Obtaining venous access for blood sampling or intravenous (IV) fluid delivery is an essential first step in patient care. However, success rates rely heavily on clinician experience and patient physiology. Difficulties in obtaining venous access result in missed sticks and injury to patients, and typically require alternative access pathways and additional personnel that lengthen procedure times, thereby creating unnecessary costs to healthcare facilities. Here, we present the first-in-human assessment of an automated robotic venipuncture device designed to safely perform blood draws on peripheral forearm veins. The device combines ultrasound imaging and miniaturized robotics to identify suitable vessels for cannulation and robotically guide an attached needle toward the lumen center. The device demonstrated results comparable to or exceeding that of clinical standards, with a success rate of 87% on all participants ([Formula: see text]), a 97% success rate on nondifficult venous access participants ([Formula: see text]), and an average procedure time of [Formula: see text][Formula: see text]s ([Formula: see text]). In the future, this device can be extended to other areas of vascular access such as IV catheterization, central venous access, dialysis, and arterial line placement.

Diagnostic blood testing is the most commonly performed clinical procedure in the world, and influences the majority of medical decisions made in hospital and laboratory settings. However, manual blood draw success rates are dependent on clinician skill and patient physiology, and results are generated almost exclusively in centralized labs from large-volume samples using labor-intensive analytical techniques. This paper presents a medical device that enables end-to-end blood testing by performing blood draws and providing diagnostic results in a fully automated fashion at the point-of-care. The system couples an image-guided venipuncture robot, developed to address the challenges of routine venous access, with a centrifuge-based blood analyzer to obtain quantitative measurements of hematology. We first demonstrate a white blood cell assay on the analyzer, using a blood mimicking fluid spiked with fluorescent microbeads, where the area of the packed bead layer is correlated with the bead concentration. Next we perform experiments to evaluate the pumping efficiency of the sample handling module. Finally, studies are conducted on the integrated device — from blood draw to analysis — using blood vessel phantoms to assess the accuracy and repeatability of the resulting white blood cell assay.

Preclinical testing in rodent models is a ubiquitous part of modern biomedical research and commonly involves accessing the venous bloodstream for blood sampling and drug delivery. Manual tail vein cannulation is a time-consuming process and requires significant skill and training, particularly since improperly inserted needles can affect the experimental results and study outcomes. In this paper, we present a miniaturized, robotic medical device for automated, image-guided tail vein cannulations in rodent models. The device is composed of an actuated three degrees-of-freedom (DOFs) needle manipulator, three-dimensional (3D) near-infrared (NIR) stereo cameras, and an animal holding platform. Evaluating the system through a series of workspace simulations and free-space positioning tests, the device exhibited a sufficient work volume for the needle insertion task and submillimeter accuracy over the calibration targets. The results indicate that the device is capable of cannulating tail veins in rodent models as small as 0.3 mm in diameter, the smallest diameter vein required to target.

Medical robots provide enhanced dexterity, vision, and safety for a broad range of procedures. In this paper, we present a hand-held, robotic device capable of performing peripheral catheter insertions with high accuracy and repeatability. The device utilizes a combination of ultrasound imaging, miniaturized robotics, and machine learning to safely and efficiently introduce a catheter sheath into a peripheral blood vessel. Here, we present the mechanical design and experimental validation of the device, known as VeniBot. Additionally, we present results on our ultrasound deep learning algorithm for vessel segmentation, and performance on tissue-mimicking phantom models that simulate difficult peripheral catheter placement. Overall, the device achieved first-attempt success rates of 97 ± 4% for vessel punctures and 89 ± 7% for sheath cannulations on the tissue mimicking models (n=240). The results from these studies demonstrate the viability of a hand-held device for performing semi-automated peripheral catheterization. In the future, the use of this device has the potential to improve clinical workflow and reduce patient discomfort by assuring a safe and efficient procedure.