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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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2
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Doroniewicz I, Ledwoń DJ, Bugdol M, Kieszczyńska K, Affanasowicz A, Latos D, Matyja M, Myśliwiec A. Towards novel classification of infants' movement patterns supported by computerized video analysis. J Neuroeng Rehabil 2024; 21:129. [PMID: 39085937 PMCID: PMC11290138 DOI: 10.1186/s12984-024-01429-3] [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: 01/09/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Positional preferences, asymmetry of body position and movements potentially indicate abnormal clinical conditions in infants. However, a lack of standardized nomenclature hinders accurate assessment and documentation of these preferences over time. Video tools offer a safe and reproducible method to analyze and describe infant movement patterns, aiding in physiotherapy management and goal planning. The study aimed to develop an objective classification system for infant movement patterns with particular emphasis on the specific distribution of muscle tension, using methods of computer analysis of video recordings to enhance accuracy and reproducibility in assessments. METHODS The study involved the recording of videos of 51 infants between 6 and 15 weeks of age, born at term, with an Apgar score of at least 8 points. Based on observations of a recording of infant spontaneous movements in the supine position, experts identified postural-motor patterns: symmetry and typical asymmetry linked to the asymmetrical tonic neck reflex. Deviations from the typical postural-motor system were indicated, and subcategories of atypical patterns were distinguished. A computer-based inference system was developed to automatically classify individual patterns. RESULTS The following division of motor patterns was used: (1) normal patterns, including (a) typical (symmetrical, asymmetrical: variants 1 and 2); and (b) atypical (variants: 1 to 4), (2) positional preference, and (3) abnormal patterns. The proposed automatic classification method achieved an expert decision mapping accuracy of 84%. For atypical patterns, the high reproducibility of the system's results was confirmed. Lower reproducibility, not exceeding 70%, was achieved with typical patterns. CONCLUSIONS Based on the observation of infant spontaneous movements, it is possible to identify movement patterns divided into typical and atypical patterns. Computer-based analysis of infant movement patterns makes it possible to objectify and satisfactorily reproduce diagnostic decisions.
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Affiliation(s)
- Iwona Doroniewicz
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Katowice, Poland
| | - Daniel J Ledwoń
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland.
| | - Monika Bugdol
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland
| | - Katarzyna Kieszczyńska
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Katowice, Poland
| | - Alicja Affanasowicz
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Katowice, Poland
| | - Dominika Latos
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Katowice, Poland
| | - Małgorzata Matyja
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Katowice, Poland
| | - Andrzej Myśliwiec
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Katowice, Poland
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Guo L, He T, Lv X, Yu F. Identifying popular, rejected and neglected children in Chinese preschool: An exploratory study on the educational application of spatial positioning data. Acta Psychol (Amst) 2024; 248:104389. [PMID: 38970888 DOI: 10.1016/j.actpsy.2024.104389] [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: 02/13/2024] [Revised: 06/28/2024] [Accepted: 07/02/2024] [Indexed: 07/08/2024] Open
Abstract
Amidst the rapid advancement of Internet of Things (IoT) technology and the burgeoning field of Multimodal Learning Analytics (MMLA), this study employs spatial positioning technology as a case study to investigate the potential of multimodal data in assessing children's social development. This study combines the spatial positioning data of preschool children collected during free play sessions in natural educational settings and the spatial metrics constructed based on observational studies to establish and validate a sociometric status Decision Tree classification model. The findings suggest that the model can overall accurately identify children with three distinct sociometric statuses, albeit with some variability in efficacy across different sociometric groups and age groups. Notably, the model demonstrates a high hitting rate in identifying the potentially neglected children, providing valuable support for educators in understanding and fostering children's developmental needs. This study also highlights the advantages of emerging technology and multimodal data application in child development assessment.
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Affiliation(s)
- Liping Guo
- Institute of Brain and Education Innovation, Faculty of Education, East China Normal University, Shanghai, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China.
| | - Ting He
- College of Education, Central China Normal University, Wuhan, China.
| | - Xue Lv
- School of Education, Jiangsu University of Technology, Changzhou, China
| | - Fang Yu
- Department of Education, Suzhou Early Childhood Education College, Suzhou, China
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4
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Haberfehlner H, Roth Z, Vanmechelen I, Buizer AI, Jeroen Vermeulen R, Koy A, Aerts JM, Hallez H, Monbaliu E. A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task. Neurorehabil Neural Repair 2024; 38:479-492. [PMID: 38842031 DOI: 10.1177/15459683241257522] [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] [Indexed: 06/07/2024]
Abstract
BACKGROUND Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. OBJECTIVE To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences. METHODS Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined. RESULTS Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude. CONCLUSIONS This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data.
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Affiliation(s)
- Helga Haberfehlner
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands
| | - Zachary Roth
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Inti Vanmechelen
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Annemieke I Buizer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands
- Amsterdam UMC, Emma Children's Hospital, Amsterdam, The Netherlands
| | | | - Anne Koy
- Department of Pediatrics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Jean-Marie Aerts
- Department of Computer Science, Mechatronics Research Group (M-Group), KU Leuven Bruges, Distrinet, Bruges, Belgium
| | - Hans Hallez
- Department of Biosystems, Division of Animal and Human Health Engineering, Measure, Model and Manage Bioresponse (M3-BIORES), KU Leuven, Leuven, Belgium
| | - Elegast Monbaliu
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
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Zhao P, Alencastre-Miranda M, Shen Z, O'Neill C, Whiteman D, Gervas-Arruga J, Igo Krebs H. Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2336-2345. [PMID: 38889045 DOI: 10.1109/tnsre.2024.3416159] [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: 06/20/2024]
Abstract
Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( κlw = 0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.
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Kukka AJ, Bhattarai P, Sundelin HEK, Gurung R, Brown NJW, Litorp H, Axelin A, Kc A. 'We did everything by phone': a qualitative study of mothers' experience of smartphone-aided screening of cerebral palsy in Kathmandu, Nepal. BMC Pediatr 2024; 24:357. [PMID: 38778316 PMCID: PMC11110401 DOI: 10.1186/s12887-024-04829-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND International guidelines recommend early intervention to all children at risk of cerebral palsy, but targeted screening programs are often lacking in low- and middle-income settings with the highest burden of disease. Smartphone applications have the potential to improve access to early diagnostics by empowering parents to film their children at home followed by centralized evaluation of videos with General Movements Assessment. We explored mothers' perceptions about participating in a smartphone aided cerebral palsy screening program in Kathmandu, Nepal. METHODS This is an explorative qualitative study that used focus group discussions (n = 2) and individual interviews (n = 4) with mothers of term-born infants surviving birth asphyxia or neonatal seizures. Parents used the NeuroMotion™ smartphone app to film their children at home and the videos were analysed using Precthl's General Movements Assessment. Sekhon et al.'s framework on the acceptability of health care interventions guided the design of the group discussions and interviews, and the deductive qualitative content analysis. RESULTS Mothers were interested in engaging with the programme and expressed hope it would benefit their children. Most felt using the app was intuitive. They were, however, unclear about the way the analysis was performed. Support from the research team was often needed to overcome an initial lack of self-confidence in using the technology, and to reduce anxiety related to the follow-up. The intervention was overall perceived as recommendable but should be supplemented by a face-to-face consultation. CONCLUSION Smartphone aided remote screening of cerebral palsy is acceptable in a lower middle-income population but requires additional technical support.
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Affiliation(s)
- Antti J Kukka
- Department of Women's and Children's Health, Uppsala University, Uppsala, SE-751 85, Sweden.
