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. Author manuscript; available in PMC: 2026 Jul 30.
Published in final edited form as: J Med Device. 2026 Jun 12;20(5):051007. doi: 10.1115/1.4071877

Design of a Novel Insertion Training System for Central Venous Catheterization

William Worrall 1, Jason Z Moore 2
PMCID: PMC13313113  NIHMSID: NIHMS2193642  PMID: 42376219

Abstract

Central venous catheterization (CVC) is a common medical procedure with a complication rate of over 15%. Current CVC manikin-based simulators lack anatomical variation and objective feedback of user interaction skills, which greatly hinders effective skill acquisition. To address these limitations, a novel CVC insertion training system was developed and integrated with the Advanced Dynamic Haptic Robotic Trainer (DHRT+). This insertion training system contains a sensorized syringe, custom tissue phantom, and a tool detection subsystem, allowing residents to practice nearly the entire CVC procedure with realistic haptics and automated feedback on subcutaneous tool positioning. Five experiments were conducted to assess the haptic fidelity of the sensorized syringe and custom tissue and to evaluate the accuracy of subcutaneous tool detection. Results showed that the sensorized syringe, which detects aspiration and simulates arterial and venous flash, accurately reproduced clinical aspiration forces. The custom tissue phantom provided realistic force feedback during ultrasound compression and needle insertion, comparable to that of commercial tissue phantoms at a substantially lower cost. The color sensors used to distinguish between inserted guidewires and catheters accurately verified tool depth and enabled tool insertion across multiple positions on the tissue surface. Overall, novel central venous catheterization (CVC) insertion training system components were presented that enabled CVC simulation and provided accurate measurement on aspiration, guidewire depth, and catheter depth.

Keywords: Medical Training, Central Venous Catheterization, Tissue Phantom, Sensorized Syringe

Graphical Abstract

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INTRODUCTION

Central venous catheterization (CVC) is a medical procedure where a catheter is inserted into a central vein and advanced toward the heart for delivering medications, taking measurements, and drawing blood samples. Typically, an ultrasound (US) probe is used to visualize the target internal jugular vein (IJV) and surrounding anatomy. Despite being performed over 5 million times in the United States each year [1], CVC procedures have a high complication rate. In fact, over 15% of CVC patients experience complications [2] such as arterial puncture, hematoma, thrombosis, pneumothorax, or infection [3]. These complications are associated with high risks of morbidity and mortality [4] and frequently arise from improper execution of procedural steps. Vein misidentification and improper needle insertion angle and depth can result in incidental arterial puncture [5]. Unrecognized arterial puncture can lead to arterial cannulation, a serious complication with the highest potential morbidity and mortality per incidence of any CVC complication [6]. Failure to achieve venous access on the first attempt, resulting in repeated vessel puncture, increases the risk of mechanical complications [7, 8]. During guidewire insertion, advancing the guidewire too far can stimulate the heart and cause arrhythmia [9]. Although rare, guidewire-induced arrhythmia can be fatal if not quickly recognized [10]. Catheter malposition, resulting from insufficient or excessive insertion depth or inappropriate catheter length [11], can lead to vascular perforation and thrombosis [12].

The risk of CVC mechanical complications is directly correlated with the experience level of the physician performing the procedure [1, 13, 14]. Notably, complications are nearly twice as likely to occur when a physician has performed under 25 insertions compared to those with more experience [4]. The higher complication rate among inexperienced physicians can be attributed to the complexity of the CVC procedure, which consists of numerous steps that require precise coordination [15] and anatomical understanding [16]. Therefore, implementing effective CVC training strategies that accelerate skill acquisition is important to reduce complication rates. One such training method is simulation-based medical training (SBMT), which allows residents to practice needle, guidewire, and catheter insertion, without putting a patient’s health at risk [17]. Moreover, SBMT helps residents refine and retain procedural techniques and skills through deliberate practice [18]. Compared with residents trained by traditional apprenticeship-based methods, those trained by SBMT required fewer needle insertions and catheter adjustments [19], thereby reducing the risk of complications [8].

