Two new US patents widen Ultrasound AI’s reach from pregnancy imaging to drug detection

Artificial intelligence companies working in medical imaging often measure their progress not only in algorithms and clinical trials but also in patents, the legal instruments that determine who may commercialize a given method and for how long. This month, Ultrasound AI, Inc., a Colorado-based developer of image-only artificial intelligence focused on maternal-fetal health, added two new United States patents to its portfolio, bringing the company’s total of granted US patents to six. The issuances, dated September 1 and September 15, 2026, cover two distinct but related territories: the use of neural networks to estimate pharmaceutical exposure from medical images, and the use of imaging data to support diagnosis, prognosis, and treatment optimization across a broad range of medical conditions.

The first of the new grants, U.S. Patent No. 12,721,589 B2, is titled Artificial Intelligence System for Determining Drug Use Through Medical Imaging. According to the patent, the invention describes methods for training neural networks to estimate pharmaceutical exposure from medical images. Two technical elements stand out in the description. The first is the selection of relevant imaging information, a step that addresses the fact that a raw medical image contains far more data than any model needs, much of it noise with respect to the question being asked. The second is the filtering of images based on quality or usefulness to the analysis, a preprocessing discipline that has become central to applied machine learning in medicine, where poor-quality acquisitions can degrade model performance in ways that are invisible to the end user.

The idea that a standard medical image could carry evidence of drug exposure is less far-fetched than it might first appear. Pharmaceutical compounds and their physiological effects can leave traces in tissue morphology, organ size, fluid distributions, and other measurable features that clinicians already assess visually. A neural network trained on appropriately labeled imaging data can, in principle, learn to detect such patterns at a scale and consistency beyond human perception. The patent does not claim that such a system is deployed or validated for clinical use; it protects the methods by which such a system could be constructed, from the curation of training images to the estimation of exposure itself. That distinction between an issued patent and an approved product is one the company itself emphasizes, noting that issued patents cover inventions and methods and do not reflect FDA-authorized indications for any Ultrasound AI product.

The second grant, U.S. Patent No. 12,733,901, issued on September 15, is considerably broader in scope. Titled Artificial Intelligence System for Comprehensive Medical Diagnosis, Prognosis, and Treatment Optimization Through Medical Imaging, it describes approaches to analyzing medical images and the underlying imaging data to generate information relevant to medical assessment, prognosis, and treatment planning. Where the first patent protects a specific estimation task, the second reaches toward the larger ambition of the imaging AI field: turning the pixel-level content of routine scans into decision support that spans the clinical pathway, from what a condition is, to how it is likely to progress, to which intervention might serve a particular patient best.

For Ultrasound AI, the new patents extend a research program that began, and remains anchored, in pregnancy imaging. The company’s existing US patents concern premature-birth prediction and the determination of clinical values through medical imaging, and its flagship technology, Delivery Date AI, was granted marketing authorization by the US Food and Drug Administration through the De Novo pathway earlier this year under grant number DEN250007. That authorization made Delivery Date AI the first technology of its kind to determine a Predicted Delivery Date solely from standard ultrasound images, without requiring biomarkers, blood tests, or patient history beyond what the scan itself contains. The De Novo pathway is the regulatory route the FDA uses for novel devices of low to moderate risk for which there is no preexisting predicate, and it typically requires substantial evidence of analytical and clinical validity before clearance is granted.

The significance of an image-only approach is worth unpacking, because it explains much of the company’s intellectual property strategy. Conventional obstetric dating relies on a combination of ultrasound measurements and clinical information such as the date of the last menstrual period, and estimates of delivery timing draw on risk models that incorporate maternal demographics, prior pregnancy outcomes, and laboratory results. A system that predicts delivery timing from the ultrasound image alone is making a stronger and more testable claim: that the developing pregnancy imprints enough information on the image itself to support the prediction. If that claim holds across diverse populations and equipment, it could make sophisticated obstetric risk assessment available anywhere standard ultrasound is performed, including settings where laboratory infrastructure is limited.

Patents are the mechanism by which such claims are protected commercially, and the company’s portfolio now spans multiple jurisdictions. In addition to its six US grants, Ultrasound AI holds patents in Israel, Singapore, Japan, and South Korea, with additional applications pending in the United States and internationally. Geographic breadth matters in medical imaging because ultrasound equipment is manufactured and sold globally, and any partner integrating AI into an ultrasound workflow will encounter the patent regimes of every market in which the device is sold. The company maintains a public listing of its portfolio at ultrasound.ai/patents.

The strategic logic of the two new grants is visible in the way they bracket the company’s core business. The drug-exposure patent pushes outward from obstetrics into pharmacology and toxicology, domains where imaging-based detection could complement or, in some contexts, substitute for laboratory and self-reported measures. The comprehensive diagnosis, prognosis, and treatment patent pushes upward in generality, covering the analytical pipeline by which imaging data becomes clinical guidance rather than any single clinical question. Together with the existing premature-birth and clinical-value patents, they form a layered fence around the central technical proposition: that neural networks can extract clinically relevant information from medical images that clinicians do not currently extract, and that the extraction can be made reliable enough to matter.

Robert Bunn, founder and chief executive of Ultrasound AI, framed the issuances in those terms. “Our goal is to turn the information in medical images into insights that help clinicians make more informed decisions,” Bunn said. “These patents reflect the breadth of our research and strengthen the intellectual property foundation for our work. Our focus remains bringing carefully developed AI into maternal-fetal care, where better information can make a meaningful difference for families.” The statement underscores a point that industry observers frequently make about imaging AI: the bottleneck is rarely the architecture of the neural network, which is often drawn from a shared pool of published techniques, but rather the curated clinical data, the validation evidence, and the regulatory clearances that separate a research prototype from a product a hospital will trust.

That gap between patent and product is where the next phase of the company’s work will be judged. Delivery Date AI is now available to practices, hospitals, imaging centers, and ultrasound equipment partners across the United States, and the company says it is exploring additional obstetric applications through ongoing research. Whether the methods protected by the new patents will follow the same path, from issuance through validation to regulatory review, remains to be seen, and the company’s own disclaimer makes clear that a patent is a claim to an invention, not evidence of clinical performance. What the September issuances do establish is that a company built on a single striking result, the prediction of delivery timing from an ordinary ultrasound image, intends to compete across a much wider field, and has secured the legal ground to do so.

Subject of Research: US patent issuances for artificial intelligence methods that extract drug-exposure and diagnostic information from medical ultrasound images

Article Title: Ultrasound AI expands intellectual property portfolio with two new US patents

Article References: Ultrasound AI expands intellectual property portfolio with two new US patents. (n.d.). Original publication

Image Credits: AI Generated

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Keywords: Ultrasound AI, artificial intelligence, medical imaging, patents, maternal-fetal health, drug exposure detection, neural networks, Delivery Date AI, FDA De Novo, premature birth prediction, diagnostic imaging, intellectual property