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ObjectiveThis study analyzes the risk points in the quality control of bioink and the main processes of bioprinting, clarifies and explores the quality control and supervision model for bioprinting medical devices, and provides theoretical and practical guidance to ensure the safety and effectiveness of bioprinting medical devices. MethodsThe quality control risk points throughout the bioprinting process were comprehensively analyzed, with a particular focus on bioprinting materials and key processes. The regulatory model and methods for bioprinting medical devices were examined. This research concentrated on critical technologies such as extrusion, laser-assisted, and in situ bioprinting, assessing their potential for clinical applications and regulatory challenges. ResultsBioink from different sources should meet regulatory requirements. It is essential to ensure aseptic handling of raw materials and to validate sterilization under "worst-case" conditions. ConclusionAs bioprinting technology advances rapidly, corresponding research into materials, processes, and quality risk control should be conducted to ensure the concurrent development of the regulatory system. This will continuously contribute to the orderly progression of the entire industry and human health.

With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

Varicella-zoster virus (VZV) infections result in a series of ophthalmic complications. Clinically, we also discover that the proportion of dry eye symptoms was significantly higher in patients with herpes zoster ophthalmicus (HZO) than in healthy individuals. Meibomian gland dysfunction (MGD) is one of the main reasons for dry eye. Therefore, we hypothesize that HZO may associate with MGD, affecting the morphology of meibomian gland (MG) because of immune response and inflammation. The purpose of this study is to retrospectively analyze the effect of HZO with craniofacial herpes zoster on dry eye and MG morphology based on an Artificial intelligence (AI) MG morphology analytic system. In this study, 26 patients were diagnosed as HZO based on a history of craniofacial herpes zoster accompanied by abnormal ocular signs. We found that the average height of all MGs of the upper eyelid and both eyelids were significantly lower in the research group than in the normal control group (p < 0.05 for all). The average width and tortuosity of all MGs for both upper and lower eyelids were not significantly different between the two groups. The MG density of the upper eyelid and both eyelids were significantly lower in the HZO group than in the normal control group (p = 0.020 and p = 0.022). Therefore, HZO may lead to dry eye, coupled with the morphological changes of MGs, mainly including a reduction in MG density and height. Moreover, it is important to control HZO early and timely, which could prevent potential long-term severe ocular surface injury.

Most of the intelligent surveillances in the industry only care about the safety of the workers. It is meaningful if the camera can know what, where and how the worker has performed the action in real time. In this paper, we propose a light-weight and robust algorithm to meet these requirements. By only two hands’ trajectories, our algorithm requires no Graphic Processing Unit (GPU) acceleration, which can be used in low-cost devices.In the training stage, in order to find potential topological structures of the training trajectories, spectral clustering with eigengap heuristic is applied to cluster trajectory points. A gradient descent based algorithm is proposed to find the topological structures, which reflects main representations for each cluster. In the fine-tuning stage, a topological optimization algorithm is proposed to fine-tune the parameters of topological structures in all training data. Finally, our method not only performs more robustly compared to some popular offline action detection methods, but also obtains better detection accuracy in an extended action sequence.

PurposeA six-category model of common retinal diseases is proposed to help primary medical institutions in the preliminary screening of the five common retinal diseases.MethodsA total of 2,400 fundus images of normal and five common retinal diseases were provided by a cooperative hospital. Two six-category deep learning models of common retinal diseases based on the EfficientNet-B4 and ResNet50 models were trained. The results from the six-category models in this study and the results from a five-category model in our previous study based on ResNet50 were compared. A total of 1,315 fundus images were used to test the models, the clinical diagnosis results and the diagnosis results of the two six-category models were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), 95% confidence interval, kappa and accuracy, and the receiver operator characteristic curves of the two six-category models were compared in the study.ResultsThe diagnostic accuracy rate of EfficientNet-B4 model was 95.59%, the kappa value was 94.61%, and there was high diagnostic consistency. The AUC of the normal diagnosis and the five retinal diseases were all above 0.95. The sensitivity, specificity, and F1-score for the diagnosis of normal fundus images were 100, 99.9, and 99.83%, respectively. The specificity and F1-score for RVO diagnosis were 95.68, 98.61, and 93.09%, respectively. The sensitivity, specificity, and F1-score for high myopia diagnosis were 96.1, 99.6, and 97.37%, respectively. The sensitivity, specificity, and F1-score for glaucoma diagnosis were 97.62, 99.07, and 94.62%, respectively. The sensitivity, specificity, and F1-score for DR diagnosis were 90.76, 99.16, and 93.3%, respectively. The sensitivity, specificity, and F1-score for MD diagnosis were 92.27, 98.5, and 91.51%, respectively.ConclusionThe EfficientNet-B4 model was used to design a six-category model of common retinal diseases. It can be used to diagnose the normal fundus and five common retinal diseases based on fundus images. It can help primary doctors in the screening for common retinal diseases, and give suitable suggestions and recommendations. Timely referral can improve the efficiency of diagnosis of eye diseases in rural areas and avoid delaying treatment.

