{'fraction_non_alphanumeric': 0.024175824175824177, 'fraction_numerical': 0.009523809523809525, 'mean_word_length': 5.0176211453744495, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
OP26.10: Effect of deviation from the mid‐sagittal plane on the measurement of fetal nuchal translucency (NT) using Volume NTTM program

in each image plane. Planes with scores higher than an empirically determined threshold value were selected as standard planes by the AM. Two evaluation criteria were used to evaluate the performance of the AM: Positive Predictive Value = True Positive Planes/(True Positive Planes + False Positive Planes), and Sensitivity = True Positive Planes/(True Positive Planes + False Negative Planes). Results: The automated method showed accurate prediction of the manually labelled planes with an average sensitivity of 91.29% and a positive predictive value of 76.29%. Through closer inspection, all false positive planes selected by the automated method contained both the SB and UV in the correct position but had not been selected by the expert because of stricter criteria imposed. Conclusions: Our results show that machine learning can be used to automatically select a correct AC plane from a fetal 3D volume, and is an early first step in the direction of automated fetalsonography. Such an automated method has the potential for reducing the time needed to complete an ultrasound examination or to perform offline examinations. Further work will be needed to evaluate this and to extend it to other areas of fetal biometry.