Most medical centers rely on high-fidelity manikins for CVC training, which allow residents to practice different components of the CVC procedure including US use, needle insertion, and catheter insertion [17]. While a variety of manikins exist, the high-fidelity CAE Healthcare (Sarasota, FL) Blue Phantom® training models feature a specialized Simulex™ tissue phantom that is compatible with a standard US probe and has selfhealing properties that enable repeated insertion of CVC tools [20]. Although high-fidelity manikins have been shown to improve CVC performance [21], their limitations often cause procedural skills to decline over time [22]. One such limitation is that manikins only represent a single patient anatomy, limiting residents’ exposure to realistic anatomical variations that complicate the procedure [23]. These variations are common, with approximately 54% of individuals having an IJV overlying the carotid artery [24] and 13–18% having small IJVs that are less than 5 mm in diameter [25].

Another limitation of CVC manikins is their inability to provide automated, objective feedback on procedural performance. Accurate tool positioning is critical in reducing CVC mechanical complications, but current manikins lack embedded sensors to track the position of the needle, guidewire, and catheter beneath the tissue phantom. Instead, expert instructors proctor training sessions and assess skills using non-standardized checklists that are unable to accurately evaluate tool positioning [26]. Overall, these limitations underscore the need for a novel training device that can automatically provide detailed, quantitative feedback on CVC procedural techniques.

One such training device is the Advanced Dynamic Haptic Robotic Trainer (DHRT+) [27] that simulates US-guided needle insertion [28] and catheter placement [29]. Using a mock US probe and a simulated US display, residents can practice needle insertion into a standard suture pad that consists of a thin silicone layer and a thick underlying foam layer. Instead of a standard syringe, the DHRT+ uses a sensorized original syringe (SOS) that detects aspiration during needle insertion [30]. The simulated US display presents diverse vessel anatomies that vary on IJV size, depth, and location, allowing residents to train with realistic anatomical variations [28, 31]. A personalized learning interface [32] eliminates the need for expert supervision by providing real-time, automated feedback on insertion angle, aspiration rate, number of insertion attempts, and other performance metrics [33]. After successfully accessing the simulated vein, residents can practice inserting a real guidewire and two-lumen indwelling catheter through the suture pad [29]. Computer vision [34, 35] and sensors beneath the suture pad [36] enable automated feedback on the ordering of tool usage and accuracy of catheter placement. The DHRT+ has been shown to improve residents’ self-efficacy from pre- to post-training [29, 32] and to flatten the CVC learning curve by promoting more efficient initial skill acquisition [37].

Despite these benefits, the DHRT+ has limitations in the (1) syringe design, (2) phantom tissue design, and (3) subcutaneous tool detection design. The DHRT+ syringe design system had an extremely strong pull back force of over 20 N and was prone to breaking due to complicated packaging layout, use of fragile microswitches, and a delicate syringe handle that had wires run through center and could not be rotated [30]. The DHRT+ tissue simulant used a foam backed silicon that provided support needed however, clogged sensors with broken foam from needle insertion prevented effective tool readings [30]. The DHRT+ subcutaneous tool detection relied on a complicated system of microswitches and array hall effect sensors with a magnet mounted on the catheter tip that could not accurately distinguish tool type (catheter or guidewire) [36]. The concept of using a multi-funnel for subcutaneous tool detection was proposed but proved ineffective [38] resulting in the single funnel system that was modeled in [39] and is integrated into the sensorized system in this paper. This paper presents the design and validation of novel CVC training technology that addresses these limitations by simulating key CVC procedural steps and measuring user performance during actions.

