المؤلفون: Kulvicius, Tomas, Zhang, Dajie, Poustka, Luise, Bölte, Sven, Jahn, Lennart, Flügge, Sarah, Kraft, Marc, Zweckstetter, Markus, Nielsen-Saines, Karin, Wörgötter, Florentin, Marschik, Peter B

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence

الوصف: There is a recent boom in the development of AI solutions to facilitate and enhance diagnostic procedures for established clinical tools. To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. Available deep learning tools, all of which are based on single sensor modalities, are however still considerably inferior to that of well-trained human assessors. These approaches are hardly comparable as all models are designed, trained and evaluated on proprietary/silo-data sets. With this study we propose a sensor fusion approach for assessing fidgety movements (FMs) comparing three different sensor modalities (pressure, inertial, and visual sensors). Various combinations and two sensor fusion approaches (late and early fusion) for infant movement classification were tested to evaluate whether a multi-sensor system outperforms single modality assessments. The performance of the three-sensor fusion (classification accuracy of 94.5\%) was significantly higher than that of any single modality evaluated, suggesting the sensor fusion approach is a promising avenue for automated classification of infant motor patterns. The development of a robust sensor fusion system may significantly enhance AI-based early recognition of neurofunctions, ultimately facilitating automated early detection of neurodevelopmental conditions.

الوصول الحر: http://arxiv.org/abs/2406.09014Test

المؤلفون: Tomantschger, Iris, Herrero, Dafne, Einspieler, Christa, Hamamura, Cristina, Voos, Mariana Calil, Marschik, Peter B

المصدر: Revista de Saúde Pública. January 2018 52

مصطلحات موضوعية: Infant, Premature, Cerebral Palsy, Psychomotor Disorders, Psychomotor Performance, Motor Activity, Disability Evaluation

الوصف: Abnormal general movements are among the most reliable markers for cerebral palsy. General movements are part of the spontaneous motor repertoire and are present from early fetal life until the end of the first half year after term. In addition to its high sensitivity (98%) and specificity (91%), the assessment of general movements is non-invasive and time- and cost-efficient. It is therefore ideal for assessing the integrity of the young nervous system, most notably in lowresource settings. Studies on the general movements assessment in low- and middle-income countries such as China, India, Iran, or South Africa are still rare but increasing. In Brazil, too, researchers have demonstrated that the evaluation of general movements adds to the functional assessment of the young nervous system. Applying general movements assessment in vulnerable populations in Brazil is therefore highly recommended.

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الوصول الحر: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102018000100500Test

المؤلفون: Einspieler, Christa, Peharz, Robert, Marschik, Peter B.

المصدر: Jornal de Pediatria. June 2016 92(3)

مصطلحات موضوعية: Cerebral palsy, Fidgety movements, General movements, Infant, Prediction, Video analysis

الوصف: Objectives: To describe fidgety movements (FMs), i.e., the spontaneous movement pattern that typically occurs at 3–5 months after term age, and discuss its clinical relevance. Sources: A comprehensive literature search was performed using the following databases: MEDLINE/PubMed, CINAHL, The Cochrane Library, Science Direct, PsycINFO, and EMBASE. The search strategy included the MeSH terms and search strings (‘fidgety movement*’) OR [(‘general movement*’) AND (‘three month*’) OR (‘3 month*’)], as well as studies published on the General Movements Trust website (www.general-movements-trust.info). Summary of the data: Virtually all infants develop normally if FMs are present and normal, even if their brain ultrasound findings and/or clinical histories indicate a disposition to later neurological deficits. Conversely, almost all infants who never develop FMs have a high risk for neurological deficits such as cerebral palsy, and for genetic disorders with a late onset. If FMs are normal but concurrent postural patterns are not age-adequate or the overall movement character is monotonous, cognitive and/or language skills at school age will be suboptimal. Abnormal FMs are unspecific and have a low predictive power, but occur exceedingly in infants later diagnosed with autism. Conclusions: Abnormal, absent, or sporadic FMs indicate an increased risk for later neurological dysfunction, whereas normal FMs are highly predictive of normal development, especially if they co-occur with other smooth and fluent movements. Early recognition of neurological signs facilitates early intervention. It is important to re-assure parents of infants with clinical risk factors that the neurological outcome will be adequate if FMs develop normally.

