Automated movement analysis to predict motor impairment in preterm infants: a retrospective study

Raghuram, Kamini; Orlandi, Silvia; Shah, Vibhuti; Chau, Tom; Luther, Maureen; Banihani, Rudaina; Church, Paige

doi:10.1038/s41372-019-0464-0

Cited by 14 publications

(42 citation statements)

References 37 publications

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“…CIMA model yielded comparable results to the GMA having 92.7% sensitivity and 81.6% specificity rate in CP prediction. Raghuram et al [ 15 ] introduced a more general analysis by building a predictive model for motor impairment (MI) rather than just a CP prediction. RGB videos of 152 infants (3–5 months) were analyzed to predict MI, defined as Bayley motor composite score <85 or 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%

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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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Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…17,18,21 Eight studies used multiple variables to generate a classification model. 19,20,[22][23][24][25]28,29 One study used reflecting markers placed on the skin of the infant's body and infrared cameras. 23 One study used accelerometer sensors only placed on hands and feet.…”

Section: Automated Movement Recognition Technologymentioning

confidence: 99%

“…In fact, when interobserver reliability was assessed in practitioners who were not the most experienced, reliability was lower (median kappa 0.55, range 0.41–0.77) 16 . To address these challenges, several groups have attempted to automate the GMA 17–30 . Automated movement recognition technologies have been employed in other clinical contexts from gait analysis as a diagnostic biomarker of Parkinson disease 31 to the assessment of stereotyped movements in autism 32 .…”

mentioning

confidence: 99%

“…16 To address these challenges, several groups have attempted to automate the GMA. [17][18][19][20][21][22][23][24][25][26][27][28][29][30] Automated movement recognition technologies have been employed in other clinical contexts from gait analysis as a diagnostic biomarker of Parkinson disease 31 to the assessment of stereotyped movements in autism. 32 The studies of GMA using an automated approach generally rely on indirect sensing via 2D or 3D video capture (marker-less technique) or direct sensing through reflecting markers placed on the skin and captured by camera systems, or sensors attached to the patient's body, like electromagnetic sensors or accelerometers (marker-based or wearable techniques).…”

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confidence: 99%

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Automated movement recognition to predict motor impairment in high‐risk infants: a systematic review of diagnostic test accuracy and meta‐analysis

Raghuram

Orlandi

Church

et al. 2021

Develop Med Child Neuro

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Aim To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high‐risk infants. Method We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high‐risk infants to predict motor impairment, including cerebral palsy (CP) and non‐CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies‐2. Meta‐analyses were performed using hierarchical summary receiver operating characteristic models. Results Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5 months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68–0.77) and 0.70 (95% CI 0.65–0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74–100) and 91% (95% CI 83–93) respectively. Sensor‐based technologies had higher specificity (0.88, 95% CI 0.80–0.93). Interpretation Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta‐analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

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A 3D pose estimation framework for preterm infants hospitalized in the Neonatal Unit

Soualmi,

Ducottet,

Patural

et al. 2023

Multimed Tools Appl

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Infant pose estimation is crucial in different clinical applications, including preterm automatic general movements assessment. Recent infant pose estimation methods are limited by a lack of real clinical data and are mainly focused on 2D detection. We introduce a stereoscopic system for infants’ 3D pose estimation, based on fine-tuning state-of-the-art 2D human pose estimation networks on a large, real, and manually annotated dataset of infants’ images. Our dataset contains over 88k images, collected from 175 videos from 53 premature infants born <33 weeks of gestational age (GA), acquired within the Neonatology department of the Centre Hospitalier Universitaire de Saint Etienne, France, between 32 and 41 weeks of GA. This framework significantly reduced the pose estimation error compared to existing 2D infant pose estimation networks. It achieved a mean error of 1.72 cm on 18000 stereoscopic images in the 3D pose estimation task. This framework is the first 3D pose estimation tool dedicated to preterm infants hospitalized in the Neonatal Unit that does not depend on any visual markers or infrared cameras.

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Automated movement analysis to predict motor impairment in preterm infants: a retrospective study

Cited by 14 publications

References 37 publications

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

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

Automated movement recognition to predict motor impairment in high‐risk infants: a systematic review of diagnostic test accuracy and meta‐analysis

A 3D pose estimation framework for preterm infants hospitalized in the Neonatal Unit

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