METHODS

Overview of CVC Insertion Training System Design

The novel CVC insertion training system, shown in Fig. 1, is integrated with the DHRT+ and is made of three subsystems: (1) a sensorized syringe, (2) custom tissue phantom, and (3) a subcutaneous tool detection module. Designed to closely match the geometry of a standard CVC syringe, the sensorized syringe can detect aspiration and provide visual indications of arterial and venous flash. The custom tissue phantom can be compressed by an US probe and permits the insertion of a guidewire, dilator, and catheter without the tissue sustaining significant damage. In addition, this tissue does not require an underlying support structure to maintain its shape, allowing tools to be detected after passing through it. After passing through the tissue, a subcutaneous funnel redirects the guidewire and catheter towards two color sensors. The first color sensor detects if the catheter was inserted far enough and the second color sensor detects if the guidewire was inserted too far into the body. These sensors allow for objective measurement on the positioning of subcutaneous tools within the body. When combined, these three subsystems simulate key CVC steps and provide an accurate measurement of these actions.

Fig. 1.

Fig. 1

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(1) Sensorized syringe, (2) custom tissue phantom, and (3) subcutaneous tool detection module

Sensorized Syringe Design

The sensorized syringe, shown in Fig. 2, is designed to closely match the size and appearance of a standard CVC syringe. The syringe barrel, plunger, and electronics mount are 3D-printed from PLA, allowing for inexpensive and rapid fabrication of main syringe components. A music-wire steel spring (k ≈ 0.5 N/m) fits into the syringe body to replicate the increased resistance felt during CVC aspiration. As the plunger is drawn back, the spring compresses to provide force feedback. An internal retaining ring expands into a groove on the barrel’s inner wall and traps components inside the syringe, eliminating fragile external fasteners used by the previous SOS design. An 18-gauge introducer needle can be press-fit onto the tip of the sensorized syringe, allowing for easy attachment and removal during the CVC simulation.

Fig. 2.

Fig. 2

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Sensorized syringe cross-section

The electronic mount inside the syringe barrel holds a red LED, blue LED, and a non-latching Hall effect sensor. These components are wired to an Arduino (Monza, Italy) Uno microcontroller using an Ethernet cable that is threaded into the syringe barrel. A small magnet on the top of the plunger trips the Hall effect sensor when the plunger is drawn. The Hall effect sensor readings are then transmitted to the DHRT+ software, which provides objective feedback on the detected aspiration time during needle insertion. An electromagnetic tracker is attached to the syringe body to utilize the existing syringe position tracking feature of the DHRT+. Depending on which vessel is accessed during needle insertion, the DHRT+ software transmits text commands to the Arduino Uno that controls the two LEDs. If the artery is accessed, the red LED within the syringe lights up. Alternatively, successful venous access triggers the blue LED. These LEDs simulate the flash of blood that enters the syringe during vessel access that proceduralists use verify that the vein was successfully accessed.

Custom Tissue Phantom Design

Detailed in Fig. 3, the 120 mm x 90 mm custom tissue phantom has a nylon mesh embedded within a 4 mm layer of Smooth-On (Macungie, PA) Ecoflex™ 00-10 silicone. This silicone has a low Shore hardness and high elasticity, allowing large-diameter tools like the dilator and catheter to easily penetrate it without causing excessive tearing or damage. Despite its flexibility, this silicone can hold a needle tip in place after it is detached from the syringe before guidewire insertion. The nylon mesh, which is held under tension by a 3D-printed support frame, allows the tissue to be compressed with an US probe without permanently deforming the silicone. Without an underlying support structure, the guidewire and catheter can be advanced through the tissue phantom toward two color sensors for tool detection. Furthermore, eliminating foam under the tissue phantom prevents foam fragments from interfering with tool detection.

Fig. 3.

