Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Journal of robotic surgery
Technical challenges in autonomous robotic ultrasound examinations: perception, planning, and control
Ultrasound is one of the most widely used medical imaging modalities in clinical practice. Robot-assisted ultrasound systems (RAUS) offer significant advantages in reducing physician workload and improving imaging standardization. However, the dynamic and complex nature of the human physiological environment severely hinders the transition of RAUS from automation to full autonomy. This paper presents a narrative review of the core research progress of RAUS between 2021 and early 2026. It comprehensively analyzes the current technological landscape of autonomous robotic ultrasound by focusing on three core pillars: perception, planning, and control. By evaluating cutting-edge breakthroughs and existing limitations across these three domains, this paper identifies the key technical bottlenecks that restrict fully autonomous RAUS. Finally, we propose an evolutionary roadmap driven by multimodal fusion, digital twins, planning-control co-design, and embodied artificial intelligence (AI) architectures. This roadmap provides a developmental reference for autonomous robotic ultrasound examinations and also lays a crucial foundation for future autonomous ultrasound-guided surgical interventions.
1 June 2026
Read appraisal →Science robotics
Embodied or virtually represented: Navigating the embodiment debate in human-robot interaction.
The validity of virtually represented robots in HRI experiments depends on when, where, and for whom it matters.
30 May 2026
Read appraisal →PloS one
MSCNet: Efficient and accurate semantic segmentation of LiDAR data using Multi-scale Convolution
In autonomous driving and intelligent robotics, the semantic information of LiDAR (Light Detection and Ranging) sensor data is crucial for understanding the surrounding environment. However, directly operating on point clouds is computationally expensive. To address this, some researchers have projected three-dimensional LiDAR data onto a two-dimensional spherical range view and used two-dimensional convolutional neural networks to segment the projected images. While the results are promising, many of these models are structurally complex, with high spatiotemporal complexity, which makes them unsuitable for real-time applications. To solve these issues, this paper proposes a multi-scale LiDAR data semantic segmentation method, MSCNet, with fewer parameters and higher segmentation accuracy. In the encoding phase, a single-channel multi-scale feature fusion block is introduced to alleviate the distribution differences between input channels. To obtain more stable local features, multi-scale dilated convolution residual blocks are designed to encode information from different receptive fields. To quickly capture global features, a pyramid pooling module is introduced. Experimental results on the SemanticKITTI, SemanticPOSS, and Pandaset datasets show that MSCNet achieves a good balance between parameter, accuracy, and running time. Particularly on the SemanticPOSS and Pandaset datasets, MSCNet achieves the best performance. Under the same parameter conditions, this method outperforms existing point cloud-based and projection-based methods.
10 Apr 2026
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