Additive manufacturing (AM) has expanded the design space for actuator systems by enabling complex internal geometries, multimaterial architectures, functional gradients, embedded channels, and application-specific components that are difficult to realize using conventional fabrication alone. These capabilities are particularly relevant when actuator performance is governed by the coupling between material selection, printed architecture, stimulus response, and system-level integration. This review examines additively manufactured actuators and actuator components using a material-architecture-function-integration framework. The actuator classes considered include soft pneumatic and fluidic actuators, electroactive and piezoelectric polymer actuators, shape-memory and 4D-printed actuators, magnetic and magnetoactive actuators, and printed pneumatic, hydraulic, mechanical, and aerospace-grade actuator components. Representative applications are discussed across biomedical and rehabilitation systems, aerospace and deployable mechanisms, soft robotics, and industrial automation. Beyond summarizing printed actuator demonstrations, the review analyzes the integration barriers that determine whether AM actuators can transition from laboratory prototypes to functional systems. These barriers include material durability, leakage, fatigue, dielectric breakdown, filler dispersion, interfacial failure, dimensional variability, environmental sensitivity, auxiliary hardware requirements, sensing, control, and benchmarking. By organizing recent developments across actuator classes, application domains, and integration strategies, this review clarifies where AM provides a functional advantage over conventional fabrication and where further validation is required for reliable deployment.

Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-based methods for automated quality assessment require manually annotated datasets, which are highly labour-intensive to acquire. These ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer perceptual variations, which hampers learning efficiency. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. US2QNet uses the variational autoencoder embedded with the three modules, pre-processing, clustering and post-processing, to jointly enhance, extract, cluster and visualize the quality feature representation of ultrasound images. The pre-processing module uses filtering of images to point the network's attention towards salient quality features, rather than getting distracted by noise. Post-processing is proposed for visualizing the clusters of feature representations in 2D space. We validated the proposed framework for quality assessment of the urinary bladder ultrasound images. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods.