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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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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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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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Jin X, Zhu H, Cao W, Zou X, Chen J. Identifying activity level related movement features of children with ASD based on ADOS videos. Sci Rep 2023; 13:3471. [PMID: 36859661 PMCID: PMC9975881 DOI: 10.1038/s41598-023-30628-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/27/2023] [Indexed: 03/03/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects about 2% of children. Due to the shortage of clinicians, there is an urgent demand for a convenient and effective tool based on regular videos to assess the symptom. Computer-aided technologies have become widely used in clinical diagnosis, simplifying the diagnosis process while saving time and standardizing the procedure. In this study, we proposed a computer vision-based motion trajectory detection approach assisted with machine learning techniques, facilitating an objective and effective way to extract participants' movement features (MFs) to identify and evaluate children's activity levels that correspond to clinicians' professional ratings. The designed technique includes two key parts: (1) Extracting MFs of participants' different body key points in various activities segmented from autism diagnostic observation schedule (ADOS) videos, and (2) Identifying the most relevant MFs through established correlations with existing data sets of participants' activity level scores evaluated by clinicians. The research investigated two types of MFs, i.e., pixel distance (PD) and instantaneous pixel velocity (IPV), three participants' body key points, i.e., neck, right wrist, and middle hip, and five activities, including Table-play, Birthday-party, Joint-attention, Balloon-play, and Bubble-play segmented from ADOS videos. Among different combinations, the high correlations with the activity level scores evaluated by the clinicians (greater than 0.6 with p < 0.001) were found in Table-play activity for both the PD-based MFs of all three studied key points and the IPV-based MFs of the right wrist key point. These MFs were identified as the most relevant ones that could be utilized as an auxiliary means for automating the evaluation of activity levels in the ASD assessment.
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Affiliation(s)
- Xuemei Jin
- South China Academy of Advanced Optoelectronics, South China Normal University (SCNU), Guangzhou, 510006, China
| | - Huilin Zhu
- Child Development and Behavior Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China.
| | - Wei Cao
- South China Academy of Advanced Optoelectronics, South China Normal University (SCNU), Guangzhou, 510006, China
| | - Xiaobing Zou
- Child Development and Behavior Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China
| | - Jiajia Chen
- South China Academy of Advanced Optoelectronics, South China Normal University (SCNU), Guangzhou, 510006, China.
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Moro M, Pastore VP, Tacchino C, Durand P, Blanchi I, Moretti P, Odone F, Casadio M. A markerless pipeline to analyze spontaneous movements of preterm infants. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107119. [PMID: 36137327 DOI: 10.1016/j.cmpb.2022.107119] [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: 01/02/2022] [Revised: 08/01/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE The analysis of spontaneous movements of preterm infants is important because anomalous motion patterns can be a sign of neurological disorders caused by lesions in the developing brain. A diagnosis in the first weeks of child's life is crucial to plan timely and appropriate rehabilitative interventions. An accurate visual assessment of infants' spontaneous movements requires highly specialized personnel, not always available, and it is operator dependent. Motion capture systems, markers and wearable sensors are commonly used for human motion analysis, but they can be cumbersome, limiting their use in the study of infants' movements. METHODS In this paper we propose a computer-aided pipeline to characterize and classify infants' motion from 2D video recordings. The final goal is detecting anomalous motion patterns. The implemented pipeline is based on computer vision and machine learning algorithms and includes a specific step to increase the interpretability of the results. Specifically, it can be summarized by the following steps: (i) body keypoints detection: we rely on a deep learning-based semantic features detector to localize the positions of meaningful landmark points on infants' bodies; (ii) parameters extraction: starting from the trajectories of the detected landmark points, we extract quantitative parameters describing infants motion patterns; (iii) classification: we implement different classifiers (Support Vector Machines, Random Forest, fully connected Neural Network, Long Short Term Memory) that, starting from the motion parameters, classify between normal or abnormal motion patterns. RESULTS We tested the proposed pipeline on a dataset, recorded at the 40th gestational week, of 142 infants, 59 with evidence of neuromotor disorders according to a medical assessment carried out a posteriori. Our procedure successfully discriminates normal and anomalous motion patterns with a maximum accuracy of 85.7%. CONCLUSIONS In conclusion, our pipeline has the potential to be adopted as a tool to support the early detection of abnormal motion patterns in preterm infants.
