Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)

Here, we share a de-identify subsample of the data used in the research article, that will allow interested scientists to test the shared code, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set). With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.