OC10.11: The data science of obstetric ultrasound: automatic analysis of full‐length anomaly scans using machine learning algorithms

Figure 1


Objectives: The clinical workflow of the second trimester anomaly scan is not well studied and holds potential for efficiency improvement. We aimed to create a model for automatic anatomical description of full‐length fetal anomaly scan videos using artificial intelligence. Methods: We prospectively recorded routine full‐length second trimester anomaly scans, extracted short clips of important scan events by detecting video freeze, and image/clip save. For machine learning, we created a training dataset by manually labelling 12% of the scan events to one of 23 principal anatomical structures, trained a deep spatiotemporal neural network with the training dataset, cross‐validated and applied the model to automatically label the rest of the scans. Finally, we retrospectively labelled a test dataset (48 scans) to compare with the automatically labelled scans. We report the model precision and workflow metrics. Results: 518 scans performed by 14 operators were analysed. The mean scan duration was 26.7 ± 15 minutes, and the mean number of scan events was 23.5 ± 14.4. The manual vs. automatic clips labelling agreement was 74.5%, ranging from 34% for placenta to 89% for heart. The brain, heart and spine were most often the first structure to be evaluated, in 18.8%, 17.6% and 17% of the scans, respectively. On average, 15% of the scan duration was dedicated to cardiac scanning, 10% to brain, and 7% to the spine (figure 1). Conclusions: Using big data, we present a model that describes how expert sonographers perform anomaly scans in a data science fashion. Understanding how operators scan and being able to measure the different operator elements will inform a better understanding of how to train operators, monitor learning progress, and enhance scanning workflow.

Ultrasound in Obstetrics & Gynecology


author = {Drukker, L. and Sharma, H. and Droste, R. and Noble, J.A. and Papageorghiou, A.T.},
title = {OC10.11: The data science of obstetric ultrasound: automatic analysis of full-length anomaly scans using machine learning algorithms},
journal = {Ultrasound in Obstetrics \& Gynecology},
volume = {56},
number = {S1},
pages = {31-31},
doi = {10.1002/uog.22275},
url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1002/uog.22275},
eprint = {https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1002/uog.22275},
year = {2020}