Sonography data science

Fetal sonography remains a highly specialised skill in spite of its necessity and importance. Because of differences in fetal and maternal anatomy, and human pyschomotor skills, there is an intra- and inter-sonographer variability amoungst expert sonographers. By understanding their similarities and differences, we want to build more interpretive models to assist a sonographer who is less experienced in scanning. This thesis’s contributions to the field of fetal sonography can be grouped into two themes. First I have used data visualisation and machine learning methods to show that a sonographer’s search strategy is anatomical (plane) dependent. Second, I show that a sonographer’s style and human skill of scanning is not easily disentangled. We first examine task-specific spatio-temporal gaze behaviour through the use of data visualisation, where a task is defined as a specific anatomical plane the sonographer is searching for. The qualitative analysis is performed at both a population and individual level, where we show that the task being performed determines the sonographer’s gaze behaviour. In our population-level analysis, we use unsupervised methods to identify meaningful gaze patterns and visualise task-level differences. In our individual-level analysis, we use a deep learning model to provide context to the eye-tracking data with respect to the ultrasound image. We then use an event-based visualisation to understand differences between gaze patterns of sonographers performing the same task. In some instances, sonographers adopt a different search strategy which is seen in the misclassified instances of an eye-tracking task classification model. Our task classification model supports the qualitative behaviour seen in our population-level analysis, where task-specific gaze behaviour is quantitatively distinct. We also investigate the use of time-based skill definitions and their appropriateness in fetal ultrasound sonography; a time-based skill definition uses years of clinical experience as an indicator of skill. The developed task-agnostic skill classification model differentiates gaze behaviour between sonographers in training and fully qualified sonographers. The preliminary results also show that fetal sonography scanning remains an operator-dependent skill, where the notion of human skill and individual scanning stylistic differences cannot be easily disentangled. Our work demonstrates how and where sonographers look at whilst scanning, which can be used as a stepping stone for building style-agnostic skill models