- Department of Pediatrics, Gävle Regional Hospital, Gävle, Region Gävleborg, Sweden.
| | | | - Heléne E K Sundelin
- Department of Biomedical and Clinical Sciences, Division of Children's and Women´S Health, Linköping University, Linköping, Sweden
- Department of Women's and Children's Health, Neuropediatric Unit, Karolinska University Hospital, KarolinskaInstitutet, Stockholm, Sweden
| | - Rejina Gurung
- Department of Women's and Children's Health, Uppsala University, Uppsala, SE-751 85, Sweden
- Golden Community, Lalitpur, Nepal
| | - Nick J W Brown
- Department of Women's and Children's Health, Uppsala University, Uppsala, SE-751 85, Sweden
- Department of Pediatrics, Gävle Regional Hospital, Gävle, Region Gävleborg, Sweden
| | - Helena Litorp
- Department of Women's and Children's Health, Uppsala University, Uppsala, SE-751 85, Sweden
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Anna Axelin
- Department of Women's and Children's Health, Uppsala University, Uppsala, SE-751 85, Sweden
- Department of Nursing Science, University of Turku, Turku, Finland
| | - Ashish Kc
- Department of Women's and Children's Health, Uppsala University, Uppsala, SE-751 85, Sweden
- School of Public Health and Community Medicine, University of Gothenburg, Gothenburg, Sweden
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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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9
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Kukka AJ, Sundelin HEK, Basnet O, Paudel P, Upadhyay Subedi K, Svensson K, Brown N, Litorp H, Gurung R, Bhattarai P, Wrammert J, Kc A. NeuroMotion smartphone application for remote General Movements Assessment: a feasibility study in Nepal. BMJ Open 2024; 14:e080063. [PMID: 38431302 PMCID: PMC10910581 DOI: 10.1136/bmjopen-2023-080063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
OBJECTIVES To evaluate the feasibility of using the NeuroMotion smartphone application for remote General Movements Assessment for screening infants for cerebral palsy in Kathmandu, Nepal. METHOD Thirty-one term-born infants at risk of cerebral palsy due to birth asphyxia or neonatal seizures were recruited for the follow-up at Paropakar Maternity and Women's Hospital, 1 October 2021 to 7 January 2022. Parents filmed their children at home using the application at 3 months' age and the videos were assessed for technical quality using a standardised form and for fidgety movements by Prechtl's General Movements Assessment. The usability of the application was evaluated through a parental survey. RESULTS Twenty families sent in altogether 46 videos out of which 35 had approved technical quality. Sixteen children had at least one video with approved technical quality. Three infants lacked fidgety movements. The level of agreement between assessors was acceptable (Krippendorf alpha 0.781). Parental answers to the usability survey were in general positive. INTERPRETATION Engaging parents in screening of cerebral palsy with the help of a smartphone-aided remote General Movements Assessment is possible in the urban area of a South Asian lower middle-income country.
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Affiliation(s)
- Antti Juhani Kukka
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Department of Pediatrics, Gävle Regional Hospital, Region Gävleborg, Gävle, Sweden
| | - Heléne E K Sundelin
- Division of children's and women's health, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Neuropediatric Unit, Department of Women's and Children's Health, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | | | - Prajwal Paudel
- Paropakar Maternity and Women's Hospital, Kathmandu, Nepal
| | | | - Katarina Svensson
- Division of Children's and Women's Health, Department of Biomedical and Clinical Sciences, Linköping University, Linkoping, Sweden
| | - Nick Brown
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Department of Pediatrics, Gävle Regional Hospital, Region Gävleborg, Gävle, Sweden
| | - Helena Litorp
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Rejina Gurung
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Golden Community, Lalitpur, Nepal
| | | | - Johan Wrammert
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Ashish Kc
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- School of Public Health and Community Medicine, University of Gothenburg, Gothenburg, Sweden
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10
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Sermpon N, Gima H. Correlation between pose estimation features regarding movements towards the midline in early infancy. PLoS One 2024; 19:e0299758. [PMID: 38416738 PMCID: PMC10901309 DOI: 10.1371/journal.pone.0299758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 02/14/2024] [Indexed: 03/01/2024] Open
Abstract
In infants, spontaneous movement towards the midline (MTM) indicates the initiation of anti-gravity ability development. Markerless 2D pose estimation is a cost-effective, time-efficient, and quantifiable alternative to movement assessment. We aimed to establish correlations between pose estimation features and MTM in early-age infants. Ninety-four infant videos were analysed to calculate the percentage and rate of MTM occurrence. 2D Pose estimation processed the videos and determined the distances and areas using wrist and ankle landmark coordinates. We collected data using video recordings from 20 infants aged 8-16 weeks post-term age. Correlations between MTM observations and distance values were evaluated. Differences in areas between groups of videos showing MTM and no MTM in the total, lower-limb, and upper-limb categories were examined. MTM observations revealed common occurrences of hand-to-trunk and foot-to-foot movements. Weak correlations were noted between limb distances to the midbody imaginary line and MTM occurrence values. Lower MTM showed significant differences in the lower part (p = 0.003) and whole area (p = 0.001). Video recording by parents or guardians could extract features using 2D pose estimation, assisting in the early identification of MTM in infants. Further research is required to assess a larger sample size with the diversity of MTM motor behaviour, and later developmental skills, and collect data from at-risk infants.
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Affiliation(s)
- Nisasri Sermpon
- Department of Physical Therapy, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo, Japan
- Faculty of Physical Therapy, Mahidol University, Salaya, Nakhon Pathom, Thailand
| | - Hirotaka Gima
- Department of Physical Therapy, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo, Japan
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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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12
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Lin SC, Chandra E, Tsao PN, Liao WC, Chen WJ, Yen TA, Hsu JYJ, Jeng SF. Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm. Phys Ther 2024; 104:pzad176. [PMID: 38245806 DOI: 10.1093/ptj/pzad176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 10/18/2023] [Accepted: 12/03/2023] [Indexed: 01/22/2024]
Abstract
OBJECTIVE Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants. METHODS This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model. RESULTS The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%-100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91). CONCLUSION The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup. IMPACT The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment.
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Affiliation(s)
- Shiang-Chin Lin
- School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Erick Chandra
- Department of Computer Science, National Taiwan University College of Electric Engineering and Computer Science, Taipei, Taiwan
| | - Po Nien Tsao
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
| | - Wei-Chih Liao
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Wei-J Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Ting-An Yen
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
| | - Jane Yung-Jen Hsu
- Department of Computer Science, National Taiwan University College of Electric Engineering and Computer Science, Taipei, Taiwan
| | - Suh-Fang Jeng
- School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan
- Physical Therapy Center, National Taiwan University Hospital, Taipei, Taiwan
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Ji S, Ma D, Pan L, Wang W, Peng X, Amos JT, Ingabire HN, Li M, Wang Y, Yao D, Ren P. Automated Prediction of Infant Cognitive Development Risk by Video: A Pilot Study. IEEE J Biomed Health Inform 2024; 28:690-701. [PMID: 37053059 DOI: 10.1109/jbhi.2023.3266350] [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: 04/14/2023]
Abstract
OBJECTIVE Cognition is an essential human function, and its development in infancy is crucial. Traditionally, pediatricians used clinical observation or medical imaging to assess infants' current cognitive development (CD) status. The object of pediatricians' greater concern is however their future outcomes, because high-risk infants can be identified early in life for intervention. However, this opportunity has not yet been realized. Fortunately, some recent studies have shown that the general movement (GM) performance of infants around 3-4 months after birth might reflect their future CD status, which gives us an opportunity to achieve this goal by cameras and artificial intelligence. METHODS First, infants' GM videos were recorded by cameras, from which a series of features reflecting their bilateral movement symmetry (BMS) were extracted. Then, after at least eight months of natural growth, the infants' CD status was evaluated by the Bayley Infant Development Scale, and they were divided into high-risk and low-risk groups. Finally, the BMS features extracted from the early recorded GM videos were fed into the classifiers, using late infant CD risk assessment as the prediction target. RESULTS The area under the curve, recall and precision values reached 0.830, 0.832, and 0.823 for two-group classification, respectively. CONCLUSION This pilot study demonstrates that it is possible to automatically predict the CD of infants around the age of one year based on their GMs recorded early in life. SIGNIFICANCE This study not only helps clinicians better understand infant CD mechanisms, but also provides an economical, portable and non-invasive way to screen infants at high-risk early to facilitate their recovery.