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الوصول الحر: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0021-75572016000400064Test

المؤلفون: Marschik, Peter B, Kwong, Amanda KL, Silva, Nelson, Olsen, Joy E, Schulte-Ruether, Martin, Bolte, Sven, Ortqvist, Maria, Eeles, Abbey, Poustka, Luise, Einspieler, Christa, Nielsen-Saines, Karin, Zhang, Dajie, Spittle, Alicia J

مصطلحات موضوعية: Computer Science - Human-Computer Interaction, Computer Science - Computers and Society, Computer Science - Software Engineering, J.4

الوصف: The Prechtl General Movements Assessment (GMA) has become a clinician and researcher tool-box for evaluating neurodevelopment in early infancy. Given it involves 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 App 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-sites data collection, adaption to local context and privacy laws, employment of user feedback, and sustainable IT structures enabling continuous software updating.
Comment: 19 pages, 3 Figures, 2 Tables

الوصول الحر: http://arxiv.org/abs/2303.14699Test

المؤلفون: Kulvicius, Tomas, Lang, Sigrun, Widmann, Claudius AA, Hansmann, Nina, Holzinger, Daniel, Poustka, Luise, Zhang, Dajie, Marschik, Peter B

مصطلحات موضوعية: Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing

الوصف: Theoretical background: early verbal development is not yet fully understood, especially in its formative phase. Research question: can a reliable, easy-to-use coding scheme for the classification of early infant vocalizations be defined that is applicable as a basis for further analysis of language development? Methods: in a longitudinal study of 45 neurotypical infants, we analyzed vocalizations of the first 4 months of life. Audio segments were assigned to 5 classes: (1) Voiced and (2) Voiceless vocalizations; (3) Defined signal; (4) Non-target; (5) Nonassignable. Results: Two female coders with different experience achieved high agreement without intensive training. Discussion and Conclusion: The reliable scheme can be used in research and clinical settings for efficient coding of infant vocalizations, as a basis for detailed manual and machine analyses.
Comment: This paper is in German

الوصول الحر: http://arxiv.org/abs/2303.08239Test

المؤلفون: Örtqvist, Maria, Marschik, Peter B, Toldo, Moreno, Zhang, Dajie, Fajardo‐Martinez, Viviana, Nielsen‐Saines, Karin, Ådén, Ulrika, Einspieler, Christa

المصدر: Acta Paediatrica. 112(6)

مصطلحات موضوعية: Paediatrics, Biomedical and Clinical Sciences, Infant Mortality, Clinical Research, Pediatric, Perinatal Period - Conditions Originating in Perinatal Period, Preterm, Low Birth Weight and Health of the Newborn, Lung, Reproductive health and childbirth, Infant, Newborn, Pregnancy, Female, Humans, Infant, Reproducibility of Results, COVID-19, SARS-CoV-2, Parturition, Risk Factors, Movement, general movement assessment, high-risk infants, motor optimality score-revised, preterm birth, reliability, Sars-CoV-2, Paediatrics and Reproductive Medicine, Pediatrics

الوصف: AimTo assess the inter-assessor reliability of the Motor Optimality Score-Revised (MOS-R) when used in infants at elevated likelihood for adverse neurological outcome.MethodsMOS-R were assessed in three groups of infants by two assessors/cohort. Infants were recruited from longitudinal projects in Sweden (infants born extremely preterm), India (infants born in low-resource communities) and the USA (infants prenatally exposed to SARS-CoV-2). Intraclass correlation coefficients (ICC) and kappa (κw) were applied. ICC of MOS-R subcategories and total scores were presented for cohorts together and separately and for age-spans: 9-12, 13-16 and 17-25-weeks post-term age.Results252 infants were included (born extremely preterm n = 97, born in low-resource communities n = 97, prenatally SARS-CoV-2 exposed n = 58). Reliability of the total MOS-R was almost perfect (ICC: 0.98-0.99) for all cohorts, together and separately. Similar result was found for age-spans (ICC: 0.98-0.99). Substantial to perfect reliability was shown for the MOS-R subcategories (κw: 0.67-1.00), with postural patterns showing the lowest value 0.67.ConclusionThe MOS-R can be used in high-risk populations with substantial to perfect reliability, both in regards of total/subcategory scores as well as in different age groups. However, the subcategory postural patterns as well as the clinical applicability of the MOS-R needs further study.

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الوصول الحر: https://escholarship.org/uc/item/5gr4s2qvTest

المؤلفون: Aizawa, Carolina Yuri Panvequio, Caron, Deyse Mayara Rodrigues, Souza, Carolina Barbosa de, Kozima, Paula Fernanda Augusto, Damasceno, Luana, Einspieler, Christa, Marschik, Peter B., Brasil, Patrícia, Schmitt, Ana Carolina Basso, Nielsen-Saines, Karin, Hasue, Renata Hydee

المصدر: Revista de Saúde Pública. January 2021 55

مصطلحات موضوعية: Child Development, Language Development, Cognition, Motor Activity, Zika Virus

الوصف: We report cognitive, language and motor neurodevelopment, assessed by the Bayley-III test, in 31 non-microcephalic children at age 3 with PCR-confirmed maternal Zika virus exposure (Rio de Janeiro, 2015–2016). Most children had average neurodevelopmental scores, however, 8 children (26%) presented delay in some domain. Language was the most affected: 7 children (22.6%) had a delay in this domain (2 presenting severe delay). Moderate delay was detected in the cognitive (3.2%) and motor (10%) domains. Maternal illness in the third trimester of pregnancy and later gestational age at birth were associated with higher Bayley-III scores. Zika-exposed children require long-term follow-up until school age.

