Background: Epilepsy is a heterogeneous syndrome. Personalised localisation of epileptogenic zone (EZ) is critical for diagnosis and treatment of drug-resistant focal epilepsy. Multichannel stereoelectroencephalography (SEEG) monitoring acquired over a period of two to three weeks was collected in different patients, resulting in comprehensive epileptogenic information and terabytes of high dimensional data. Consequently, there is a need for high-throughput data analytical methods to enable data-driven, personalised seizure detection and EZ localisation. Methods: Here, a seizure detection and EZ localisation AI system — SEEGformer is proposed, by utilising SEEG data from 61 patients acquired across two centres and three cohorts capturing tens of thousands of abnormal discharges and around ten seizures per person on average. SEEGformer employs a parallel transformer architecture to analyse multiple representations of multichannel SEEG signals, including the real part, imaginary part, and amplitude of the analytic signal after Fourier transform. The MRI information was encoded in SEEGformer to construct the structural dependence of the brain areas. Inter-channel dependencies and interactions were captured for seizure detection. A cross-channel attention mechanism computed the epileptogenic risk score for each channel to localise EZ using ictal SEEG data. Each patient’s SEEG data was used to train and validate their individual-specific SEEGformer model. Findings: In three clinical cohorts, SEEGformer achieved an average AUROC of 0.937 (95% CI, 0.922–0.950) for seizure detection and 0.798 (95% CI, 0.749–0.847) for EZ localisation. Localisation performance surpassed state-of the-art methods by over 5%. SEEGformer further revealed distinct phase synchronisation patterns in dynamically evolving epileptogenic zone networks, with a significance level of P < 0.0001. Interpretation: Due to its high interpretability and visualisation capabilities, SEEGformer can enhance clinical decision-making by providing an objective, data-driven reference to optimise epileptogenic zone delineation and surgical strategy development. Currently, the improved SEEGformer is being developed to construct a dedicated SEEG atlas for epilepsy.

Through fusing materials into thin, elongated strands with structured cross-sections, recent advances in multimaterial fibers have enabled myriad applications for simultaneous sensing of many different inputs. Addressing the underlying challenges of preform fabrication, thermal compatibility of dissimilar materials with heterogeneous properties, and functional material deposition enables the creation of flexible embodiments such as smart fabrics or miniaturized devices for in vivo applications. By leveraging micro-nano fabrication techniques, microscale devices can be integrated at the tip of the fiber with photo, thermal, magnetic, electro, pneumatic, or hydraulic actuation, leading to thin, tethered fiberbots for targeted therapy and endoluminal intervention.

Medical image registration, which establishes spatial correspondences between different medical images, serves as a fundamental process in numerous clinical applications and diagnostic workflows. Despite significant advancement in unsupervised deep learning-based registration methods, these approaches consistently yield suboptimal results compared to their weakly-supervised counterparts. Recent advancements in universal segmentation models have made it easier to obtain anatomical labels from medical images. However, existing registration methods have not fully leveraged the rich anatomical and structural prior information provided by segmentation labels. To address this limitation, we propose a BIdirectional feature Alignment and Semantics-guided Network, referred to as BIASNet, for weakly-supervised image registration. Specifically, starting from multi-scale features extracted from the pre-trained VoCo, fine-tuned using Low-Rank Adaptation (LoRA), we propose a dual-attribute learning scheme, incorporating a novel BIdirectional Alignment and Fusion (BIAF) module for extracting both semantics-wise and intensity-wise features. These two types of features are subsequently fed into a semantics-guided progressive registration framework for accurate deformation field estimation. We further propose an anatomical region deformation consistency learning to regularize the target anatomical regions deformation. Comprehensive experiments conducted on three typical yet challenging datasets demonstrate that our method achieves consistently better results than other state-of-the-art deformable registration approaches. The source code is publicly available at https://github.com/xiehousheng/BIASNet.

Deformation of flexible robots can be practically assessed using extension/compression, shear, curvature, and torsion. Sensing based on one or more of the above characteristics enables closed-loop control for delicate tasks that require precision and dexterity. Due to the increasing popularity of flexible robotics in recent years, significant research effort has been directed to this burgeoning field. Although numerous studies have addressed soft sensing technologies, their successful integration into flexible robotic systems remains limited. This article provides a comprehensive review of sensing methods, from multidimensional deformation to the underlying principles of deriving hard-to-measure deformation from surrogate parameters. It focuses on sensing modalities such as strain measurement via piezoelectric, capacitive, resistive, and optical techniques. The applications of deformation sensing in industrial and service robotics are described. Future challenges and potential research issues including resolution, conformability, multifunctionality, crosstalk, and miniaturization are discussed. The need for a synergistic approach across disciplines is highlighted, emphasizing the integration of new materials, microstructures, advanced manufacturing technologies, and state-of-the-art signal processing techniques.

Intraoperative and spatiotemporal monitoring of neuroinflammatory indices during brain-computer interface (BCI) implantation is essential for ensuring safety and efficacy of the procedure. Current biomolecular detection approaches are unable to obtain spatiotemporally resolved inflammatory profiling, which is important for guiding the placement of microelectrodes intraoperatively. This study presents an intraoperative spatiotemporal acute inflammation detector (ISAID) that harnesses droplet-based sampling and multiplexed titanium oxynitride (TiNO) plasmonic biosensing to assess local inflammation during the insertion of intracortical microelectrodes. Through freestanding sampling droplets and fine-tuned TiNO-based biosensors, the ISAID achieved precise, sensitive, and integrated sampling to biosensing for cytokine detection with a spatial resolution down to 610 μm and a fast equivalent bioassay time of 1.25 min. The proposed system also allows multiple ISAID biosensing modes, enabling both spatial inflammation mapping and multi-cytokine analysis. Quantitative analyses of inflammatory cytokines with in vivo mouse models demonstrate the accuracy and practical advantages of the system.