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Celik HI, Yildiz A, Yildiz R, Mutlu A, Soylu R, Gucuyener K, Duyan-Camurdan A, Koc E, Onal EE, Elbasan B. Using the center of pressure movement analysis in evaluating spontaneous movements in infants: a comparative study with general movements assessment. Ital J Pediatr 2023; 49:165. [PMID: 38124131 PMCID: PMC10731817 DOI: 10.1186/s13052-023-01568-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Researchers have attempted to automate the spontaneous movement assessment and have sought quantitative and objective methods over the past decade. The purpose of the study was to present a quantitative assessment method of spontaneous movement using center-of-pressure (COP) movement analysis. METHODS A total of 101 infants were included in the study. The infants were placed in the supine position on the force plate with the cranial-caudal orientation. In this position, the recording of video and COP movement data were made simultaneously for 3 min. Video recordings were used to observe global and detailed general movement assessment (GMA), and COP time series data were used to obtain quantitative movement parameters. RESULTS According to the global GMA, 13 infants displayed absent fidgety movements (FMs) and 88 infants displayed normal FMs. The binary logistic regression model indicated significant association between global GMA and COP movement parameters (chi-square = 20.817, p < 0.001). The sensitivity, specificity, and overall accuracy of this model were 85% (95% CI: 55-98), 83% (95% CI: 73-90), and 83% (95% CI: 74-90), respectively. The multiple linear regression model showed a significant association between detailed GMA (motor optimality score-revised/MOS-R) and COP movement parameters (F = 10.349, p < 0.001). The MOS-R total score was predicted with a standard error of approximately 1.8 points (6%). CONCLUSIONS The present study demonstrated the possible avenues for using COP movement analysis to objectively detect the absent FMs and MOS-R total score in clinical settings. Although the method presented in this study requires further validation, it may complement observational GMA and be clinically useful for infant screening purposes, particularly in clinical settings where access to expertise in observational GMA is not available.
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Affiliation(s)
- Halil Ibrahim Celik
- Bilge Çocuk Special Education and Rehabilitation Center, Beysukent, Çankaya, s06800, Ankara, Turkey.
| | - Ayse Yildiz
- Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Erzurum Technical University, Erzurum, Turkey
| | - Ramazan Yildiz
- Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Erzurum Technical University, Erzurum, Turkey
| | - Akmer Mutlu
- Faculty of Physical Therapy and Rehabilitation, Developmental and Early Physiotherapy Unit, Hacettepe University, Ankara, Turkey
| | - Ruhi Soylu
- Faculty of Medicine, Department of Biophysics, Hacettepe University, Ankara, Turkey
| | - Kivilcim Gucuyener
- Faculty of Medicine, Department of Pediatrics, Section of Pediatric Neurology, Gazi University, Ankara, Turkey
| | - Aysu Duyan-Camurdan
- Faculty of Medicine, Department of Pediatrics, Section of Social Pediatrics, Gazi University, Ankara, Turkey
| | - Esin Koc
- Faculty of Medicine, Department of Pediatrics, Section of Neonatal Medicine, Gazi University, Ankara, Turkey
| | - Eray Esra Onal
- Faculty of Medicine, Department of Pediatrics, Section of Neonatal Medicine, Gazi University, Ankara, Turkey
| | - Bulent Elbasan
- Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Gazi University, Ankara, Turkey
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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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16
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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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Doi H, Furui A, Ueda R, Shimatani K, Yamamoto M, Sakurai K, Mori C, Tsuji T. Spatiotemporal patterns of spontaneous movement in neonates are significantly linked to risk of autism spectrum disorders at 18 months old. Sci Rep 2023; 13:13869. [PMID: 37620366 PMCID: PMC10449803 DOI: 10.1038/s41598-023-40368-2] [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: 04/13/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023] Open
Abstract
Infants make spontaneous movements from the prenatal period. Several studies indicate that an atypical pattern of body motion during infancy could be utilized as an early biomarker of autism spectrum disorders (ASD). However, to date, little is known about whether the body motion pattern in neonates is associated with ASD risk. The present study sought to clarify this point by examining, in a longitudinal design, the link between features of spontaneous movement at about two days after birth and ASD risk evaluated using the Modified Checklist for Autism in Toddlers by their caregivers at 18 months old. The body movement features were quantified by a recently developed markerless system of infant body motion analysis. Logistic regression analysis revealed that ASD risk at 18 months old is associated with the pattern of spontaneous movement at the neonatal stage. Further, logistic regression based on body movement features during sleep shows better performance in classifying high- and low-risk infants than during the awake state. These findings raise the possibility that early signs of ASD risk may emerge at a developmental stage far earlier than previously thought.
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Affiliation(s)
- Hirokazu Doi
- Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, Nagasaki, 852-8523, Japan.
- School of Science and Engineering, Kokushikan University, 4-28-1 Setagaya, Setagaya-ku, Tokyo, 154-8515, Japan.
- Department of Information and Management Systems Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
| | - Akira Furui
- Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8527, Japan.
| | - Rena Ueda
- Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8527, Japan
| | - Koji Shimatani
- Faculty of Health and Welfare, Prefectural University of Hiroshima, 1-1, Gakuen-machi, Mihara, Hiroshima, 734-8558, Japan
| | - Midori Yamamoto
- Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan
| | - Kenichi Sakurai
- Department of Nutrition and Metabolic Medicine, Center for Preventive Medical Sciences, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan
| | - Chisato Mori
- Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan
- Department of Bioenvironmental Medicine, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Toshio Tsuji
- Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8527, Japan.
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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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Soualmi A, Alata O, Ducottet C, Patural H, Giraud A. Mean 3D Dispersion for Automatic General Movement Assessment of Preterm Infants. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083633 DOI: 10.1109/embc40787.2023.10340961] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The General Movement assessment (GMA) is a validated assessment of brain maturation primarily based on the qualitative analysis of the complexity and the variation of spontaneous motor activity. The GMA can identify preterm infants presenting an early abnormal developmental trajectory before term-equivalent age, which permits a personalized early developmental intervention. However, GMA is time-consuming and relies on a qualitative analysis; these limitations restrict the implementation of GMA in clinical practice. In this study based on a validated dataset of 183 videos from 92 premature infants (54 males, 38 females) born <33 weeks of gestational age (GA) and acquired between 32 and 40 weeks of GA, we introduce the mean 3D dispersion (M3D) for objective quantification and classification of normal and abnormal GMA. Moreover, we have created a new 3D representation of skeleton joints which allows an objective comparison of spontaneous movements of infants of different ages and sizes. Preterm infants with normal versus abnormal GMA had a distinct M3D distribution (p <0.001). The M3D has shown a good classification performance for GMA (AUC=0.7723) and presented an accuracy of 74.1%, a sensitivity of 75.8%, and a specificity of 70.1% when using an M3D of 0.29 as a classification threshold.Clinical relevance- Our study paves the way for the development of quantitative analysis of GMA within the Neonatal Unit.