Fig. 3

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Custom tissue phantom before (left) and after (right) silicone pouring

Subcutaneous Tool Detection Module Design

After the guidewire and catheter are inserted through the custom tissue phantom, they enter the subcutaneous tool detection module. Directly beneath the tissue, a 3D-printed funnel redirects inserted CVC tools towards a narrow channel where they are detected. This funnel is 3D-printed to avoid interfering with the magnetic tracker and to reduce manufacturing time and cost. The inner contact surface of the funnel is printed parallel to the build plate to minimize roughness and ensure smooth insertion of the guidewire and catheter. Similarly, the funnel is angled at 20° from the horizontal to ensure successful guidewire redirection. An angle of 25° or less between the funnel and an inserted needle has been shown to prevent the guidewire from buckling against the funnel [39]. Thus, if the needle is inserted between the 30–45° recommended for CVC [40], a funnel angle of 20° ensures successful redirection of CVC tools.

After being inserted and advanced through the custom tissue phantom, the guidewire and catheter are detected using two Adafruit (New York, NY) TCS34725 color sensors. These two sensors are held in place by a 3D-printed mount that also holds the Arduino Uno, an Adafruit TCA9548A I2C multiplexer for wiring multiple color sensors with the same I2C address, and an Ethernet breakout to connect the syringe and Arduino Uno. The first color sensor detects whether the guidewire and catheter have been successfully inserted and advanced in the correct order during the simulated CVC procedure. Moreover, this sensor is placed such that it detects a catheter at approximately 14 cm, the recommended catheter depth for IJV CVC [41]. The second sensor, placed 6 cm from the first sensor, detects whether the guidewire has been inserted too far into the body. This distance corresponds to the typical depth at which a guidewire would trigger an arrhythmia during a real IJV CVC procedure [9]. If the second sensor detects the guidewire, an arrhythmia is displayed on a simulated electrocardiogram.

Experimental Methods

Experiments were conducted for each of the three subsystems to evaluate the performance and fidelity of the CVC insertion training system. In Experiment 1, the sensorized syringe was fixed to an ATI Industrial Automation (Apex, NC) Gamma IP65 six-axis force transducer using a 3D-printed mount (Fig. 4). The plunger was manually drawn three times to measure the aspiration force required to pull the plunger 20 mm at a rate consistent with standard clinical aspiration. These measurements were repeated for a standard CVC syringe, whose needle tip was plugged with rubber to simulate the increased resistance felt while aspirating during insertion caused by vacuum pressure within the syringe [42]. In all experiments utilizing the force transducer, forces were recorded continuously at 10,000 Hz. This provided a preliminary engineering characterization for the spring strength that was in the syringe. Unlike previous designs this syringe allows for varying spring diameters to be placed inside to easily simulate differing forces.

Fig. 4.

Fig. 4

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Experimental setup for measuring the aspiration force of the sensorized syringe and a standard CVC syringe

In Experiment 2, the force response of the custom tissue phantom during compression by a US probe was measured and compared with that of Simulex US-compatible tissue used in Blue Phantom training models. Both tissues were fixed to the force transducer used in Experiment 1 with 3D-printed mounts (Fig. 5). A 3D-printed US probe end effector was mounted to a Dunkermotoren (Bonndorf, Germany) linear actuator and advanced 10 mm into each tissue at a constant velocity of 100 mm/s. This provides a constant speed for repeatable measurements within the range of human physician movements that does not create excessive force due to tissue acceleration. This end effector features a 20 x 50 mm flat contact surface, consistent with standard US probes used in CVC procedures [43]. This provided a preliminary engineering characterization for the tissue strength during compression to demonstrate general agreement with current Simulex mannikin phantom.

Fig. 5.

Fig. 5

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Experimental setup for measuring the force response of the custom and Simulex tissue phantoms during ultrasound compression and needle insertion

Experiment 3 measured the axial needle insertion force into the custom and Simulex tissue phantoms. Detailed in Fig. 5, both tissues were secured to the force transducer and a standard CVC syringe with an 18-gauge introducer needle that was fixed to the linear actuator using a 3D-printed mount. The needle was advanced 15 mm into each tissue at a constant velocity of 100 mm/s. This provided a preliminary engineering characterization for the needle insertion force compared to the Simulex mannikin.

