Inspired by the collective intelligence in natural swarms, microrobotic agents have been controlled to form artificial swarms for targeted drug delivery, enhanced imaging, and hyperthermia. Different from these well-investigated tasks, this work aims to develop microrobotic swarms for embolization, which is a clinical technique used to block blood vessels for treating tumors, fistulas, and arteriovenous malformations. Magnetic particle swarms were formed for selective embolization to address the low selectivity of the present embolization technique that is prone to cause complications such as stroke and blindness. We established an analytical model that describes the relationships between fluid viscosity, flow rate, branching angle, magnetic field strength, and swarm integrity, based on which an actuation strategy was developed to maintain the swarm integrity inside a targeted region under fluidic flow conditions. Experiments in microfluidic channels, ex vivo tissues, and in vivo porcine kidneys validated the efficacy of the proposed strategy for selective embolization.

Robotic manipulation of deformable objects has been a classic topic in robotics. Compared to synthetic deformable objects such as rubber balls and clothes, biological cells are highly deformable and more prone to damage. This article presents robotic manipulation of deformable cells for orientation control (both out-of-plane and in-plane), which is required in both clinical (e.g., in vitro fertilization) and biomedical (e.g., clone) applications. Compared to manual cell orientation control based on empirical experience, the robotic approach, based on modeling and path planning, effectively rotates a cell, while consistently maintaining minimal cell deformation to avoid cell damage. A force model is established to determine the minimal force applied by the micropipette to rotate a spherical or, more generally, ellipsoidal oocyte. The force information is translated into indentation through a contact mechanics model, and the manipulation path of the micropipette is formed by connecting the indentation positions on the oocyte. An optimal controller is designed to compensate for the variations of mechanical properties across oocytes. The polar body of an oocyte is detected by deep neural networks with robustness to shape and size differences. In experiments, the system achieved an accuracy of 97.6% in polar body detection and an accuracy of 0.7 $^{\circ }$ in oocyte orientation control with maximum oocyte deformation of 2.70 $\mu$ m throughout the orientation control process.

Background: In infertility treatment, blastocyst morphological grading is commonly used in clinical practice for blastocyst evaluation and selection, but has shown limited predictive power on live birth outcomes of blastocysts. To improve live birth prediction, a number of artificial intelligence (AI) models have been established. Most existing AI models for blastocyst evaluation only used images for live birth prediction, and the area under the receiver operating characteristic (ROC) curve (AUC) achieved by these models has plateaued at ~0.65.Methods: This study proposed a multimodal blastocyst evaluation method using both blastocyst images and patient couple’s clinical features (e.g., maternal age, hormone profiles, endometrium thickness, and semen quality) to predict live birth outcomes of human blastocysts. To utilize the multimodal data, we developed a new AI model consisting of a convolutional neural network (CNN) to process blastocyst images and a multilayer perceptron to process patient couple’s clinical features. The data set used in this study consists of 17,580 blastocysts with known live birth outcomes, blastocyst images, and patient couple’s clinical features.Results: This study achieved an AUC of 0.77 for live birth prediction, which significantly outperforms related works in the literature. Sixteen out of 103 clinical features were identified to be predictors of live birth outcomes and helped improve live birth prediction. Among these features, maternal age, the day of blastocyst transfer, antral follicle count, retrieved oocyte number, and endometrium thickness measured before transfer are the top five features contributing to live birth prediction. Heatmaps showed that the CNN in the AI model mainly focuses on image regions of inner cell mass and trophectoderm (TE) for live birth prediction, and the contribution of TE-related features was greater in the CNN trained with the inclusion of patient couple's clinical features compared with the CNN trained with blastocyst images alone.Conclusions: The results suggest that the inclusion of patient couple’s clinical features along with blastocyst images increases live birth prediction accuracy.Funding: Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Program.

Recent interest in high-precision positioning stages with large travel ranges has sparked renewed attention to the development of voice coil motors (VCMs). Due to their large output force, VCMs can actuate more complicated flexure structures, eliminate rail friction, and improve positioning speed. The VCM structure is both compact and flexible; hence, it is convenient to design VCMs for a variety of stage structures. Furthermore, VCMs combined with other actuators are able to achieve large travel ranges with high precision. In this paper, we summarize the principles and control methods of a typical VCM, and we analyze its properties, including thrust force, acceleration, and response time. We then present recent research on high-precision VCM positioning stages with large travel ranges.

The robotic manipulation of deformable linear objects is a classic and challenging topic. Apart from synthetic objects, such as wires and cables, linear objects are also commonly found in biological cells and organisms. Biomanipulation of such objects is hampered by difficulties, such as limited degrees of freedom of micromanipulators and varied mechanical properties of the biological entities to manipulate. This article presents a robotic manipulation of human sperm, which are deformable cells with a linear shape. The shape and movement of the cell are recapitulated by our developed geometric and kinematic models. Under unfixed constraints between the end-effector and the cell, path planning is designed to update the manipulation point to control cell deformation. A state transition function is formulated in path planning to handle the stiffness variations of sperm without force sensing. A model-predictive controller is designed to minimize the orientation error and manipulation path length. To detect sperm tail for visual feedback, an accuracy of 98% was achieved via deep neural networks. The robotic manipulation of human sperm was performed using a standard clinical setup of a glass micropipette to rotate a sperm to the target orientation. Experimental results showed that robotic sperm rotation achieved an orientation error of 0.8 $^{\circ }$ , a tail curvedness of 0.14 $\mu$ m $^{-1}$ , and an operation time of 5.6 s, all significantly less than those of the manual approach. The less orientation error and tail curvedness after robotic rotation led to a significantly lower speed of sperm entering the micropipette during sperm aspiration, resulting in a higher success rate of 97% (versus 76% after manual rotation) for aspiration control.

Precise rotational positioning of end-effectors under microscopy is crucial for robotic micromanipulation. However, the end-effector is presently limited to a fixed orientation, which is manually set before a given micromanipulation task, lacking accuracy and versatility of in situ reorientation. In this article, we present a unified framework for rotationally positioning the end-effector in three dimensions by establishing a general rotational model, developing a detection method within the limited field of view under microscopy, and designing a three-loop control strategy that adapts to different experimental requirements and model parameters. In experiments, a standard angled micropipette was used as the end-effector to verify the validity of the proposed methods. The performance was evaluated experimentally where the micropipette was robotically rotated to an arbitrarily desired orientation with an average orientation error less than $2^\circ$ . In the experiments of sperm manipulation, the in situ micropipette orientation control capability improved the success rate of sperm immobilization and achieved dexterous robotic sperm orientation for facile aspiration.