Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

Ihlen, Espen Alexander F.; Støen, Ragnhild; Boswell, Lynn; Regnier, Raye-Ann de; Fjørtoft, Toril; Gaebler‐Spira, Deborah; Labori, Cathrine; Loennecken, Marianne; Msall, Michael E.; Møinichen, Unn Inger; Peyton, Colleen; Schreiber, Michael D.; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Vågen, Randi Tynes; Øberg, Gunn Kristin; Adde, Lars

doi:10.3390/jcm9010005

Cited by 69 publications

(105 citation statements)

References 34 publications

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“…In the cases considered so far, the prevalence of fidgety movements in newborns from the age of two months has been considered more frequently, especially in the context of early detection of the risk of cerebral palsy [ 35 ]. An approach similar to that presented in this study was used by Ihnen et al [ 27 ], who analyzed cerebral palsy prediction in a large group of recordings of children over nine weeks of age. Their training set was built based on the classification of the entire recordings, and then each 5 s of the recording were classified.…”

Section: Discussionmentioning

confidence: 99%

“…In individual studies, authors have attempted to identify irregularities, mostly by reducing the problem to the classification of the recording into the normal and abnormal groups. The proposed solutions can be divided according to the method of the extraction of features describing the child’s movement into those based on features extracted directly from the recording (optical flow, background subtraction) [ 25 , 26 ] and pose-based features, in which the extraction of features is preceded by the process of locating individual body segments [ 27 , 28 ]. Currently, the extraction capabilities of pose-based features have improved due to the availability of ready-made human pose estimation libraries such as OpenPose [ 29 ], whose accuracy in the case of infant movement analysis has been confirmed in various studies [ 28 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

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Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification

Doroniewicz

Ledwoń

Affanasowicz

et al. 2020

Sensors

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Observation of neuromotor development at an early stage of an infant’s life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the foundation for contemporary attempts at objectification and computer-aided diagnosis based on video recordings’ analysis. The present study attempts to automatically detect writhing movements, one of the normal general movement categories presented by newborns in the first weeks of life. A set of 31 recordings of newborns on the second and third day of life was divided by five experts into videos containing writhing movements (with occurrence time) and poor repertoire, characterized by a lower quality of movement in relation to the norm. Novel, objective pose-based features describing the scope, nature, and location of each limb’s movement are proposed. Three machine learning algorithms are evaluated in writhing movements’ detection in leave-one-out cross-validation for different feature extraction time windows and overlapping time. The experimental results make it possible to indicate the optimal parameters for which 80% accuracy was achieved. Based on automatically detected writhing movement percent in the video, infant movements are classified as writhing movements or poor repertoire with an area under the ROC (receiver operating characteristics) curve of 0.83.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification

Doroniewicz

Ledwoń

Affanasowicz

et al. 2020

Sensors

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“…Moreover, Random Forest and AdaBoost seem to be a good choice of classifier, but the method lacks kinematic features that could be introduced by using depth cameras. A new model called Computer-based Infant Movement Assessment (CIMA) was introduced and evaluated on even more infants (377 high-risk infants) by Ihlen et al [ 18 ]. The 1–5 min video recording of 9–15 weeks corrected age infants were used to predict CP.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

“…As a result of the nominal use of GMA in neonatal follow-up programs, several studies have tried to automate this method. These studies are based on either indirect sensing using visual sensors (2D or 3D video) [ 7 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 24 ], direct sensing using motion sensors [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ], or both [ 32 , 33 , 34 ]. They have shown excellent results, however, they lack full automation and also have several fundamental limitations.…”

Section: Introductionmentioning

confidence: 99%

“…They have shown excellent results, however, they lack full automation and also have several fundamental limitations. First, all the studies are either based on a small number of subjects or a fewer number of data samples with respect to CP [ 7 , 18 , 19 , 20 , 25 , 26 , 27 , 32 , 34 ]. It is also not clear if the prediction model in these studies has external validity for high-risk infants.…”

Section: Introductionmentioning

confidence: 99%

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AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

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General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl’s assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

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Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

et al. 2022

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Key Points Question What is the external validity of a deep learning–based method to predict cerebral palsy (CP) based on infants’ spontaneous movements at 9 to 18 weeks’ corrected age? Findings In this prognostic study of 557 infants with a high risk of perinatal brain injury, a deep learning–based method for early prediction of CP had sensitivity of 71%, specificity of 94%, positive predictive value of 68%, and negative predictive value of 95%. Prognosis of CP based on the deep learning–based method was associated with later functional level and CP subtype in children with CP. Meaning This study’s findings suggest that deep learning–based assessments could support early detection of CP in infants at high risk.

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Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

Cited by 69 publications

References 34 publications

Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification

Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

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