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Abbasi H, Mollet SR, Williams SA, Lim L, Battin MR, Besier TF, McMorland AJC. Deep-Learning Markerless Tracking of Infant General Movements using Standard Video Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083202 DOI: 10.1109/embc40787.2023.10340116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Monitoring spontaneous General Movements (GM) of infants 6-20 weeks post-term age is a reliable tool to assess the quality of neurodevelopment in early infancy. Abnormal or absent GMs are reliable prognostic indicators of whether an infant is at risk of developing neurological impairments and disorders such as cerebral palsy (CP). Therapeutic interventions are most effective at improving neuromuscular outcomes if administered in early infancy. Current clinical protocols require trained assessors to rate videos of infant movements, a time-intensive task. This work proposes a simple, inexpensive, and broadly applicable markerless pose-estimation approach for automatic infant movement tracking using conventional video recordings from handheld devices (e.g., tablets and mobile phones). We leverage the enhanced capabilities of deep-learning technology in image processing to identify 12 anatomical locations (3 per limb) in each video frame, tracking a baby's natural movement throughout the recordings. We validate the capability of resnet152 and a mobile-net-v2-1 to identify body-parts in unseen frames from a full-term male infant, using a novel automatic unsupervised approach that fuses likelihood outputs of a Kalman filter and the deep-nets. Both deep-net models were found to perform very well in the identification of anatomical locations in the unseen data with high average Percentage of Correct Keypoints (aPCK) performances of >99.65% across all locations.Clinical relevance-Results of this research confirm the feasibility of a low-cost and publicly accessible technology to automatically track infants' GMs and diagnose those at higher risk of developing neurological conditions early, when clinical interventions are most effective.
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Ruiz-Zafra A, Precioso D, Salvador B, Lubian-Lopez SP, Jimenez J, Benavente-Fernandez I, Pigueiras J, Gomez-Ullate D, Gontard LC. NeoCam: An Edge-Cloud Platform for Non-Invasive Real-Time Monitoring in Neonatal Intensive Care Units. IEEE J Biomed Health Inform 2023; 27:2614-2624. [PMID: 37819832 DOI: 10.1109/jbhi.2023.3240245] [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: 10/13/2023]
Abstract
In this work we introduce NeoCam, an open source hardware-software platform for video-based monitoring of preterms infants in Neonatal Intensive Care Units (NICUs). NeoCam includes an edge computing device that performs video acquisition and processing in real-time. Compared to other proposed solutions, it has the advantage of handling data more efficiently by performing most of the processing on the device, including proper anonymisation for better compliance with privacy regulations. In addition, it allows to perform various video analysis tasks of clinical interest in parallel at speeds of between 20 and 30 frames-per-second. We introduce algorithms to measure without contact the breathing rate, motor activity, body pose and emotional status of the infants. For breathing rate, our system shows good agreement with existing methods provided there is sufficient light and proper imaging conditions. Models for motor activity and stress detection are new to the best of our knowledge. NeoCam has been tested on preterms in the NICU of the University Hospital Puerta del Mar (Cádiz, Spain), and we report the lessons learned from this trial.
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22
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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: 1] [Impact Index Per Article: 1.0] [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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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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Ledwoń D, Danch-Wierzchowska M, Doroniewicz I, Kieszczyńska K, Affanasowicz A, Latos D, Matyja M, Mitas AW, Myśliwiec A. Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107455. [PMID: 36893565 DOI: 10.1016/j.cmpb.2023.107455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/15/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Neurodevelopmental assessment enables the identification of infant developmental disorders in the first months of life. Thus, the appropriate therapy can be initiated promptly, increasing the chances for correct motor function. Posture asymmetry is one of the crucial aspects evaluated during the diagnosis. Available diagnostic methods are mainly based on qualitative assessment and subjective expert opinion. Current trends in computer-aided diagnosis focus mostly on analyzing infants' spontaneous movement videos using artificial intelligence methods, based primarily on limbs movement. This study aims to develop an automatic method for determining the infant's positional asymmetry in a video recording using computer image processing methods. METHODS We made the first attempt to determine positional preferences in a recording automatically. We proposed six quantitative features describing trunk and head position based on pose estimation. As a result of our algorithm, we estimate the percentage of each trunk position in a recording using known machine learning methods. The training and test sets were created from 51 recordings collected during our research and 12 recordings from the benchmark dataset evaluated by five of our experts. The method was assessed using the leave-one-subject-out cross-validation method for ground truth video fragments and different classifiers. Log loss for multiclass classification and ROC AUC were determined to evaluate the results for both our and benchmark datasets. RESULTS In a classification of the shortened side, the QDA classifier yields the most accurate results, gaining the lowest log loss of 0.552 and AUC of 0.913. The high accuracy (92.03) and sensitivity (93.26) confirm the method's potential in screening for asymmetry. CONCLUSIONS The method allows obtaining quantitative information about positional preference, a valuable extension of basic diagnostics without additional tools and procedures. In combination with an analysis of limbs movement, it may constitute one of the elements of a novelty computer-aided infants' diagnosis system in the future.
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Affiliation(s)
- Daniel Ledwoń
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
| | - Marta Danch-Wierzchowska
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
| | - Iwona Doroniewicz
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland
| | - Katarzyna Kieszczyńska
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland
| | - Alicja Affanasowicz
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland
| | - Dominika Latos
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland
| | - Małgorzata Matyja
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland
| | - Andrzej W Mitas
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
| | - Andrzej Myśliwiec
- Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland
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25
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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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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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27
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Duan B, Lv HY, Huang Y, Xu ZM, Chen WX. Deep learning for the screening of primary ciliary dyskinesia based on cranial computed tomography. Front Physiol 2023; 14:1098893. [PMID: 37008008 PMCID: PMC10050729 DOI: 10.3389/fphys.2023.1098893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/13/2023] [Indexed: 03/17/2023] Open
Abstract
Objective: To analyze the cranial computed tomography (CT) imaging features of patients with primary ciliary dyskinesia (PCD) who have exudative otitis media (OME) and sinusitis using a deep learning model for early intervention in PCD.Methods: Thirty-two children with PCD diagnosed at the Children’s Hospital of Fudan University, Shanghai, China, between January 2010 and January 2021 who had undergone cranial CT were retrospectively analyzed. Thirty-two children with OME and sinusitis diagnosed using cranial CT formed the control group. Multiple deep learning neural network training models based on PyTorch were built, and the optimal model was trained and selected to observe the differences between the cranial CT images of patients with PCD and those of general patients and to screen patients with PCD.Results: The Swin-Transformer, ConvNeXt, and GoogLeNet training models had optimal results, with an accuracy of approximately 0.94; VGG11, VGG16, VGG19, ResNet 34, and ResNet 50, which are neural network models with fewer layers, achieved relatively strong results; and Transformer and other neural networks with more layers or neural network models with larger receptive fields exhibited a relatively weak performance. A heat map revealed the differences in the sinus, middle ear mastoid, and fourth ventricle between the patients with PCD and the control group. Transfer learning can improve the modeling effect of neural networks.Conclusion: Deep learning-based CT imaging models can accurately screen for PCD and identify differences between the cranial CT images.
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Shinya Y. How well does the General Movement Assessment predict general developmental delay in infants born very preterm/low birthweight? Dev Med Child Neurol 2023; 65:301-302. [PMID: 36114788 DOI: 10.1111/dmcn.15409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Yuta Shinya
- Graduate School of Education, The University of Tokyo, Tokyo, Japan
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29
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Sermpon N, Gima H. Relationship between fidgety movement and frequency of movement toward midline: An observational study. Early Hum Dev 2023; 177-178:105718. [PMID: 36801663 DOI: 10.1016/j.earlhumdev.2023.105718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/24/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND Infants show other movements and posture patterns during the fidgety movement period, including movement toward midline (MTM). Few studies have quantified MTM occurring during the fidgety movement period. AIMS This study aimed to examine the relationship between fidgety movements (FMs) and MTM frequency and occurrence rate per minute, from two video data sets (video attached to Prechtl video manual and accuracy data from Japan). STUDY DESIGN Observational study. SUBJECTS It encompassed 47 videos. Of these, 32 were deemed normal FMs. The study amalgamated FMs that were sporadic, abnormal, or absent into a category of aberrant (n = 15). OUTCOME MEASURES Infant video data were observed. MTM item occurrences were recorded and calculated for occurrence percentage and MTM rate of occurrence per minute. The differences between groups for the upper limbs, lower limbs, and total MTM were statistically analysed. RESULTS Twenty-three infant videos of normal FMs and seven infant videos of aberrant FMs showed MTM. Eight infant videos of aberrant FMs showed no MTM, and only four with absent FMs were included. There was a significant difference in the total MTM rate of occurrence per minute between normal FMs versus aberrant FMs (p = 0.008). CONCLUSIONS This study presented MTM frequency and rate of occurrence per minute in infants who showed FMs during the fidgety movement period. Those who showed absent FMs also demonstrated no MTM. Further study may need a larger sample size of absent FMs and information on later development.