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Affiliation(s)
- Matteo Moro
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, via Dodecaneso 35, Genova 16146, Italy; Machine Learning Genoa (MaLGa) Center, via Dodecaneso 35, Genova 16146, Italy.
| | - Vito Paolo Pastore
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, via Dodecaneso 35, Genova 16146, Italy; Machine Learning Genoa (MaLGa) Center, via Dodecaneso 35, Genova 16146, Italy; Italian Institute of Technology (IIT), via Morego 30, Genova 16163, Italy.
| | - Chaira Tacchino
- Istituto Giannina Gaslini, via Gerolamo Gaslini 5, Genova 16147, Italy.
| | - Paola Durand
- Istituto Giannina Gaslini, via Gerolamo Gaslini 5, Genova 16147, Italy.
| | - Isabella Blanchi
- Istituto Giannina Gaslini, via Gerolamo Gaslini 5, Genova 16147, Italy.
| | - Paolo Moretti
- Istituto Giannina Gaslini, via Gerolamo Gaslini 5, Genova 16147, Italy.
| | - Francesca Odone
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, via Dodecaneso 35, Genova 16146, Italy; Machine Learning Genoa (MaLGa) Center, via Dodecaneso 35, Genova 16146, Italy.
| | - Maura Casadio
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, via Dodecaneso 35, Genova 16146, Italy; Italian Institute of Technology (IIT), via Morego 30, Genova 16163, Italy.
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Doi H, Iijima N, Furui A, Soh Z, Yonei R, Shinohara K, Iriguchi M, Shimatani K, Tsuji T. Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants. Sci Rep 2022; 12:18045. [PMID: 36302797 PMCID: PMC9614013 DOI: 10.1038/s41598-022-21308-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 09/26/2022] [Indexed: 01/24/2023] Open
Abstract
Early intervention is now considered the core treatment strategy for autism spectrum disorders (ASD). Thus, it is of significant clinical importance to establish a screening tool for the early detection of ASD in infants. To achieve this goal, in a longitudinal design, we analyzed spontaneous bodily movements of 4-month-old infants from general population and assessed their ASD-like behaviors at 18 months of age. A total of 26 movement features were calculated from video-recorded bodily movements of infants at 4 months of age. Their risk of ASD was assessed at 18 months of age with the Modified Checklist for Autism in Toddlerhood, a widely used screening questionnaire. Infants at high risk for ASD at 18 months of age exhibited less rhythmic and weaker bodily movement patterns at 4 months of age than low-risk infants. When the observed bodily movement patterns were submitted to a machine learning-based analysis, linear and non-linear classifiers successfully predicted ASD-like behavior at 18 months of age based on the bodily movement patterns at 4 months of age, at the level acceptable for practical use. This study analyzed the relationship between spontaneous bodily movements at 4 months of age and the ASD risk at 18 months of age. Experimental results suggested the utility of the proposed method for the early screening of infants at risk for ASD. We revealed that the signs of ASD risk could be detected as early as 4 months after birth, by focusing on the infant's spontaneous bodily movements.
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Affiliation(s)
- Hirokazu Doi
- grid.411113.70000 0000 9122 4296Department of Science and Engineering, Kokushikan University, Setagaya, Japan
| | - Naoya Iijima
- grid.257022.00000 0000 8711 3200Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan
| | - Akira Furui
- grid.257022.00000 0000 8711 3200Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan
| | - Zu Soh
- grid.257022.00000 0000 8711 3200Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan
| | - Rikuya Yonei
- grid.257022.00000 0000 8711 3200School of Engineering, Hiroshima University, Higashihiroshima, Japan
| | - Kazuyuki Shinohara
- grid.174567.60000 0000 8902 2273Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
| | - Mayuko Iriguchi
- grid.174567.60000 0000 8902 2273Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
| | - Koji Shimatani
- grid.412155.60000 0001 0726 4429Faculty of Health and Welfare, Prefectural University of Hiroshima, Hiroshima, Japan
| | - Toshio Tsuji
- grid.257022.00000 0000 8711 3200Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan
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Changes in the Complexity of Limb Movements during the First Year of Life across Different Tasks. ENTROPY 2022; 24:e24040552. [PMID: 35455215 PMCID: PMC9028366 DOI: 10.3390/e24040552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 01/22/2023]
Abstract
Infants’ limb movements evolve from disorganized to more selectively coordinated during the first year of life as they learn to navigate and interact with an ever-changing environment more efficiently. However, how these coordination patterns change during the first year of life and across different contexts is unknown. Here, we used wearable motion trackers to study the developmental changes in the complexity of limb movements (arms and legs) at 4, 6, 9 and 12 months of age in two different tasks: rhythmic rattle-shaking and free play. We applied Multidimensional Recurrence Quantification Analysis (MdRQA) to capture the nonlinear changes in infants’ limb complexity. We show that the MdRQA parameters (entropy, recurrence rate and mean line) are task-dependent only at 9 and 12 months of age, with higher values in rattle-shaking than free play. Since rattle-shaking elicits more stable and repetitive limb movements than the free exploration of multiple objects, we interpret our data as reflecting an increase in infants’ motor control that allows for stable body positioning and easier execution of limb movements. Infants’ motor system becomes more stable and flexible with age, allowing for flexible adaptation of behaviors to task demands.
