The future of AI in ob/gyn ultrasound
Data and a lot of it is the fundamental requirement for creating a successful deep learning application. One of the practical limitations of software developers is ethically and efficiently obtaining de-identified patient data to create such a thing. One of the biggest players on the AI block is the UK-based company Intelligent Ultrasound, which acquired over 1 million high-quality images from real obstetric scans to develop algorithms for the software ScanNav. The goals of ScanNav are to provide real-time guidance to sonographers by automatically capturing the six correct images as recommended by the UK fetal anomaly-screening program and provide an audit showing that all the images were obtained. In a sense, this provides a layer of quality improvement to ensure that optimal patient care is being delivered.
The software is still in development and some limitations include real-time guidance for probe placement, especially with unique patient considerations such as obesity. A crucial aspect to consider in these situations is patient privacy. While individual data may be de-identified, further advances in machine learning may be able to identify individuals if appropriate safety measures aren’t taken with data security.15
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At the end of 2018, SonoScape medical (Shenzhen, China) announced development of their S-fetus algorithm, designed for the S60 ultrasound system, which will scan the entire fetus with a single cine loop. Thousands of real images were used to develop algorithms to identify appropriate landmarks and accurate measurements. In addition, in true deep learning fashion, the system continues to fine-tune its analysis with each additional exam it performs. The S-fetus software will select the best images and automatically measure key growth components. This software will consolidate the multistep process of obtaining fetal biometry to a single push of a button. In addition to saving an immense amount of time and keystrokes for each patient, it will alert the sonographer if manual adjustments or measurements do not meet image standards, thus providing the sonographer feedback and resulting in better images.
Fetal ultrasonography is a mainstay of routine prenatal care. Significant advancements have been made over the years to improve image quality and diagnostic accuracy while maintaining the ease, reproducibility, and efficiency for sonographers performing and physicians interpreting the images. One of AI’s greatest benefits is removing its dependency on the operator and standardizing our approach to improve patient safety, especially in low-resources settings where expertise may otherwise be lacking. Keep your eyes and ears open as the data and hype about this technology are only going to skyrocket in our field. The preliminary schedule for ISUOG in Berlin in October of this year includes courses on how large data and AI may impact our field, and the sessions are sure to be well attended. AI and deep learning certainly warrants all the buzz and energy surrounding it, but realistically, is not yet sophisticated enough to replace obstetricians, maternal-fetal medicine specialists or radiologists. Rest assured, we don’t have to worry about our job security quite yet.
The authors report no potential conflicts of interest with regard to this article.
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