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Affiliation(s)
- Nisasri Sermpon
- Department of Physical Therapy, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Japan; Faculty of Physical Therapy, Mahidol University, Thailand
| | - Hirotaka Gima
- Department of Physical Therapy, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Japan.
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30
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Koehler JC, Falter-Wagner CM. Digitally assisted diagnostics of autism spectrum disorder. Front Psychiatry 2023; 14:1066284. [PMID: 36816410 PMCID: PMC9928948 DOI: 10.3389/fpsyt.2023.1066284] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/11/2023] [Indexed: 02/04/2023] Open
Abstract
Digital technologies have the potential to support psychiatric diagnostics and, in particular, differential diagnostics of autism spectrum disorder in the near future, making clinical decisions more objective, reliable and evidence-based while reducing clinical resources. Multimodal automatized measurement of symptoms at cognitive, behavioral, and neuronal levels combined with artificial intelligence applications offer promising strides toward personalized prognostics and treatment strategies. In addition, these new technologies could enable systematic and continuous assessment of longitudinal symptom development, beyond the usual scope of clinical practice. Early recognition of exacerbation and simplified, as well as detailed, progression control would become possible. Ultimately, digitally assisted diagnostics will advance early recognition. Nonetheless, digital technologies cannot and should not substitute clinical decision making that takes the comprehensive complexity of individual longitudinal and cross-section presentation of autism spectrum disorder into account. Yet, they might aid the clinician by objectifying decision processes and provide a welcome relief to resources in the clinical setting.
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Affiliation(s)
- Jana Christina Koehler
- Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich, Germany
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31
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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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Design and Construct Validity of a Postural Control Test for Pre-Term Infants. Diagnostics (Basel) 2022; 13:diagnostics13010096. [PMID: 36611388 PMCID: PMC9818709 DOI: 10.3390/diagnostics13010096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/24/2022] [Accepted: 12/25/2022] [Indexed: 12/30/2022] Open
Abstract
A review of the literature indicated that the greatest prognostic value for predicting motor impairment in high-risk infants is the absence of fidgety movements (FMs) at 3 months of post-term age. The purpose of the present study was to characterize a new posturometric test (PT) based on a center-of-pressure (CoP) movement analysis, in terms of design and construct validity, for the detection of postural control disturbances in pre-term infants. The comparative studies were carried out between pre-term infants who presented normal FMs (18 participants) and infants with absent FMs (19 participants), which consisted of the analysis of the CoP trajectory and CoP area in supine and prone positions using the force platform. New PT was performed simultaneously with GMs recorded using a force platform. Statistical analyses revealed significant differences between the groups of infants who presented absent FMs and normal FMs for almost all CoP parameters describing spontaneous sway in the supine position. Based on these preliminary results, it can be concluded, that the application of PT based on the analysis of CoP trajectory, area, and velocity in the supine position has been demonstrated to be valid for the detection of postural control disturbances in pre-term infants.
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Davidson SA, Ward R, Elliott C, Harris C, Bear N, Thornton A, Salt A, Valentine J. From guidelines to practice: A retrospective clinical cohort study investigating implementation of the early detection guidelines for cerebral palsy in a state-wide early intervention service. BMJ Open 2022; 12:e063296. [PMID: 36428013 PMCID: PMC9703326 DOI: 10.1136/bmjopen-2022-063296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVES To report on knowledge translation strategies and outcomes from the implementation of the early detection guidelines for cerebral palsy (CP) in a state-wide tertiary early intervention (EI) service and investigate the impact of social determinants on clinical services. DESIGN Retrospective longitudinal cohort study. SETTING The Western Australia tertiary paediatric EI service. PARTICIPANTS EI clinicians, consumers and children using the EI service. OUTCOME MEASURES Knowledge translation strategies including consumer perspectives, clinician training and Communities of Practice (CoP) guided implementation. We measured changes in referral number and age, delivery of early detection and intervention following the implementation of the guidelines. Exposure to adverse childhood experiences (ACEs), appointment non-attendance (DNA) rates, remoteness and socioeconomic quintiles were used to measure social determinants of health using negative binomial (Incidence Rate Ratios, IRR) and logistic regression (Odds Ratios, ORs). RESULTS Ten consumers participated in Focus Groups, 100 clinicians were trained and 22 clinicians established a monthly CoP. Referrals increased fourfold to 511 children. Corrected gestational age at referral decreased from a median of 16.1 to 5.1 months (p<0.001) and at first appointment from 18.8 to 6.8 months (p<0.001). Children living in social disadvantage had the highest DNA risk (quintile 1 vs 5: IRR 2.2, 95% CI 1.1 to 4.6, p=0.037). Children exposed to ACEs had higher odds of living in social disadvantage (quintile 1 vs 5, OR=3.8, 95% CI 1.4 to 10.0, p=0.007). No significant association was found between remoteness and DNA rate or ACE score. CONCLUSIONS Implementation strategies reduced referral age and improved the delivery of early detection assessments. Further investigation of the association between social disadvantage, DNA risk and ACE score is required in the development of a state-wide early detection network.
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Affiliation(s)
- Sue-Anne Davidson
- School of Allied Health, Curtin University, Perth, Western Australia, Australia
- Kids Rehab WA, Perth Children's Hospital, Nedlands, Western Australia, Australia
- Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Roslyn Ward
- School of Allied Health, Curtin University, Perth, Western Australia, Australia
| | - Catherine Elliott
- School of Allied Health, Curtin University, Perth, Western Australia, Australia
- Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Courtenay Harris
- School of Allied Health, Curtin University, Perth, Western Australia, Australia
| | - Natasha Bear
- Institute for Health Research, Notre Dame University, Perth, Western Australia, Australia
| | - Ashleigh Thornton
- Telethon Kids Institute, Nedlands, Western Australia, Australia
- Division of Paediatrics, Medical School, The University of Western Australia, Perth, Western Australia, Australia
| | - Alison Salt
- School of Allied Health, Curtin University, Perth, Western Australia, Australia
- Kids Rehab WA, Perth Children's Hospital, Nedlands, Western Australia, Australia
- Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Jane Valentine
- School of Allied Health, Curtin University, Perth, Western Australia, Australia
- Kids Rehab WA, Perth Children's Hospital, Nedlands, Western Australia, Australia
- Telethon Kids Institute, Nedlands, Western Australia, Australia
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Balta D, Kuo H, Wang J, Porco IG, Morozova O, Schladen MM, Cereatti A, Lum PS, Della Croce U. Characterization of Infants' General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:7426. [PMID: 36236525 PMCID: PMC9572717 DOI: 10.3390/s22197426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Cerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale. Automated analysis of GM performed using low-cost instrumentation in the home may be used to estimate quantitative metrics predictive of movement disorders. This study explored if infants' GM may be successfully evaluated in a familiar environment by processing the 3D trajectories of points of interest (PoI) obtained from recordings of a single commercial RGB-D sensor. The RGB videos were processed using an open-source markerless motion tracking method which allowed the estimation of the 2D trajectories of the selected PoI and a purposely developed method which allowed the reconstruction of their 3D trajectories making use of the data recorded with the depth sensor. Eight infants' GM were recorded in the home at 3, 4, and 5 months of age. Eight GM metrics proposed in the literature in addition to a novel metric were estimated from the PoI trajectories at each timepoint. A pediatric neurologist and physiatrist provided an overall clinical evaluation from infants' video. Subsequently, a comparison between metrics and clinical evaluation was performed. The results demonstrated that GM metrics may be meaningfully estimated and potentially used for early identification of movement disorders.
