The breadth of the AI4US collection becomes clearer when the individual contributions are grouped by clinical problem rather than by technique. A striking number of the papers converge on the heart. Echocardiography, the ultrasound-based assessment of cardiac structure and function, is among the most widely performed imaging examinations in the world, yet it remains one of the most demanding to interpret. Measurements such as left ventricular ejection fraction, a key indicator of how strongly the heart pumps, depend on tracing the boundaries of the left ventricle across many frames of a moving image sequence. Manual tracing is slow and varies between operators, which is why several studies in the issue tackle this exact task. One adapts the Segment Anything Model, a general-purpose vision foundation model, to echocardiographic sequences through an implicit decoding strategy; another integrates multi-scale information and diverse prompts into a large medical model to estimate ejection fraction directly; a third introduces a diffusion-based segmentation model, reflecting a broader trend in which generative modeling frameworks are repurposed for precise anatomical delineation.

Temporal information is a recurring theme in these cardiac works. A heart is a moving target, and a segmentation that ignores motion is throwing away much of the physics of the examination. The special issue includes a motion-enhanced, shape-aware semi-supervised network for echocardiographic sequence segmentation, as well as a semi-supervised approach to automatic four-dimensional mitral valve segmentation from transesophageal echocardiography. The latter is notable on two fronts. Transesophageal imaging places the probe close to the heart, yielding unusually detailed views, but four-dimensional datasets from this modality are enormous and laborious to annotate. Semi-supervised learning, in which a model learns from a small set of fully labeled examples alongside a much larger pool of unlabeled data, offers a pragmatic path forward, and its application to a structure as geometrically intricate as the mitral valve illustrates how far these methods have matured.

Beyond segmentation, the cardiac papers extend into prediction. One contribution addresses subclinical atrial fibrillation, a rhythm disturbance that often goes undetected because it comes and goes unpredictably, by combining deep learning with strain analysis of echocardiographic images. Strain refers to the deformation of heart muscle during the cardiac cycle, and subtle abnormalities in deformation can betray electrical disease before it declares itself on an electrocardiogram. Another cardiac study tackles view recognition, the task of determining which standard plane the sonographer is currently imaging, using an efficient network built on a state space model and trained with evidential learning, an approach that allows the network to express uncertainty about its own predictions. Standard plane recognition is a deceptively important problem: fetal echocardiography in particular requires the operator to find specific anatomical planes among dozens of possible positions, and automated recognition could serve both as a quality-control aid and as a training tool.

The fetus features prominently elsewhere in the collection as well. A study on operationalizing machine learning models for standard fetal ultrasound plane classification moves beyond model development into the engineering discipline of MLOps, the practice of deploying, monitoring, and maintaining machine learning systems in production. This shift in emphasis is significant. A model that performs well in a laboratory setting may fail silently in a clinic when image quality drifts, when hardware changes, or when patient populations differ from the training data. Operational concerns like these are increasingly recognized as the true frontier of clinical artificial intelligence, and their appearance in a special issue of this kind signals that the field is beginning to treat deployment as seriously as architecture design.

Obstetric imaging also demands extreme computational efficiency, because modern ultrasound systems can acquire volumetric data at high frame rates. One paper in the issue addresses deep learning-based high-precision three-dimensional ultrasound imaging for large organs, while another applies an efficient network with a state space model to fetal echocardiographic standard view recognition. State space models have recently emerged as an alternative to transformers for sequence modeling, offering the ability to capture long-range dependencies with computational cost that scales more favorably with input length. Their adoption in ultrasound research, where video and volume data strain the budgets of attention-based architectures, exemplifies how the field borrows and adapts the newest tools from the wider machine learning community.

Abdominal and vascular imaging form another major cluster. The liver is a frequent target: one study improves the segmentation of hepatic vascular networks in ultrasound volumes using a three-dimensional U-Net with intensity transformation-based data augmentation, a strategy that artificially diversifies training images by altering their brightness and contrast distributions, thereby teaching the network to be robust to the notoriously variable gain settings of ultrasound machines. Two further liver studies move from anatomy to diagnosis. One performs simultaneous grading of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound within an artificial intelligence framework. Quantitative ultrasound extracts numerical features, such as acoustic attenuation and backscatter statistics, from the raw image signal rather than from its visual appearance, and combining these features across modalities allows a single examination to characterize several dimensions of liver disease at once. This matters clinically because fatty liver disease and its inflammatory and fibrotic progression are among the most common chronic conditions worldwide, and current assessment often relies on liver biopsy, an invasive procedure that samples only a tiny fraction of the organ. Another liver paper targets nonalcoholic steatohepatitis specifically, using convolutional neural networks with a dual-branch architecture that fuses global and local image features, an appropriate design for a disease in which both diffuse texture changes and focal patterns carry diagnostic meaning.

