stockholm, Sweden
- Functional Area Occupational Therapy & Physiotherapy, Allied Health Professionals Function, Karolinska University Hospital, 11330 Stockholm, Sweden
| | - Abbey Eeles
- Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- The Royal Women’s Hospital, Parkville, VIC 3052, Australia
- Department of Physiotherapy, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Luise Poustka
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Leibniz Science, Campus Primate Cognition, 37075 Göttingen, Germany; (P.B.M.)
| | - Christa Einspieler
- iDN, Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
| | - Karin Nielsen-Saines
- Division of Pediatric Infectious Diseases, David Geffen UCLA School of Medicine, Los Angeles, CA 90095, USA
| | - Dajie Zhang
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Leibniz Science, Campus Primate Cognition, 37075 Göttingen, Germany; (P.B.M.)
- iDN, Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
| | - Alicia J. Spittle
- Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- The Royal Women’s Hospital, Parkville, VIC 3052, Australia
- Department of Physiotherapy, The University of Melbourne, Parkville, VIC 3052, Australia
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13
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Turner A, Hayes S, Sharkey D. The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development. SENSORS (BASEL, SWITZERLAND) 2023; 23:4800. [PMID: 37430717 DOI: 10.3390/s23104800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/07/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient's home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children's dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children's complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring.
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Affiliation(s)
- Alexander Turner
- Department of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
| | - Stephen Hayes
- Department of Engineering, Nottingham Trent University, Nottingham NG4 2EA, UK
| | - Don Sharkey
- Department of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
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14
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Marschik PB, Kulvicius T, Flügge S, Widmann C, Nielsen-Saines K, Schulte-Rüther M, Hüning B, Bölte S, Poustka L, Sigafoos J, Wörgötter F, Einspieler C, Zhang D. Open video data sharing in developmental science and clinical practice. iScience 2023; 26:106348. [PMID: 36994082 PMCID: PMC10040728 DOI: 10.1016/j.isci.2023.106348] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/19/2022] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
In behavioral research and clinical practice video data has rarely been shared or pooled across sites due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing. This demand is even more imperative when data-heavy computer-based approaches are involved. To share data while abiding by privacy protection rules, a critical question arises whether efforts at data de-identification reduce data utility? We addressed this question by showcasing an established and video-based diagnostic tool for detecting neurological deficits. We demonstrated for the first time that, for analyzing infant neuromotor functions, pseudonymization by face-blurring video recordings is a viable approach. The redaction did not affect classification accuracy for either human assessors or artificial intelligence methods, suggesting an adequate and easy-to-apply solution for sharing behavioral video data. Our work shall encourage more innovative solutions to share and merge stand-alone video datasets into large data pools to advance science and public health.
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Affiliation(s)
- Peter B. Marschik
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research; Department of Women’s and Children’s Health, Karolinska Institutet, 11330 Stockholm, Sweden
- iDN – interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
- Leibniz-ScienceCampus Primate Cognition, 37075 Göttingen, Germany
| | - Tomas Kulvicius
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- Department for Computational Neuroscience, Third Institute of Physics-Biophysics, Georg-August-University of Göttingen, 37077 Göttingen, Germany
| | - Sarah Flügge
- Department for Computational Neuroscience, Third Institute of Physics-Biophysics, Georg-August-University of Göttingen, 37077 Göttingen, Germany
| | - Claudius Widmann
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Karin Nielsen-Saines
- Division of Pediatric Infectious Diseases, David Geffen UCLA School of Medicine Los Angeles, CA 90095, USA
| | - Martin Schulte-Rüther
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, 37075 Göttingen, Germany
| | - Britta Hüning
- Department of Pediatrics I, Neonatology, University Children’s Hospital Essen, University Duisburg-Essen, 45147 Essen, Germany
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research; Department of Women’s and Children’s Health, Karolinska Institutet, 11330 Stockholm, Sweden
- Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, 11861 Stockholm, Sweden
- Curtin Autism Research Group, Curtin School of Allied Health, Curtin University, 6102 Perth, WA
