Training and prototype development in robot-assisted surgery requires appropriate and safe environments for the execution of surgical procedures. Current dry lab laparoscopy phantoms often lack the ability to mimic complex, interactive surgical tasks. This work presents an interactive surgical phantom for the cholecystectomy. The phantom enables the removal of the gallbladder during cholecystectomy by allowing manipulations and cutting interactions with the synthetic tissue. The force-displacement behavior of the gallbladder is modelled based on retraction demonstrations. The force model is compared to the force model of ex-vivo porcine gallbladders and evaluated on its ability to estimate retraction forces.

Vertebrate animals benefit from a combination of rigidity for structural support and softness for adaptation. Similarly, integrating rigidity and softness can enhance the versatility of soft robotics. However, the challenges associated with creating durable bonding interfaces between soft and rigid materials have limited the development of hybrid robots. Existing solutions require specialized machinery, such as polyjet 3D printers, which are not commonly available. In response to these challenges, we have developed a 3D printing technique that can be used with almost all commercially available FDM printers. This technique leverages the common issue of underextrusion to create a strong bond between soft and rigid materials. Underextrusion generates a porous structure, similar to fibrous connective tissues, that provides a robust interface with the rigid part through layer fusion, while the porosity enables interlocking with the soft material. Our experiments demonstrated that this method outperforms conventional adhesives commonly used in soft robotics, achieving nearly 200\% of the bonding strength in both lap shear and peeling tests. Additionally, we investigated how different porosity levels affect bonding strength. We tested the technique under pressure scenarios critical to soft and hybrid robots and achieved three times more pressure than the current adhesion solution. Finally, we fabricated various hybrid robots using this technique to demonstrate the wide range of capabilities this approach and hybridity can bring to soft robotics. has context menu

Artificial intelligence (AI), machine learning, and deep learning (DL) methods are becoming increasingly important in the field of biomedical image analysis. However, to exploit the full potential of such methods, a representative number of experimentally acquired images containing a significant number of manually annotated objects is needed as training data. Here we introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data for DL systems. We show the versatility of our approach in the context of muscle fiber and connective tissue analysis in histological sections. We demonstrate that it is possible to perform robust and expert-level segmentation tasks on previously unseen real-world data, without the need for manual annotations using synthetic training data alone. Being a fully parametric technique, our approach poses an interpretable and controllable alternative to Generative Adversarial Networks (GANs) and has the potential to significantly accelerate quantitative image analysis in a variety of biomedical applications in microscopy and beyond.

$\bf{Purpose}$: To describe the 3D structural changes in both connective and neural tissues of the optic nerve head (ONH) that occur concurrently at different stages of glaucoma using traditional and AI-driven approaches. $\bf{Methods}$: We included 213 normal, 204 mild glaucoma (mean deviation [MD] $\ge$ -6.00 dB), 118 moderate glaucoma (MD of -6.01 to -12.00 dB), and 118 advanced glaucoma patients (MD < -12.00 dB). All subjects had their ONHs imaged in 3D with Spectralis optical coherence tomography. To describe the 3D structural phenotype of glaucoma as a function of severity, we used two different approaches: (1) We extracted human-defined 3D structural parameters of the ONH including retinal nerve fiber layer (RNFL) thickness, lamina cribrosa (LC) shape and depth at different stages of glaucoma; (2) we also employed a geometric deep learning method (i.e. PointNet) to identify the most important 3D structural features that differentiate ONHs from different glaucoma severity groups without any human input. $\bf{Results}$: We observed that the majority of ONH structural changes occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using PointNet, we also found that 3D ONH structural changes were present in both neural and connective tissues. In both approaches, we observed that structural changes were more prominent in the superior and inferior quadrant of the ONH, particularly in the RNFL, the prelamina, and the LC. As the severity of glaucoma increased, these changes became more diffuse (i.e. widespread), particularly in the LC. $\bf{Conclusions}$: In this study, we were able to uncover complex 3D structural changes of the ONH in both neural and connective tissues as a function of glaucoma severity. We hope to provide new insights into the complex pathophysiology of glaucoma that might help clinicians in their daily clinical care.