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Affiliation(s)
- Diletta Balta
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - HsinHung Kuo
- Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USA
| | - Jing Wang
- Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USA
| | | | - Olga Morozova
- Children’s National Hospital, Washington, DC 20010, USA
| | - Manon Maitland Schladen
- Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USA
- Department of Rehabilitation Medicine, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - Peter Stanley Lum
- Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USA
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
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Biomarker und Neuromonitoring zur Entwicklungsprognose nach perinataler Hirnschädigung. Monatsschr Kinderheilkd 2022; 170:688-703. [PMID: 35909417 PMCID: PMC9309449 DOI: 10.1007/s00112-022-01542-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2022] [Indexed: 11/02/2022]
Abstract
Das sich entwickelnde Gehirn ist in der Perinatalperiode besonders empfindlich für eine Vielzahl von Insulten, wie z. B. Extremfrühgeburtlichkeit und perinatale Asphyxie. Ihre Komplikationen können zu lebenslangen neurokognitiven, sensorischen und psychosozialen Einschränkungen führen; deren Vorhersage bleibt eine Herausforderung. Eine Schlüsselfunktion kommt der möglichst exakten Identifikation von Hirnläsionen und funktionellen Störungen zu. Die Prädiktion stützt sich auf frühe diagnostische Verfahren und die klinische Erfassung der Meilensteine der Entwicklung. Zur klinischen Diagnostik und zum Neuromonitoring in der Neonatal- und frühen Säuglingsperiode stehen bildgebende Verfahren zur Verfügung. Hierzu zählen zerebrale Sonographie, MRT am errechneten Termin, amplitudenintegriertes (a)EEG und/oder klassisches EEG, Nah-Infrarot-Spektroskopie, General Movements Assessment und die frühe klinische Nachuntersuchung z. B. mithilfe der Hammersmith Neonatal/Infant Neurological Examination. Innovative Biomarker und -muster (Omics) sowie (epi)genetische Prädispositionen sind Gegenstand wissenschaftlicher Untersuchungen. Neben der Erfassung klinischer Risiken kommt psychosozialen Faktoren im Umfeld des Kindes eine entscheidende Rolle zu. Eine möglichst akkurate Prognose ist mit hohem Aufwand verbunden, jedoch zur gezielten Beratung der Familien und der Einleitung von frühen Interventionen, insbesondere vor dem Hintergrund der hohen Plastizität des sich entwickelnden Gehirns, von großer Bedeutung. Diese Übersichtsarbeit fokussiert die Charakterisierung der oben genannten Verfahren und ihrer Kombinationsmöglichkeiten. Zudem wird ein Ausblick gegeben, wie innovative Techniken in Zukunft die Prädiktion der Entwicklung und Nachsorge dieser Kinder vereinfachen können.
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Groos D, Adde L, Aubert S, Boswell L, de Regnier RA, Fjørtoft T, Gaebler-Spira D, Haukeland A, Loennecken M, Msall M, Möinichen UI, Pascal A, Peyton C, Ramampiaro H, Schreiber MD, Silberg IE, Songstad NT, Thomas N, Van den Broeck C, Øberg GK, Ihlen EA, Støen R. Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk. JAMA Netw Open 2022; 5:e2221325. [PMID: 35816301 PMCID: PMC9274325 DOI: 10.1001/jamanetworkopen.2022.21325] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
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 < .001), but no significant improvement in accuracy was observed compared with the GMA tool (85.9%; 95% CI, 78.9%-91.3%; P = .11). The deep learning prediction model had higher sensitivity among infants with nonambulatory CP (100%; 95% CI, 63.1%-100%) vs ambulatory CP (58.3%; 95% CI, 27.7%-84.8%; P = .02) and spastic bilateral CP (92.3%; 95% CI, 64.0%-99.8%) vs spastic unilateral CP (42.9%; 95% CI, 9.9%-81.6%; P < .001). 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.
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Affiliation(s)
- Daniel Groos
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lars Adde
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Clinical Services, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Sindre Aubert
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lynn Boswell
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
| | - Raye-Ann de Regnier
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
- Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Toril Fjørtoft
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Clinical Services, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Deborah Gaebler-Spira
- Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Shirley Ryan AbilityLab, Chicago, Illinois
| | - Andreas Haukeland
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Loennecken
- Division of Paediatric and Adolescent Medicine, Oslo University Hospital, Oslo, Norway
| | - Michael Msall
- Section of Developmental and Behavioral Pediatrics, University of Chicago, Comer Children’s Hospital, Chicago, Illinois
- Kennedy Research Center on Neurodevelopmental Disabilities, University of Chicago, Comer Children’s Hospital, Chicago, Illinois
| | - Unn Inger Möinichen
- Division of Paediatric and Adolescent Medicine, Oslo University Hospital, Oslo, Norway
| | - Aurelie Pascal
- Department of Rehabilitation Sciences and Physiotherapy, Ghent University, Ghent, Belgium
| | - Colleen Peyton
- Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Pediatrics, University of Chicago, Comer Children’s Hospital, Chicago, Illinois
| | - Heri Ramampiaro
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Michael D. Schreiber
- Department of Pediatrics, University of Chicago, Comer Children’s Hospital, Chicago, Illinois
| | | | - Nils Thomas Songstad
- Department of Pediatrics and Adolescent Medicine, University Hospital of North Norway, Tromsø, Norway
| | - Niranjan Thomas
- Department of Neonatology, Christian Medical College Vellore, Vellore, Tamil Nadu, India
| | | | - Gunn Kristin Øberg
- Division of Paediatric and Adolescent Medicine, Oslo University Hospital, Oslo, Norway
- Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway
| | - Espen A.F. Ihlen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ragnhild Støen
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neonatology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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Haffner DN, Sankovic A. A Neurologist's Guide to Neonatal Neurodevelopmental Assessments and Preterm Brain Injury. Semin Pediatr Neurol 2022; 42:100974. [PMID: 35868724 DOI: 10.1016/j.spen.2022.100974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/14/2022] [Accepted: 04/14/2022] [Indexed: 11/19/2022]
Abstract
Despite advances in medical care and improved survival of extremely preterm infants, rates of neurodevelopmental impairment remain high. Outcomes are significantly improved with early intervention, but infants must be appropriately identified to facilitate services. Neuroimaging provides important information regarding neurodevelopmental outcomes but prognosticating and communicating risk remains challenging. Standardized neonatal neurodevelopmental assessments provide supplemental information to aid in the identification of high-risk infants and counseling for their families.
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Affiliation(s)
- Darrah N Haffner
- Division of Pediatric Neurology, Nationwide Children's Hospital and the Ohio State University, 700 Children's Dr Columbus, 43205 OH, United States.
| | - Alexandra Sankovic
- Division of Pediatric Neurology, Nationwide Children's Hospital and the Ohio State University, 700 Children's Dr Columbus, 43205 OH, United States
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Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. SENSORS 2022; 22:s22124386. [PMID: 35746168 PMCID: PMC9231145 DOI: 10.3390/s22124386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/30/2022] [Accepted: 06/07/2022] [Indexed: 02/06/2023]
Abstract
Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor’s office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0–4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day.
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Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study. CHILDREN (BASEL, SWITZERLAND) 2022; 9:children9060843. [PMID: 35740780 PMCID: PMC9222200 DOI: 10.3390/children9060843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/30/2022] [Accepted: 06/02/2022] [Indexed: 11/20/2022]
Abstract
The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks’ gestational age or birthweight ≤ 1500 g evaluated at 3−5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7−292/7 weeks) and 960 g (range 769−1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18−24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.