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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] [Key Words] [MESH Headings] [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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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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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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Di Giorgio E, Rosa-Salva O, Frasnelli E, Calcagnì A, Lunghi M, Scattoni ML, Simion F, Vallortigara G. Abnormal visual attention to simple social stimuli in 4-month-old infants at high risk for Autism. Sci Rep 2021; 11:15785. [PMID: 34349200 PMCID: PMC8338945 DOI: 10.1038/s41598-021-95418-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/22/2021] [Indexed: 11/23/2022] Open
Abstract
Despite an increasing interest in detecting early signs of Autism Spectrum Disorders (ASD), the pathogenesis of the social impairments characterizing ASD is still largely unknown. Atypical visual attention to social stimuli is a potential early marker of the social and communicative deficits of ASD. Some authors hypothesized that such impairments are present from birth, leading to a decline in the subsequent typical functioning of the learning-mechanisms. Others suggested that these early deficits emerge during the transition from subcortically to cortically mediated mechanisms, happening around 2–3 months of age. The present study aimed to provide additional evidence on the origin of the early visual attention disturbance that seems to characterize infants at high risk (HR) for ASD. Four visual preference tasks were used to investigate social attention in 4-month-old HR, compared to low-risk (LR) infants of the same age. Visual attention differences between HR and LR infants emerged only for stimuli depicting a direct eye-gaze, compared to an adverted eye-gaze. Specifically, HR infants showed a significant visual preference for the direct eye-gaze stimulus compared to LR infants, which may indicate a delayed development of the visual preferences normally observed at birth in typically developing infants. No other differences were found between groups. Results are discussed in the light of the hypotheses on the origins of early social visual attention impairments in infants at risk for ASD.
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Affiliation(s)
- Elisa Di Giorgio
- Dipartimento Di Psicologia Dello Sviluppo E Della Socializzazione, Università Degli Studi Di Padova, Via Venezia 8, 35131, Padova, PD, Italy.
| | - Orsola Rosa-Salva
- CIMeC, Center for Mind/Brain Sciences, University of Trento, Mattarello, Italy
| | | | - Antonio Calcagnì
- Dipartimento Di Psicologia Dello Sviluppo E Della Socializzazione, Università Degli Studi Di Padova, Via Venezia 8, 35131, Padova, PD, Italy
| | - Marco Lunghi
- Dipartimento Di Psicologia Dello Sviluppo E Della Socializzazione, Università Degli Studi Di Padova, Via Venezia 8, 35131, Padova, PD, Italy
| | - Maria Luisa Scattoni
- Research Coordination and Support Service, Istituto Superiore Di Sanità, Rome, Italy
| | - Francesca Simion
- Dipartimento Di Psicologia Dello Sviluppo E Della Socializzazione, Università Degli Studi Di Padova, Via Venezia 8, 35131, Padova, PD, Italy
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The Challenging Heterogeneity of Autism: Editorial for Brain Sciences Special Issue "Advances in Autism Research". Brain Sci 2020; 10:brainsci10120948. [PMID: 33297430 PMCID: PMC7762320 DOI: 10.3390/brainsci10120948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 02/07/2023] Open
Abstract
My personal experience as Guest Editor of the Special Issue (SI) entitled "Advances in Autism Research" began with a nice correspondence with Andrew Meltzoff, from the University of Washington, Seattle (WA, USA), which, in hindsight, I consider as a good omen for the success of this Special Issue: "Dear Antonio… [...].
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Early Motor Development Predicts Clinical Outcomes of Siblings at High-Risk for Autism: Insight from an Innovative Motion-Tracking Technology. Brain Sci 2020; 10:brainsci10060379. [PMID: 32560198 PMCID: PMC7349903 DOI: 10.3390/brainsci10060379] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/12/2020] [Accepted: 06/12/2020] [Indexed: 11/17/2022] Open
Abstract
Atypical motor patterns are potential early markers and predictors of later diagnosis of Autism Spectrum Disorder (ASD). This study aimed to investigate the early motor trajectories of infants at high-risk (HR) of ASD through MOVIDEA, a semi-automatic software developed to analyze 2D and 3D videos and provide objective kinematic features of their movements. MOVIDEA was developed within the Italian Network for early detection of Autism Spectrum Disorder (NIDA Network), which is currently coordinating the most extensive surveillance program for infants at risk for neurodevelopmental disorders (NDDs). MOVIDEA was applied to video recordings of 53 low-risk (LR; siblings of typically developing children) and 50 HR infants’ spontaneous movements collected at 10 days and 6, 12, 18, and 24 weeks. Participants were grouped based on their clinical outcome (18 HR received an NDD diagnosis, 32 HR and 53 LR were typically developing). Results revealed that early developmental trajectories of specific motor parameters were different in HR infants later diagnosed with NDDs from those of infants developing typically. Since MOVIDEA was useful in the association of quantitative measures with specific early motor patterns, it should be applied to the early detection of ASD/NDD markers.
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