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

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.

Towards human-level performance on automatic pose estimation of infant spontaneous movements.

Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1424 videos from a clinical international community.

GeneTools--application for functional annotation and statistical hypothesis testing.

Background: Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information.