Precision-controlled microscale manipulation tasks—including neural probe implantation, ophthalmic surgery, and cell membrane puncture— often involve minimally invasive membrane penetration techniques with real-time force feedback to minimize tissue trauma. This imposes rigorous design requirements on the corresponding miniaturised instruments with robotic assistance. This paper proposes an integrated piezoelectric module (IPEM) that combines high-frequency vibration-assisted penetration with real-time in-situ force sensing. The IPEM features a compact piezoelectric actuator integrated with a central tungsten probe, generating axial micro-vibration (4652 Hz) to enable smooth tissue penetration while simultaneously measuring contact and penetration forces via the piezoelectric effect. Extensive experiments were conducted to validate the effectiveness and efficacy of the pro-posed IPEM. Both static and dynamic force sensing tests demonstrate the linearity, sensitivity(9.3 mV/mN), and accuracy (MAE < 0.3mN, MAPE < 1%) of the embedded sensing unit. In gelatin phantom tests, the module reduced puncture and insertion forces upon activation of vibration. In vivo experiments in mouse brains further confirmed that the system could reduce penetration resistance (from an average of 11.67mN without vibration to 7.8mN with vibration, decreased by 33%) through the pia mater and accurately mimic the electrode implantation-detachment sequence, leaving a flexible electrode embedded with minimal trauma. This work establishes a new paradigm for smart surgical instruments by integrating a com-pact actuator-sensor design with real-time in-situ force feedback capabilities, with immediate applications in brain-machine interfaces and microsurgical robotics.

Robot-assisted microsurgery is a promising technique for a number of clinical specialties including neurosurgery. One of the prerequisites of such procedures is accurate vision guidance, delineating not only the exposed surface details but also embedded microvasculature. Conventional microscopic cameras used for vascular imaging are susceptible to specular reflections and changes in ambient light with low tissue resolution and contrast. Photoacoustic microscopy (PAM) is emerging as a promising tool and increasingly used for vascular imaging due to its high image resolution and tissue contrast. This paper presents a fused microscopic imaging scheme that integrates standard surgical microscopy with PAM for improved intraoperative visualization and guidance. We propose the FPM-RNet to Fuse Photoacoustic and surgical Microscopic imaging via cross-modality Representation and Registration Network. A MOdality Representation Network (MORNet) is used to extract unified feature representation across white-light and PAM modalities, and a Hierarchical Iterative Registration Network (HIRNet) is used to establish the correspondence between the two modalities in a coarse-to-fine manner based on multi-resolution feature maps. A synthetic dataset with ground truth correspondence and an in vivo dataset of mouse brain vasculature are used to evaluate our proposed network. Extensive validation on the two datasets has shown significant improvements compared to the current state-of-the-art methods assessed with intersection over union and Dice scores (10.3% and 6.6% on the synthetic dataset and 15.9% and 11.8% on the in vivo dataset, respectively).

Stereotactic neurointervention is a common procedure for biopsy, injection, ablation, and implantation of electrodes for deep brain stimulation. Guided by preoperative imaging, conventional approaches are mostly performed manually, lacking operation stability and interactive feedback. The intraoperative magnetic resonance imaging (MRI) guidance enables both structural and functional assessment during operation, permitting interactive adaptation to tissue deformation and avoidance of critical anatomical regions. Here, we report an MRI-guided robotic system for stereotactic neurointervention. A macro-micro hybrid pneumatic-hydraulic actuated stereotactic robot with a large range of motion and high precision is developed. This is coupled with a compact bioinspired soft actuator for target intervention. A global-focal MRI sequence is proposed for interactive navigation, closed-loop control, and precise targeting. Validation is performed with phantom, cadaveric, and in vivo animal studies, showing positional accuracies of 0.39, 0.68, and 0.14 millimeters, respectively, demonstrating superior performance compared to the current state of the art in robotic-assisted stereotactic neurointervention.

Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image segmentation. However, convolutional neural networks (CNNs) suffer from limited receptive fields, which fail to model the long-range dependency of organs or tumors. Besides, these models are heavily dependent on the training of the final segmentation head. And existing methods can not well address aforementioned limitations simultaneously. Hence, in our work, we proposed a novel shape prior module (SPM), which can explicitly introduce shape priors to promote the segmentation performance of UNet-based models. The explicit shape priors consist of global and local shape priors. The former with coarse shape representations provides networks with capabilities to model global contexts. The latter with finer shape information serves as additional guidance to relieve the heavy dependence on the learnable prototype in the segmentation head. To evaluate the effectiveness of SPM, we conduct experiments on three challenging public datasets. And our proposed model achieves state-of-the-art performance. Furthermore, SPM can serve as a plug-andplay structure into classic CNNs and Transformer-based backbones, facilitating the segmentation task on different datasets.

Endoluminal and endocavitary intervention via natural orifices of the body is an emerging trend in medicine, further underpinning the future of early intervention and precision surgery. This motivates the development of small continuum robots to navigate freely in confined and tortuous environment. The trade-off between a large range of motion and high precision with concomitant actuation cross-talk poses a major challenge. Here, we present a submillimeter-scale fiber robot (~1 mm) capable of decoupled macro and micro manipulations for intervention and operation. The thin optical fibers, working both as mechanical