Publications by authors named "Valeria Ottaviani"

Article Synopsis
  • Early diagnosis of neurodevelopmental impairments in preterm infants can be time-consuming and qualitative, relying on expert visual analysis of movement.
  • A new deep-learning algorithm, TwinEDA, is proposed to automate limb-pose estimation using depth images, combining high performance with low computational demands.
  • Tested on a dataset of 27,000 depth video frames, TwinEDA demonstrated superior speed and efficiency compared to existing methods, marking a key advancement in automatic monitoring for preterm infants.
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Objective: Monitoring infants' breathing activity is crucial in research and clinical applications but remains a challenge. This study aims to develop a contactless method to monitor breathing patterns and thoracoabdominal asynchronies in infants inside the incubator, using depth cameras.

Methods: We proposed an algorithm to extract the 3D displacements of the ribcage and abdomen from the analysis of depth images.

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Background: When learning and improving singing, the quantitative characterization of artists' performances based only on vocal parameters does not provide enough information to identify strategies for improvement. Simultaneous monitoring of sound production and breathing patterns in professional singers can allow the exploration of the mechanisms that promote effective singing modalities through association with respiratory efforts.

Methods: We developed and tested a novel portable device that simultaneously monitors vocal activity and breathing patterns without interfering with natural singing.

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Background: Current criteria for surfactant administration assume that hypoxia is a direct marker of lung-volume de-recruitment. We first introduced an early, non-invasive assessment of lung mechanics by the Forced Oscillation Technique (FOT) and evaluated its role in predicting the need for surfactant therapy.

Objectives: To evaluate whether lung reactance (Xrs) assessment by FOT within 2 h of birth identifies infants who would need surfactant within 24 h; to eventually determine Xrs performance and a cut-off value for early detection of infants requiring surfactant.

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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). 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.

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Objective: Despite technical specifications of neonatal mechanical ventilators (MVs) guarantee clinically irrelevant discrepancies between the set and the delivered values of ventilation parameters, previous studies reported large deviations. Most studies characterized performances of a given model/brand by studying a single device, disregarding possible intramodel differences, and leaving the accuracy of the ventilation parameters effectively delivered in clinical settings unknown. The aim of this study was to evaluate the real-life accuracy of pressure and volume parameters delivered by neonatal ventilators ready to be used on patients in neonatal intensive care units (NICUs).

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