Sonio Suspect is now FDA 510(K) cleared. It is designed to enhance prenatal screening with AI-driven anomaly detection and real-time quality control, supporting early and accurate diagnosis. By identifying anomalies across key fetal regions, it helps clinicians streamline workflows and improve patient outcomes with greater confidence.
Sonio, a leader in prenatal care innovation, announces that the U.S. Food and Drug Administration (FDA) has granted clearance for its latest AI module, Sonio Suspect. This groundbreaking technology transforms prenatal diagnostics by significantly enhancing the detection of fetal anomalies, delivering a 22-point1 improvement in reader performance and enabling early, more accurate identification and characterization of abnormalities.
““The clearance of Sonio Suspect is a major milestone for Sonio, at the core of our mission to improve patient outcomes in maternal fetal medicine. By combining real-time AI quality control with AI-driven anomaly detection, Sonio supports ultrasound providers at every step of the patient pathway, from exhaustive documentation to accurate diagnosis. Our technology is designed to help healthcare providers detect issues early and streamline processes, ultimately improving the care every patient receives.”
Research shows that up to 51%* of fetal anomalies are missed during standard prenatal ultrasound screenings, with 31%** of these missed cases resulting from misinterpretation of high-quality images. Sonio Suspect tackles this challenge by providing automatic detection of eight abnormal findings across seven ultrasound views of three key fetal anatomical regions: the heart, brain, and abdomen.
Sonio Suspect enables early identification of subtle congenital malformations from as early as 11 weeks. By providing a broader diagnostic window, it supports clinicians in identifying abnormalities sooner, giving families and healthcare providers more time to act, plan interventions, and improve both maternal and fetal outcomes.
A multicenter reader performance study involving 47 sites, including 37 in the USA, demonstrated a statistically significant 22-point improvement in anomaly detection (from 69% to 91% AUC, p<0.001). This improvement was confirmed across diverse patient demographics, including BMI, and was consistent regardless of clinician background or experience — whether Maternal-Fetal Medicine specialists, Obstetricians and Gynecologists, or Radiologists.
Early and accurate identification of congenital malformations is critical to reducing both fetal and maternal mortality. 80%** of missed cases result from poor-quality images (49%**) or misinterpretation (31%), this is why cutting-edge technology must address both quality control and diagnostic accuracy. Sonio Suspect will be seamlessly integrated into Sonio’s comprehensive reporting and workflow solution, which already includes Sonio Detect, a quality assurance algorithm that ensures high-quality image acquisition.By combining real-time quality control with AI-driven anomaly detection, Sonio bridges the gap between technology and clinical application, empowering providers with consistent, efficient, and accurate prenatal screening across diverse healthcare settings.
*[Weedn2014]: Weedn et al. (2014), Maternal reporting of prenatal ultrasounds among women in the National Birth Defects Prevention Study. Birth Defects Research Part A: Clinical and Molecular Teratology, 100: 4-12.
**van Nisselrooij AEL, Teunissen AKK, Clur SA, et al. Why are congenital heart defects being missed?. Ultrasound Obstet Gynecol. 2020;55(6):747-757. doi:10.1002/uog.20358
1.Early refers to detection starting from 11 weeks (T1)
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*[Weedn2014]: Weedn et al. (2014), Maternal reporting of prenatal ultrasounds among women in the National Birth Defects Prevention Study. Birth Defects Research Part A: Clinical and Molecular Teratology, 100: 4-12.
**van Nisselrooij AEL, Teunissen AKK, Clur SA, et al. Why are congenital heart defects being missed?. Ultrasound Obstet Gynecol. 2020;55(6):747-757. doi:10.1002/uog.20358
***AI-powered quality assurance capabilities are part of FDA 510(k) cleared Sonio Detect.
****AI-driven abnormality detection capabilities are part of FDA 510(k) cleared Sonio Suspect (K243614). Sonio Suspect is a concurrent reading aid and does not replace the comprehensive anatomic survey or clinical judgment of the interpreting physician. Not commercialized yet.
*****https://newbornscreening.hrsa.gov/conditions/congenital-cytomegalovirus
Picture: AI-detected ultrasound signs of congenital cytomegalovirus (CMV) infection: subependymal cyst and periventricular echogenicity (not cleared, for research only)
