AI can make maternal ultrasonography more accessible, accurate and efficient

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Ultrasound is one of the most important advances in medicine. With the capability of seeing the fetus inside the uterus, physicians can now more accurately estimate the gestational age, growth and weight of the fetus, diagnose countless fetal abnormalities and other advances in obstetrics.

None of these abilities people now take for granted would have been possible but for ultrasound. This has dramatically improved outcomes for pregnancies.

Now, artificial intelligence emerges as another leap forward for maternal care – akin in significance to the advent of ultrasound, said Dr. Yinka Oyelese, director of obstetric imaging at Beth Israel Deaconess Medical Center.

And the combination of these two innovations holds boundless potential, promising to revolutionize the landscape of pregnancy care, he says.

We interviewed Oyelese to discuss the coming together of ultrasound and AI, and to learn more about his recent appointment to the scientific advisory board of Sonio, a femtech company using AI in ultrasound to better the maternal healthcare space.

Q. How did ultrasound and artificial intelligence technologies come together? How are they a good fit?

A. Artificial intelligence and ultrasound have combined to transform medical imaging, especially in maternal-fetal medicine (MFM). AI improves ultrasound technology by providing sophisticated picture analysis, work automation and tools for decision assistance. Given the complementary qualities of AI and ultrasonography, their integration is a natural and logical progression.

Real-time fetal imaging is made possible by ultrasound, which offers important information on the health and development of the growing fetus. However, deciphering ultrasound images can be difficult and time-consuming; it calls for training and experience.

Here’s where AI shines. AI systems may quickly analyze large volumes of ultrasonography data, which helps medical professionals identify anomalies, measure fetal biometrics and provide precise diagnoses. Ultrasound becomes more effective, accurate and accessible by using AI, eventually improving maternal care patient outcomes.

Q. What are the ways in which AI helps improve maternal ultrasound?

A. As an expert in the field of MFM, I see many ways in which AI will transform maternal ultrasonography. First, AI has become a routine part of what we do in our daily lives, greatly improving efficiency, reliability, accuracy and quality control in such activities as banking, immigration, radiology and aviation.

The ability to process tremendous amounts of data and for machines to learn over time is something we now often expect, and sometimes take for granted. For instance, my access to my phone, banking and several other daily activities is based on facial recognition.

As time has evolved, my phone has learned to recognize my face even when I wear a mask, put on glasses or grow a beard. The same exciting potential exists for AI in ultrasound. AI’s rapid automated analysis and image recognition capabilities dramatically speeds up and simplifies accurate image acquisition in ultrasound.

Currently, performing a detailed scan is time-consuming and cumbersome, and is the rate-limiting step of ultrasound. The ability to accurately acquire the optimal image, even from a sweep of fetal structures is a tremendous advance that can only make ultrasound more efficient and accurate.

AI’s ability to then correctly identify and interpret the structures will make the job of all sonographers, sonologists and physicians so much easier. AI-powered ultrasound’s correct and reproducible identification of fetal structures allows the sonographer to evaluate their scans against the AI’s results, permitting sonographers to improve the quality of their examinations and improve their proficiency.

Thus, AI provides a powerful tool for improving the scanning abilities of sonographers.

Furthermore, AI is a powerful tool for assessment of quality and will lead to improvement of all ultrasound units. Its ability to spot tiny patterns that may be missed by sonographers and physicians enhances early intervention and increases diagnostic accuracy, all of which benefit patients.

Predictive models developed by AI help with risk assessment, enabling medical professionals to handle high-risk pregnancies proactively. Additionally, AI-powered decision support systems provide insightful guidance to physicians in the provision of individualized patient care.

Perhaps one of the most exciting applications of this technology will be in telemedicine, where the remote sonographer can assess and complete their scans with feedback from AI, rather than waiting for the physician interpretation.

This will allow the sonographer to present a complete and accurate scan and report to the physician, and has the potential to democratize ultrasound, making accurate and complete ultrasound examinations available to populations where previously it was not possible due to lack of sufficient personnel.

AI-powered simulations support instruction in education, guaranteeing future MFM professionals are proficient. AI is a powerful tool in quality assessment. The images obtained by sonographers and sonologists can quickly be assessed by AI, leading to improvement in the image acquisition by imagers.

Additionally, AI makes telemedicine possible, enabling remote consultations to provide maternal-fetal care in disadvantaged locations. Finally, AI provides tremendous opportunities for research, which will greatly enhance the care pregnant mothers receive.

In summary, AI will make maternal ultrasonography more accessible, accurate, and efficient, which will completely change the landscape of maternal care and will greatly improve outcomes, especially in settings where this was not previously possible.

Q. What made you recently decide to join the scientific advisory board of AI ultrasound vendor Sonio? What do you hope to accomplish?

A. Sonio was founded by some of the global leaders in fetal imaging and AI, who have pioneered several advances in our field. My decision was driven by the unwavering commitment of the team at Sonio to enhancing pregnancy care – an area I am deeply passionate about and have dedicated much of my life to.

Sonio’s mission aligns with my lifelong dedication to using ultrasound technology to improve pregnancy outcomes. The potential of the combination of ultrasound and artificial intelligence technologies is tremendously exciting and holds great promise for advancing medical care to pregnant women.

However, no technology is adequate without a vision to improve the wellbeing of our patients, and every technological advance must be accessible and available to those who need it the most, especially the most vulnerable. Sonio’s focus on making advanced yet user-friendly ultrasound solutions widely accessible, especially in vulnerable communities, strongly appealed to me.

The team at Sonio share my objective of giving global access to high-quality prenatal care. Sonio’s dedication to combining technology and clinical experience resonated strongly with me as an MFM specialist who has dedicated much of his life to ultrasound in pregnancy in ways that lead to improved pregnancy outcomes.

It is clear to me that Sonio’s overarching objective is to radically transform prenatal care by providing hitherto unavailable sophisticated yet simple and easy-to-use ultrasound solutions to healthcare practitioners throughout the world using artificial intelligence. By joining Sonio, I aim to leverage my expertise to ensure their technology achieves the highest standards of clinical accuracy and effectiveness.

Our shared goal is to diminish barriers to prenatal care globally and improve maternal and fetal health outcomes through innovative solutions. This collaboration represents an exciting and fulfilling opportunity to make a significant and transformative impact in the field of maternal-fetal medicine and to expectant mothers.

Follow Bill’s HIT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.



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