Robotic operation is one of the major challenges in the remote maintenance of ITER vacuum vessel (VV) and future fusion reactors as inner operations of Tokamak have to be done by robots due to the internal adverse conditions. This paper introduces a novel remote handling robot (RHR) for the maintenance of ITER-like D-shaped vessel. The modular designed RHR, which is an important part of the remote handling system for ITER, consists of three parts: an omnidirectional transfer vehicle (OTV), a planar articulated arm (PAA), and an articulated teleoperated manipulator (ATM). The task of RHR is to carry processing tools, such as the viewing system, leakage detector, and electric screwdriver, to inspect and maintain the components installed inside the D-shaped vessel. The kinematics of the OTV, as well as the kinematic analyses of the PAA and ATM, is studied in this paper. Because of its special length and heavy payload, the dynamics of the PAA is also investigated through a dynamic simulation system based on robot technology middleware (RTM). The results of the path planning, workspace simulations, and dynamic simulation indicate that the RHR has good mobility together with satisfying kinematic and dynamic performances and can well accomplish its maintenance tasks in the ITER-like D-shaped vessel.

In this paper, we propose a fast feature points-based object tracking method for robot grasp. In the detection phase, we detect the object with SIFT feature points extraction and matching. Then we compute the object's image position with homography constraints and set up an interest window to accommodate the object. In the tracking phase, we only focus on the interest window, detecting feature points from the window and updating the window's position and size. Our method is of special practical meaning in the case of service robot grasp. Because when the robot grasps the object, the object's image size is usually small relative to the whole image, it is unnecessary to detect the whole image. On the other hand, the object is partially occluded by the robot gripper. SIFT is good at dealing with occlusion, but it is time consuming. Hence, by combining SIFT and an interest window, our method gains the ability to deal with occlusion and can satisfy the real-time requirements at the same time. Experiments show that our method exceeds several leading feature points-based object tracking methods in real-time performance.

This paper proposes a combined method of ant-based clustering and cerebellar model articulation controller for performance assessment for a fleet of machines. A novel ant-based clustering algorithm with kernel method is used to cluster machines in a fleet. The algorithm has two features. First, a projection based on kernel principal component analysis replaces random projection to improve the efficiency. Second, the clustering is performed on the feature space after kernel mapping to improve the clustering accuracy. The algorithm can cluster machines in a self-organizing way to achieve the horizontal assessment. The vertical assessment for the single machine based on CMAC is presented. Then, how to combine the vertical and horizontal assessment results is discussed. The outlier mining method to detect abnormal machines based on the clustering results is also proposed. Cluster-based global outlying factor is suggested to measure the outlying degree of abnormal machine. Finally, the case study on axial fans shows that the combined method can give a more comprehensive assessment for fans' performance monitoring.

In the field of machine vision, the multi-solution problem of the perspective-three-point (P3P) camera pose estimation problem has been widely researched. Many researchers have tried to find unique solutions that will enable the use of the P3P method in practical applications such as the calibration of external parameters of a camera. Gao found a special case that is very simple and intuitive, but can only be used for a camera with a very large field of view. Therefore, it did not attract much attention. The purpose of this study was to analyse Gao's special case and use it for calibrating the external parameters of an omnidirectional camera, which is used widely because of its very large field of view. We first verify Gao's special case and extend its application. We then propose an iterative method for finding the unique solution. After this, we perform a simulation experiment to determine the effect of the configuration of the three control points on the accuracy of the unique solution. Finally, we use an application to examine the efficiency of the method.

A more suitable motion planning method for an omni-directional mobile robot (OMR), an improved APF method (iAPF), is proposed in this paper by introducing the revolving factor into the artificial potential field (APF). Accordingly, the motion direction derived from traditional artificial potential field (tAPF) is regulated. The maximum velocity, maximum acceleration and energy consumption of the OMR moving in different directions are analyzed, based on the kinematic and dynamic constraints of an OMR, and the anisotropy of OMR is presented in this paper. Then the novel concept of an Anisotropic-Function is proposed to indicate the quality of motion in different directions, which can make a very favorable trade-off between time-optimality, stability and efficacy-optimality. In order to obtain the optimal motion, the path that the robot can take in order to avoid the obstacle safely and reach the goal in a shorter path is deduced. Finally, simulations and experiments are carried out to demonstrate that the motion resulting from the iAPF is high-speed, highly stable and highly efficient when compared to the tAPF.

This paper presents a rapid traversability assessment approach based on an extended 2.5D grid-based representaion of the rough terrain. Stereo vision system is used to perceive the environment surrounding robot. Conventional 2D, 3D and other 2.5D grid maps determine the traversability indices of the grids directly from the sensor feedback, while our approach attempts to address the indices of terrain from multiple grids instead. By analyzing the properties of multiple grids that the robot is to traverse, passable grids are distinguished, which also takes the robot's size into account. Fuzzy logic framework is applied to extract traversabiltiy indices from the terrain characteristics. A soccer robot equipped with a stereo vision system is adopted for experiments. The results show that our map is capable of speeding the process of traversability assessment and providing an autonomous mobile robot with a appropriate representation of 3D uneven terrain profile.

This paper presents a rapid traversability assessment approach based on an extended 2.5D grid-based representaion of the rough terrain. Stereo vision system is used to perceive the environment surrounding robot. Conventional 2D, 3D and other 2.5D grid maps determine the traversability indices of the grids directly from the sensor feedback, while our approach attempts to address the indices of terrain from multiple grids instead. By analyzing the properties of multiple grids that the robot is to traverse, passable grids are distinguished, which also takes the robot's size into account. Fuzzy logic framework is applied to extract traversabiltiy indices from the terrain characteristics. A soccer robot equipped with a stereo vision system is adopted for experiments. The results show that our map is capable of speeding the process of traversability assessment and providing an autonomous mobile robot with a appropriate representation of 3D uneven terrain profile.