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40
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Cott R, Hagmann C, Etter R, Latal B. [Differences in the Distribution of the General Movements Classification Between Neonatal Risk Groups in the Children's Hospital Zurich: An Observational Study]. Z Geburtshilfe Neonatol 2022; 226:265-273. [PMID: 35672004 DOI: 10.1055/a-1808-2843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Neonatal infants are at increased risk for motor development disorders. OBJECTIVE To compare General Movements (GMs) classification between three neonatal risk groups, correlate the GMs Assessment (GMA) with a standardized developmental neurological examination (SDNE) and determine risk factors for abnormal GMs. METHODS Monocentric observational study with three risk groups (children with operated congenital heart disease (CHD) n=26, with operated congenital gastrointestinal malformations (CGM) n=17 and with fetal operated myelomeningocele (MMC) n=12 who underwent inpatient video-based examination. GMA was evaluated according to Hadders-Algra classification and divided into 4 categories: normal optimal (NO), normal suboptimal (NS), mildly abnormal (MA), definitely abnormal (DA). RESULTS The distribution was as follows: CHD 80.8% NS, 19.2% MA, CGM 5.9% NO, 64.7% NS, 29.4% MA, MMC upper extremities 100% NS, lower extremities 33.3% NS, 33.3% MA and 33.3% DA (group comparison Kruskal-Wallis 10.729, p=0.003). GMA correlated significantly with SDNE (Spearman r s=0.869, p<0.001). Binary logistic regression analysis showed that only gestational age (Chi2=11.93, p<0.001) correlated with abnormal GMs. CONCLUSION The majority of children showed normal GMs. Children with MMC and those with lower gestational age showed an increased risk of abnormal GMs. The GMA and SDNE represent complementary "bedside tools" to detect early motor abnormalities.
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Affiliation(s)
- Rachel Cott
- Abteilung Entwicklungspädiatrie, Universitäts-Kinderspital Zürich, Zürich, Schweiz
| | - Cornelia Hagmann
- Abteilung Intensivmedizin und Neonatologie, Universitäts-Kinderspital Zürich, Zürich, Schweiz
| | - Ruth Etter
- Abteilung Entwicklungspädiatrie, Universitäts-Kinderspital Zürich, Zürich, Schweiz
| | - Bea Latal
- Abteilung Entwicklungspädiatrie, Universitäts-Kinderspital Zürich, Zürich, Schweiz
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Shin HI, Shin HI, Bang MS, Kim DK, Shin SH, Kim EK, Kim YJ, Lee ES, Park SG, Ji HM, Lee WH. Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants. Sci Rep 2022; 12:3138. [PMID: 35210507 PMCID: PMC8873498 DOI: 10.1038/s41598-022-07139-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/11/2022] [Indexed: 12/23/2022] Open
Abstract
This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The Hammersmith Infant Neurological Examination (HINE) was performed in infants at 4 months of corrected age. Joint positional data were extracted using a pretrained pose-estimation model. Complexity and similarity indices of joint angle and angular velocity in terms of sample entropy and Pearson correlation coefficient were compared between the infants with HINE < 60 and ≥ 60. Video images of spontaneous movements were recorded in 65 preterm infants at term-equivalent age. Complexity indices of joint angles and angular velocities differed between the infants with HINE < 60 and ≥ 60 and correlated positively with HINE scores in most of the joints at the upper and lower extremities (p < 0.05). Similarity indices between each joint angle or joint angular velocity did not differ between the two groups in most of the joints at the upper and lower extremities. Quantitative assessments of spontaneous movements in preterm infants are feasible using a deep learning algorithm and sample entropy. The results indicated that complexity indices of joint movements at both the upper and lower extremities can be potential candidates for detecting developmental outcomes in preterm infants.
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Affiliation(s)
- Hyun Iee Shin
- 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, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Moon Suk Bang
- Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, 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
| | - Yoo-Jin Kim
- Department of Pediatrics, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Eun Sun Lee
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Pediatrics, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Seul Gi Park
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hye Min Ji
- Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Woo Hyung Lee
- Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
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Estimation of Gross Motor Functions in Children with Cerebral Palsy Using Zebris FDM-T Treadmill. J Clin Med 2022; 11:jcm11040954. [PMID: 35207227 PMCID: PMC8880133 DOI: 10.3390/jcm11040954] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 02/05/2022] [Accepted: 02/08/2022] [Indexed: 12/10/2022] Open
Abstract
A standardized observational instrument designed to measure change in gross motor function over time in children with cerebral palsy is the Gross Motor Function Measure (GMFM). The process of evaluating a value for the GMFM index can be time consuming. It typically takes 45 to 60 min for the patient to complete all tasks, sometimes in two or more sessions. The diagnostic procedure requires trained and specialized therapists. The paper presents the estimation of the GMFM measure for patients with cerebral palsy based on the results of the Zebris FDM-T treadmill. For this purpose, the regression analysis was used. Estimations based on the Generalized Linear Regression were assessed using different error metrics. The results obtained showed that the GMFM score can be estimated with acceptable accuracy. Because the Zebris FDM-T is a widely used device in gait rehabilitation, our method has the potential to be widely adopted for objective diagnostics of children with cerebral palsy.
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Leo M, Bernava GM, Carcagnì P, Distante C. Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions. SENSORS (BASEL, SWITZERLAND) 2022; 22:866. [PMID: 35161612 PMCID: PMC8839211 DOI: 10.3390/s22030866] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 06/14/2023]
Abstract
Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby's movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.
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Affiliation(s)
- Marco Leo
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council of Italy, Via Monteroni Snc, 73100 Lecce, Italy; (P.C.); (C.D.)
| | - Giuseppe Massimo Bernava
- Institute for Chemical-Physical Processes (IPCF), National Research Council of Italy, Viale Ferdinando Stagno d’Alcontres 37, 98158 Messina, Italy;
| | - Pierluigi Carcagnì
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council of Italy, Via Monteroni Snc, 73100 Lecce, Italy; (P.C.); (C.D.)
| | - Cosimo Distante
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council of Italy, Via Monteroni Snc, 73100 Lecce, Italy; (P.C.); (C.D.)
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44
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Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review. Cognit Comput 2022. [DOI: 10.1007/s12559-021-09940-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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45
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Juneja M, Einspieler C, Khosla M. General Movement Assessment in Prediction of Neurodevelopmental Disability and Cerebral Palsy. Indian Pediatr 2022; 59. [PMID: 36263493 PMCID: PMC9632578 DOI: 10.1007/s13312-022-2616-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Monica Juneja
- Department of Pediatrics, Maulana Azad Medical College and Lok Nayak Hospital, New Delhi, India
| | | | - Megha Khosla
- Department of Pediatrics, Maulana Azad Medical College and Lok Nayak Hospital, New Delhi, India
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Esposito G, Marschik PB, Nordahl-Hansen A. Technological advancements in the assessment and intervention of developmental disabilities. RESEARCH IN DEVELOPMENTAL DISABILITIES 2021; 119:104088. [PMID: 34610524 DOI: 10.1016/j.ridd.2021.104088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Gianluca Esposito
- Affiliative Behavior and Physiology Lab, Department of Psychology and Cognitive Science, University of Trento, Trento, Italy; Social and Affective Neuroscience Lab, Psychology Program, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
| | - Peter B Marschik
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Germany and Leibniz Science Campus Primate Cognition, Göttingen, Germany; iDN - Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Austria; Center of Neurodevelopmental Disorders (KIND), Karolinska Institutet, Stockholm, Sweden.
| | - Anders Nordahl-Hansen
- Faculty of Teacher Education and Languages, Østfold University College, Halden, Norway.