| | - Luise Poustka
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, 37075 Göttingen, Germany
| | - Jeff Sigafoos
- School of Education, Victoria University of Wellington, 6012 Wellington, New Zealand
| | - Florentin Wörgötter
- Leibniz-ScienceCampus Primate Cognition, 37075 Göttingen, Germany
- Department for Computational Neuroscience, Third Institute of Physics-Biophysics, Georg-August-University of Göttingen, 37077 Göttingen, Germany
| | - Christa Einspieler
- iDN – interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
| | - Dajie Zhang
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- iDN – interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
- Leibniz-ScienceCampus Primate Cognition, 37075 Göttingen, Germany
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15
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Marschik-Zhang D, Wang J, Shen X, Zhu X, Gao H, Yang H, Marschik PB. Building Blocks for Deep Phenotyping in Infancy: A Use Case Comparing Spontaneous Neuromotor Functions in Prader-Willi Syndrome and Cerebral Palsy. J Clin Med 2023; 12:784. [PMID: 36769434 PMCID: PMC9917638 DOI: 10.3390/jcm12030784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/20/2023] Open
Abstract
With the increasing worldwide application of the Prechtl general movements assessment (GMA) beyond its original field of the early prediction of cerebral palsy (CP), substantial knowledge has been gained on early neuromotor repertoires across a broad spectrum of diagnostic groups. Here, we aimed to profile the neuromotor functions of infants with Prader-Willi syndrome (PWS) and to compare them with two other matched groups. One group included infants with CP; the other included patients who were treated at the same clinic and turned out to have inconspicuous developmental outcomes (IOs). The detailed GMA, i.e., the motor optimality score-revised (MOS-R), was used to prospectively assess the infants' (N = 54) movements. We underwent cross-condition comparisons to characterise both within-group similarities and variations and between-group distinctions and overlaps in infants' neuromotor functions. Although infants in both the PWS and the CP groups scored similarly low on MOS-R, their motor patterns were different. Frog-leg and mantis-hand postures were frequently seen in the PWS group. However, a PWS-specific general movements pattern was not observed. We highlight that pursuing in-depth knowledge within and beyond the motor domain in different groups has the potential to better understand different conditions, improve accurate diagnosis and individualised therapy, and contribute to deep phenotyping for precision medicine.
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Affiliation(s)
- Dajie Marschik-Zhang
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- Leibniz Science Campus Primate Cognition, 37077 Göttingen, Germany
- iDN—Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
| | - Jun Wang
- Department of Rehabilitation, Children’s Hospital, Fudan University, Shanghai 201102, China
| | - Xiushu Shen
- Department of Rehabilitation, Children’s Hospital, Fudan University, Shanghai 201102, China
| | - Xiaoyun Zhu
- Department of Rehabilitation, Children’s Hospital, Fudan University, Shanghai 201102, China
| | - Herong Gao
- Department of Rehabilitation, Children’s Hospital, Fudan University, Shanghai 201102, China
| | - Hong Yang
- Department of Rehabilitation, Children’s Hospital, Fudan University, Shanghai 201102, China
| | - Peter B. Marschik
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- Leibniz Science Campus Primate Cognition, 37077 Göttingen, Germany
- iDN—Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, 8036 Graz, Austria
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Child and Adolescent Psychiatry, Region Stockholm, Karolinska Institutet & Stockholm Health Care Services, 17176 Stockholm, Sweden
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16
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Haberfehlner H, van de Ven SS, van der Burg SA, Huber F, Georgievska S, Aleo I, Harlaar J, Bonouvrié LA, van der Krogt MM, Buizer AI. Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning. Front Robot AI 2023; 10:1108114. [PMID: 36936408 PMCID: PMC10018017 DOI: 10.3389/frobt.2023.1108114] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/09/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance. Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8-23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0-1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut's prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy. Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15-20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively. Conclusion: This proof-of-concept study shows the potential of using stick figures extracted from common videos in a machine learning approach to automatically assess dystonia. Sufficient tracking accuracy can be reached by manually adding labels within 15-20 frames per video. With a relatively small data set, it is possible to train a model that can automatically assess dystonia with a performance comparable to human scoring.
