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Khodadadzadeh M, Sloan AT, Jones NA, Coyle D, Kelso JAS. Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies. Sci Rep 2024; 14:15580. [PMID: 38971875 PMCID: PMC11227524 DOI: 10.1038/s41598-024-66312-6] [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: 06/20/2023] [Accepted: 07/01/2024] [Indexed: 07/08/2024] Open
Abstract
A recent experiment probed how purposeful action emerges in early life by manipulating infants' functional connection to an object in the environment (i.e., tethering an infant's foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism~world connection.
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Affiliation(s)
- Massoud Khodadadzadeh
- School of Computer Science and Technology, University of Bedfordshire, Luton, LU1 3JU, UK.
- The Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, UK.
- Intelligent Systems Research Centre, Ulster University, Derry, Londonderry, BT48 7JL, UK.
| | - Aliza T Sloan
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, 33431, US
| | - Nancy Aaron Jones
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, 33431, US
| | - Damien Coyle
- The Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, UK
- Intelligent Systems Research Centre, Ulster University, Derry, Londonderry, BT48 7JL, UK
| | - J A Scott Kelso
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, 33431, US
- Intelligent Systems Research Centre, Ulster University, Derry, Londonderry, BT48 7JL, UK
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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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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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Kim Y, Kim H, Choi J, Cho K, Yoo D, Lee Y, Park SJ, Jeong MH, Jeong SH, Park KH, Byun SY, Kim T, Ahn SH, Cho WH, Lee N. Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study. BMC Pediatr 2023; 23:525. [PMID: 37872515 PMCID: PMC10591351 DOI: 10.1186/s12887-023-04350-1] [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: 06/16/2023] [Accepted: 10/06/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Respiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician's ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR). METHODS We utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers. RESULTS A mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P < 0.05). Our proposed model, tested on a dataset from March 4, 2019, to March 4, 2022. The mean AUROC of our proposed model for IMV support prediction performance demonstrated 0.861 (95%CI, 0.853-0.869). It is superior to conventional approaches, such as newborn early warning score systems (NEWS), Random Forest, and eXtreme gradient boosting (XGBoost) with 0.611 (95%CI, 0.600-0.622), 0.837 (95%CI, 0.828-0.845), and 0.0.831 (95%CI, 0.821-0.845), respectively. The highest AUPRC value is shown in the proposed model at 0.327 (95%CI, 0.308-0.347). The proposed model performed more accurate predictions as gestational age decreased. Additionally, the model exhibited the lowest alarm rate while maintaining the same sensitivity level. CONCLUSION Deep learning approaches can help accurately standardize the prediction of invasive mechanical ventilation for neonatal patients and facilitate advanced neonatal care. The results of predictive, recall, and alarm performances of the proposed model outperformed the other models.
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Affiliation(s)
- Younga Kim
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | | | | | | | | | | | - Su Jeong Park
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Mun Hui Jeong
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Seong Hee Jeong
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Kyung Hee Park
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Shin-Yun Byun
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Taehwa Kim
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Pusan National University School of Medicine, and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Sung-Ho Ahn
- Department of Neurology, Division of Biostatistics, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Busan, Korea
| | - Woo Hyun Cho
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Pusan National University School of Medicine, and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Narae Lee
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea.
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Khodadadzadeh M, Sloan AT, Jones NA, Coyle D, Kelso JAS. 2D Capsule Networks Detect Perceived Changes in Infant∼Environment Relationship Reflected in 3D Movement Dynamics. RESEARCH SQUARE 2023:rs.3.rs-3088795. [PMID: 37503229 PMCID: PMC10371075 DOI: 10.21203/rs.3.rs-3088795/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Can infant exploration and causal discovery be detected using Artificial Intelligence (AI)? A recent experiment probed how purposeful action emerges in early life by manipulating infants' functional connection to an object in the environment (i.e., tethering one foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism∼world connection. Pairing theory-driven experimentation with AI tools thus opens a path to developing functionally-relevant assessments of infant behaviour that are likely to be useful in clinical settings.
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Affiliation(s)
- Massoud Khodadadzadeh
- Intelligent Systems Research Centre, Ulster University, Derry/Londonderry, BT48 7JL, United Kingdom
- The Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
| | - Aliza T. Sloan
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Nancy Aaron Jones
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Damien Coyle
- Intelligent Systems Research Centre, Ulster University, Derry/Londonderry, BT48 7JL, United Kingdom
- The Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
| | - J. A. Scott Kelso
- Intelligent Systems Research Centre, Ulster University, Derry/Londonderry, BT48 7JL, United Kingdom
- Human Brain and Behaviour Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, United States
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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: 0] [Impact Index Per Article: 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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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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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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