Vascular structure segmentation receives dedicated attention through a network that encodes A-line coordinates, the one-dimensional signal traces that underlie ultrasound beam formation, for the purpose of delineating vascular anatomy. Working at the level of A-lines rather than finished images represents a return to the physical roots of ultrasound, and it suggests that some of the field’s future gains may come from integrating signal-level information with image-level learning. In a related application, deep learning-based segmentation of abdominal aortic aneurysms and their intraluminal thrombus in three-dimensional ultrasound images addresses a condition whose surveillance currently depends on repeated measurements of a dilating vessel. Automated and reproducible measurement of both the aneurysm sac and the blood clot within it could standardize the growth assessments that determine when surgical intervention becomes necessary.

Oncological imaging appears in several guises. Breast ultrasound, a mainstay of screening in dense tissue where mammography performs poorly, is the subject of two contributions: one combines adaptive ensemble loss functions with multi-scale attention in a U-shaped segmentation network, and another applies hybrid adaptive attention with deep supervision to breast lesion segmentation in ultrasound computed tomography images, a modality that reconstructs acoustic properties of tissue in three dimensions. A separate study asks a deceptively simple question: can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images? The affirmative answer carries a broader lesson, namely that metadata and acquisition parameters that are usually discarded during preprocessing can themselves be informative features, encoding information about tissue scattering physics that the image alone may not fully convey.

The collection also reaches into specialties where ultrasound plays a surgical rather than diagnostic role. One paper presents three-dimensional ultrasound shape completion and anatomical feature detection for minimally invasive spine surgery. In such procedures, the surgeon works through small incisions with limited direct visualization, and intraoperative ultrasound offers a radiation-free alternative to fluoroscopy for navigating the bony anatomy. Shape completion, the task of inferring the full geometry of a structure from partial or occluded observations, is a natural fit for this setting because acoustic shadowing from bone routinely leaves gaps in the image. Another contribution introduces a network for tongue contour segmentation built on a carefully managed dataset, extending ultrasound into the assessment of speech and swallowing, where tracking the tongue’s shape during movement has applications ranging from speech therapy to the study of deglutition disorders.

Taken together, these twenty studies sketch the contours of a field in transition. Several unifying threads deserve emphasis. First, foundation models and promptable segmentation architectures, exemplified by the adaptation of the Segment Anything Model to cardiac imaging, are lowering the cost of building application-specific tools, since a pretrained general model can be specialized with far less data than a network trained from scratch. Second, semi-supervised and data-efficient paradigms are ascendant precisely because expert annotation remains the scarcest resource in medical imaging; every hour a cardiologist spends tracing ventricles is an hour not spent with patients. Third, uncertainty quantification, whether through evidential training or related techniques, is moving from a theoretical nicety to a practical requirement, since a clinical assistant that cannot signal when it is unsure is more dangerous than one that abstains. Fourth, the papers collectively span the pipeline from raw signal to deployed system, encompassing data augmentation, architecture design, loss engineering, and operational maintenance, which reflects a maturing understanding that clinical impact requires excellence at every stage rather than at any single one.

The geographic and institutional diversity of the authorship, spanning universities across Italy and an international community of contributors, underscores that ultrasound artificial intelligence is a genuinely global enterprise. Ultrasound is the imaging modality most accessible in low-resource settings, and advances that make its interpretation more reliable without requiring expensive hardware have direct implications for health equity. A portable probe paired with a smartphone and a well-calibrated neural network can, in principle, bring diagnostic capabilities to places where computed tomography scanners and specialist radiologists are unavailable. The editorial’s framing of the special issue as a revolution in image processing and analysis is therefore not merely rhetorical: it points toward a future in which the operator dependence that has long been ultrasound’s greatest limitation is progressively offset by algorithms that standardize measurement, flag pathology, and guide the hand of the person holding the probe. The papers gathered under the AI4US banner, now available as an online collection, provide both a snapshot of current capability and a roadmap of the problems, from four-dimensional valve dynamics to quantitative tissue characterization, that the next generation of work will need to solve.

Subject of Research: The application of artificial intelligence and deep learning to ultrasound image processing and analysis across medical specialties.

Article Title: AI4US: Revolutionizing ultrasound image processing and analysis with artificial intelligence

Article References: Moccia, S., Fiorentino, M. C., & Tomassini, S. (2026). AI4US: Revolutionizing ultrasound image processing and analysis with artificial intelligence. Medical & Biological Engineering & Computing. https://doi.org/10.1007/s11517-026-03667-0

Image Credits: AI Generated

DOI: 10.1007/s11517-026-03667-0

Keywords: ultrasound, artificial intelligence, deep learning, echocardiography, image segmentation, medical imaging, machine learning, computer vision, fetal ultrasound, breast ultrasound, SAM, clinical diagnosis