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Affiliation(s)
- Helga Haberfehlner
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, Amsterdam, Netherlands
- Amsterdam Movement Sciences, Rehabilitation and Development, Amsterdam, Netherlands
- Department of Rehabilitation Sciences, Katholieke Universiteit Leuven, Campus Bruges, Bruges, Belgium
- *Correspondence: Helga Haberfehlner,
| | - Shankara S. van de Ven
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, Amsterdam, Netherlands
| | | | - Florian Huber
- Netherlands eScience Center, Amsterdam, Netherlands
- Centre for Digitalization and Digitality, University of Applied Sciences Düsseldorf, Düsseldorf, Germany
| | | | | | - Jaap Harlaar
- Department Biomechanical Engineering, Delft University of Technology (TU Delft), Delft, Netherlands
| | - Laura A. Bonouvrié
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, Amsterdam, Netherlands
- Amsterdam Movement Sciences, Rehabilitation and Development, Amsterdam, Netherlands
| | - Marjolein M. van der Krogt
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, Amsterdam, Netherlands
- Amsterdam Movement Sciences, Rehabilitation and Development, Amsterdam, Netherlands
| | - Annemieke I. Buizer
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, Amsterdam, Netherlands
- Amsterdam Movement Sciences, Rehabilitation and Development, Amsterdam, Netherlands
- Emma Children’s Hospital, Amsterdam UMC, Amsterdam, Netherlands
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17
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Moreira A, Tovar M, Smith AM, Lee GC, Meunier JA, Cheema Z, Moreira A, Winter C, Mustafa SB, Seidner S, Findley T, Garcia JGN, Thébaud B, Kwinta P, Ahuja SK. Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia. Am J Physiol Lung Cell Mol Physiol 2023; 324:L76-L87. [PMID: 36472344 PMCID: PMC9829478 DOI: 10.1152/ajplung.00250.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/27/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.
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Affiliation(s)
- Alvaro Moreira
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Miriam Tovar
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Alisha M Smith
- Veterans Administration Research Center for AIDS and HIV-1 Infection and Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- The Foundation for Advancing Veterans' Health Research, South Texas Veterans Health Care System, San Antonio, Texas
- Department of Microbiology, Immunology & Molecular Genetics, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Grace C Lee
- Veterans Administration Research Center for AIDS and HIV-1 Infection and Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- Pharmacotherapy Education and Research Center, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- College of Pharmacy, The University of Texas at Austin, Austin, Texas
| | - Justin A Meunier
- Veterans Administration Research Center for AIDS and HIV-1 Infection and Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Zoya Cheema
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Axel Moreira
- Division of Critical Care, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas
| | - Caitlyn Winter
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Shamimunisa B Mustafa
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Steven Seidner
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Tina Findley
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston and Children's Memorial Hermann Hospital, Houston, Texas
| | - Joe G N Garcia
- Department of Medicine, University of Arizona Health Sciences, Tucson, Arizona
| | - Bernard Thébaud
- Sinclair Centre for Regenerative Medicine, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Pediatrics, Children's Hospital of Eastern Ontario (CHEO) and CHEO Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Przemko Kwinta
- Neonatal Intensive Care Unit, Department of Pediatrics, Jagiellonian University Medical College, Krakow, Poland
| | - Sunil K Ahuja
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- The Foundation for Advancing Veterans' Health Research, South Texas Veterans Health Care System, San Antonio, Texas
- Department of Microbiology, Immunology & Molecular Genetics, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, Texas
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18
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Tamburin S, Filippetti M, Mantovani E, Smania N, Picelli A. Spasticity following brain and spinal cord injury: assessment and treatment. Curr Opin Neurol 2022; 35:728-740. [PMID: 36226708 DOI: 10.1097/wco.0000000000001114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
PURPOSE OF REVIEW Spasticity is a common sequela of brain and spinal cord injury and contributes to disability, reduces quality of life, and increases economic burden. Spasticity is still incompletely recognized and undertreated. We will provide an overview of recent published data on the definition, assessment, and prediction, therapeutic advances, with a focus on promising new approaches, and telemedicine applications for spasticity. RECENT FINDINGS Two new definitions of spasticity have been recently proposed, but operational criteria should be developed, and test-retest and inter-rater reliability should be explored. Cannabinoids proved to be effective in spasticity in multiple sclerosis, but evidence in other types of spasticity is lacking. Botulinum neurotoxin injection is the first-line therapy for focal spasticity, and recent literature focused on optimizing its efficacy. Several pharmacological, interventional, and nonpharmacological therapeutic approaches for spasticity have been explored but low-quality evidence impedes solid conclusions on their efficacy. The recent COVID-19 pandemic yielded guidelines/recommendations for the use of telemedicine in spasticity. SUMMARY Despite the frequency of spasticity, robust diagnostic criteria and reliable assessment scales are required. High-quality studies are needed to support the efficacy of current treatments for spasticity. Future studies should explore telemedicine tools for spasticity assessment and treatment.
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Affiliation(s)
- Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Italy
| | - Mirko Filippetti
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Italy
| | - Elisa Mantovani
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Italy
| | - Nicola Smania
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Italy
| | - Alessandro Picelli
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Italy
- Canadian Advances in Neuro-Orthopaedics for Spasticity Congress (CANOSC), Kingston, Ontario, Canada
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