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الوصول الحر: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102021000100701Test

المؤلفون: Martinez, Viviana Fajardo, Zhang, Dajie, Paiola, Sophia, Mok, Thalia, Cambou, Mary C, Kerin, Tara, Rao, Rashmi, Brasil, Patricia, Ferreira, Fatima, Fuller, Trevon, Bhattacharya, Debika, Foo, Suan-Sin, Chen, Weiqiang, Jung, Jae, Einspieler, Christa, Marschik, Peter B, Nielsen-Saines, Karin

المصدر: BMJ Open. 13(1)

مصطلحات موضوعية: Paediatrics, Reproductive Medicine, Biomedical and Clinical Sciences, Prevention, Clinical Research, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Infectious Diseases, Brain Disorders, Reproductive health and childbirth, Good Health and Well Being, Infant, Newborn, Infant, Humans, Pregnancy, Female, Cohort Studies, Longitudinal Studies, Brazil, COVID-19, Pregnancy Complications, Infectious, Paediatric neurology, Paediatric infectious disease & immunisation, Maternal medicine, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences, Biomedical and clinical sciences, Health sciences, Psychology

الوصف: ObjectiveTo evaluate neuromotor repertoires and developmental milestones in infants exposed to antenatal COVID-19.DesignLongitudinal cohort study.SettingHospital-based study in Los Angeles, USA and Rio de Janeiro, Brazil between March 2020 and December 2021.ParticipantsInfants born to mothers with COVID-19 during pregnancy and prepandemic control infants from the Graz University Database.InterventionsGeneral movement assessment (GMA) videos between 3 and 5 months post-term age were collected and clinical assessments/developmental milestones evaluated at 6-8 months of age. Cases were matched by gestational age, gender and post-term age to prepandemic neurotypical unexposed controls from the database.Main outcome measuresMotor Optimality Scores Revised (MOS-R) at 3-5 months. Presence of developmental delay (DD) at 6-8 months.Results239 infants were enrolled; 124 cases (83 in the USA/41 in Brazil) and 115 controls. GMA was assessed in 115 cases and 115 controls; 25% were preterm. Median MOS-R in cases was 23 (IQR 21-24, range 9-28) vs 25 (IQR 24-26, range 20-28) in controls, p

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الوصول الحر: https://escholarship.org/uc/item/5775k2dvTest

المؤلفون: Kulvicius, Tomas, Zhang, Dajie, Nielsen-Saines, Karin, Bölte, Sven, Kraft, Marc, Einspieler, Christa, Poustka, Luise, Wörgötter, Florentin, Marschik, Peter B

مصطلحات موضوعية: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning

الوصف: Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we proposed an innovative non-intrusive approach using a pressure sensing device to classify infant general movements (GMs). Here, we tested the feasibility of using pressure data to differentiate typical GM patterns of the ''fidgety period'' (i.e., fidgety movements) vs. the ''pre-fidgety period'' (i.e., writhing movements). Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a 32x32-grid pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4-16 weeks of post-term age. For proof-of-concept, 1776 pressure data snippets, each 5s 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 (FM+) or absent (FM-). Multiple neural network architectures were tested to distinguish the FM+ vs. FM- classes, including support vector machines (SVM), feed-forward networks (FFNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks. The CNN achieved the highest average classification accuracy (81.4%) for classes FM+ vs. FM-. Comparing the pros and cons of other methods aiming at automated GMA to the pressure sensing approach, we concluded 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.

الوصول الحر: http://arxiv.org/abs/2208.00884Test

المؤلفون: Marschik, Peter B, Kulvicius, Tomas, Flügge, Sarah, Widmann, Claudius, Nielsen-Saines, Karin, Schulte-Rüther, Martin, Hüning, Britta, Bölte, Sven, Poustka, Luise, Sigafoos, Jeff, Wörgötter, Florentin, Einspieler, Christa, Zhang, Dajie

المصدر: iScience, 2023

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: Video recording is a widely used method for documenting infant and child behaviours in research and clinical practice. Video data has rarely been shared due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing. This demand is even more imperative when data-driven computer-based approaches are involved, such as screening tools to complement clinical assessments. 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 the Prechtl's general movements assessment (GMA), an established and globally practised video-based diagnostic tool in early infancy for detecting neurological deficits, such as cerebral palsy. To date, no shared expert-annotated large data repositories for infant movement analyses exist. Such datasets would massively benefit training and recalibration of human assessors and the development of computer-based approaches. In the current study, sequences from a prospective longitudinal infant cohort with a total of 19451 available general movements video snippets were randomly selected for human clinical reasoning and computer-based analysis. We demonstrated for the first time that pseudonymisation by face-blurring video recordings is a viable approach. The video redaction did not affect classification accuracy for either human assessors or computer vision methods, suggesting an adequate and easy-to-apply solution for sharing movement video data. We call for further explorations into efficient and privacy rule-conforming approaches for deidentifying video data in scientific and clinical fields beyond movement assessments. These approaches shall enable sharing and merging stand-alone video datasets into large data pools to advance science and public health.

الوصول الحر: http://arxiv.org/abs/2207.11020Test