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Redd CB, Karunanithi M, Boyd RN, Barber LA. Technology-assisted quantification of movement to predict infants at high risk of motor disability: A systematic review. RESEARCH IN DEVELOPMENTAL DISABILITIES 2021; 118:104071. [PMID: 34507051 DOI: 10.1016/j.ridd.2021.104071] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 07/08/2021] [Accepted: 08/20/2021] [Indexed: 05/23/2023]
Abstract
AIM To systematically review the scientific literature to determine the predictive validity of technology-assisted measures of observable infant movement in infants less than six months of corrected age (CA) to identify high-risk of motor disability. METHOD A comprehensive search for randomised and non-randomised controlled trials, cohort studies and cross-comparison trials was performed on five electronic databases up to Feb 2021. Studies were included if they quantified infant movement before 6 months CA using some method of technology-assistance and compared the instrumented measure to a diagnostic clinical measure of neurodevelopment. Studies were excluded if they did not report a technology-assisted measure of infant movement. Methodological quality of the included studies was assessed using the Downs and Black scale. RESULTS 23 studies met the full inclusion and exclusion criteria. Methodological quality of the included papers ranged from 9 to 24 (out of 26) on the Downs and Black scale. Infant movement assessments included the General Movements Assessment (GMA) and domains of the Hammersmith Infant Neurological Assessment (HINE). Studies used 2D video recordings, RGB-Depth recordings, accelerometry, and electromagnetic motion tracking technologies to quantify movement. Analytical approaches and movement features of interest were individual and varied. Technology assisted quantitative assessments identified cases of later diagnosed CP with sensitivity 44-100 %, specificity 59-95 %, Area under the ROC Curve 82-93 %; and typical development with sensitivity range 30-46 %, specificity 88-95 %, Area under the ROC Curve 68 %. INTERPRETATION Technology-assisted assessments of movement in infants less than 6 months CA using current technologies are feasible. Validation of measurement tools are limited. Although methods and results appear promising clinical uptake of technology-assisted assessments remains limited.
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Affiliation(s)
- Christian B Redd
- CSIRO, The Australian e-Health Research Centre, Brisbane, Australia; The University of Queensland, Queensland Cerebral Palsy and Rehabilitation Research Centre, Child Health Research Centre, Faculty of Medicine, Brisbane, Australia.
| | | | - Roslyn N Boyd
- The University of Queensland, Queensland Cerebral Palsy and Rehabilitation Research Centre, Child Health Research Centre, Faculty of Medicine, Brisbane, Australia
| | - Lee A Barber
- The University of Queensland, Queensland Cerebral Palsy and Rehabilitation Research Centre, Child Health Research Centre, Faculty of Medicine, Brisbane, Australia; Griffith University, School of Health Sciences and Social Work, Nathan, Australia
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48
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Fontana C, Ottaviani V, Veneroni C, Sforza SE, Pesenti N, Mosca F, Picciolini O, Fumagalli M, Dellacà RL. An Automated Approach for General Movement Assessment: A Pilot Study. Front Pediatr 2021; 9:720502. [PMID: 34513767 PMCID: PMC8424086 DOI: 10.3389/fped.2021.720502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 07/28/2021] [Indexed: 01/07/2023] Open
Abstract
Objective: The objective of the study was to develop an automatic quantitative approach to identify infants with abnormal movements of the limbs at term equivalent age (TEA) compared with general movement assessment (GMA). Methods: GMA was performed at TEA by a trained operator in neonates with neurological risk. GMs were classified as normal (N) or abnormal (Ab), which included poor repertoire and cramped synchronized movements. The signals from four micro-accelerometers placed on all limbs were recorded for 10 min simultaneously. A global index (KC_index), quantifying the characteristics of individual limb movements and the coordination among the limbs, was obtained by adding normalized kurtosis of the distribution of the first principal component of the acceleration signals to the cross-correlation of the jerk for the upper and lower limbs. Results: Sixty-eight infants were studied. A KC_index cut-off of 201.5 (95% CI: 199.9-205.0) provided specificity = 0.86 and sensitivity = 0.88 in identifying infants with Ab movements. Conclusions: KC_index provides an automatic and quantitative measure that may allow the identification of infants who require further neurological evaluation.
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Affiliation(s)
- Camilla Fontana
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Valeria Ottaviani
- TechRes Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano University, Milan, Italy
| | - Chiara Veneroni
- TechRes Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano University, Milan, Italy
| | - Sofia E. Sforza
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, NICU, Milan, Italy
| | - Nicola Pesenti
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, NICU, Milan, Italy
- Department of Statistics and Quantitative Methods, Division of Biostatistics, Epidemiology and Public Health, University of Milano-Bicocca, Milan, Italy
| | - Fabio Mosca
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, NICU, Milan, Italy
| | - Odoardo Picciolini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Milano-Pediatric Physical Medicine and Rehabilitation Unit, Milan, Italy
| | - Monica Fumagalli
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, NICU, Milan, Italy
| | - Raffaele L. Dellacà
- TechRes Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano University, Milan, Italy
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49
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Reich S, Zhang D, Kulvicius T, Bölte S, Nielsen-Saines K, Pokorny FB, Peharz R, Poustka L, Wörgötter F, Einspieler C, Marschik PB. Novel AI driven approach to classify infant motor functions. Sci Rep 2021; 11:9888. [PMID: 33972661 PMCID: PMC8110753 DOI: 10.1038/s41598-021-89347-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/12/2021] [Indexed: 02/02/2023] Open
Abstract
The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network's architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future.
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Affiliation(s)
- Simon Reich
- University Medical Center Göttingen, Child and Adolescent Psychiatry and Psychotherapy, 37075, Göttingen, Germany
| | - Dajie Zhang
- University Medical Center Göttingen, Child and Adolescent Psychiatry and Psychotherapy, 37075, Göttingen, Germany
- Division of Phoniatrics, Research Unit interdisciplinary Developmental Neuroscience, Medical University of Graz, 8036, Graz, Austria
- Leibniz ScienceCampus Primate Cognition, 37075, Göttingen, Germany
| | - Tomas Kulvicius
- University Medical Center Göttingen, Child and Adolescent Psychiatry and Psychotherapy, 37075, Göttingen, Germany
- Georg-August University Göttingen, Third Institute of Physics-Biophysics, 37077, Göttingen, Germany
| | - Sven Bölte
- Department of Women's and Children's Health, Karolinska Institutet, Center of Neurodevelopmental Disorders (KIND), 113 30, Stockholm, Sweden
| | - Karin Nielsen-Saines
- University of California, David Geffen School of Medicine, Los Angeles, CA, 90095, USA
| | - Florian B Pokorny
- Division of Phoniatrics, Research Unit interdisciplinary Developmental Neuroscience, Medical University of Graz, 8036, Graz, Austria
- University of Augsburg, EIHW-Chair of Embedded Intelligence for Health Care and Wellbeing, 86159, Augsburg, Germany
| | - Robert Peharz
- Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands
| | - Luise Poustka
- University Medical Center Göttingen, Child and Adolescent Psychiatry and Psychotherapy, 37075, Göttingen, Germany
- Leibniz ScienceCampus Primate Cognition, 37075, Göttingen, Germany
| | - Florentin Wörgötter
- Leibniz ScienceCampus Primate Cognition, 37075, Göttingen, Germany
- Georg-August University Göttingen, Third Institute of Physics-Biophysics, 37077, Göttingen, Germany
| | - Christa Einspieler
- Division of Phoniatrics, Research Unit interdisciplinary Developmental Neuroscience, Medical University of Graz, 8036, Graz, Austria
| | - Peter B Marschik
- University Medical Center Göttingen, Child and Adolescent Psychiatry and Psychotherapy, 37075, Göttingen, Germany.
- Division of Phoniatrics, Research Unit interdisciplinary Developmental Neuroscience, Medical University of Graz, 8036, Graz, Austria.
- Leibniz ScienceCampus Primate Cognition, 37075, Göttingen, Germany.
- Department of Women's and Children's Health, Karolinska Institutet, Center of Neurodevelopmental Disorders (KIND), 113 30, Stockholm, Sweden.
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