Experiment 4 was designed to demonstrate the feasibility of using color sensors to detect subcutaneous guidewires and catheters. An Adafruit TCS34725 color sensor was mounted on eight 3D-printed funnels of different colors (red, orange, yellow, green, blue, purple, black, white) to determine if a particular funnel color produces a clear separation of sensor readings for the empty funnel, guidewire, and catheter. The sensor’s clear channel was used to read each object because it captures the total light intensity and provides stable readings in variable lighting conditions. Additionally, a 101 ms integration time and 4x gain were used to amplify sensor readings in the low-light tool detection channel. Before reading the guidewire and catheter, ten empty funnel readings were averaged to establish a baseline value. For each funnel color, fifty measurements of the guidewire and catheter were taken and normalized by dividing the raw reading by the baseline. These readings provide a statistically significant result in the ability of each color to effectively distinguish CVC tools that were inserted.

The catheter depth required for detection by the first color sensor varies with insertion location on the custom tissue. Thus, Experiment 5 was designed to understand at which insertion locations the catheter is correctly detected at the recommended 14 cm for CVC [41]. A standard two-lumen indwelling catheter was inserted at 35 points on the custom tissue surface arranged in a 5 x 7 grid with 16 mm spacing. Then, the catheter was advanced until it was detected by the first color sensor, and its depth was recorded to the nearest millimeter. Because the distance between the two sensors is fixed at 6 cm, only the distance to the first sensor was considered. If the catheter is correctly detected at 14 cm, this spacing ensures the guidewire triggers arrhythmia at 20 cm. This experiment provides calibration of the system based on the funnel geometry at varying locations to properly measure catheter insertion depth.

Statistical Analysis

For Experiment 4 the standard deviation and average signal was calculated for each of the eight different colored funnels (red, orange, yellow, green, blue, purple, black, white) in sensing the guidewire and catheter across n=50 samples for each condition. This allowed for the discrimination index (DI) to be calculated for each funnel color for empty versus guidewire, empty versus catheters, and guidewire versus catheter. This statistical analysis enabled determination of the sensor’s discrimination ability.

RESULTS AND DISCUSSION

Sensorized Syringe Results

Results from Experiment 1 characterize that the sensorized syringe can produce similar force feedback to an actual syringe during aspiration. The average peak aspiration forces across three trials were 7.65 N and 6.76 N for the sensorized syringe and standard CVC syringe, respectively (see Fig. 6). The force difference of only 13.1% highlights the similarity between the real and simulated aspiration forces. In contrast, the SOS required over 20 N to successfully aspirate [30], which is over three times greater than the real aspiration force. In addition, the SOS did not allow varying diameter springs to be utilized (spring outer diameter had to equal fixed syringe inner diameter), therefore limiting its ability for future variations. Providing realistic aspiration force feedback allows trainees to use the sensorized syringe to become more familiar with and practice proper one-hand aspiration techniques.

Fig. 6.

Fig. 6

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Aspiration force of standard CVC syringe and sensorized syringe during three trials

One limitation of the sensorized syringe design is that its force profile is fixed regardless of if it enters the simulated area of the vein or artery. In practice of actual CVC procedures, the user feels a sudden relaxation of the force upon entering the vein or artery as fluid is able to flow into the syringe [42]. Therefore, the sensorized syringe aspiration feeling is accurate up until the point of entering the vein or artery. To signal to the user when the needle has entered the vein or artery lights are used within the device (red light for artery, blue light for vein), thereby trading haptic feedback for visual feedback. Future work will look to incorporate dynamic aspiration forces into the sensorized syringe design to provide more realistic force feedback upon entering the vein or artery.

Custom Tissue Phantom Results

Experiment 2 results indicate that the custom and Simulex tissue phantoms had similar sigmoid-shaped force responses during US compression. As shown in Fig. 7, the force increased minimally during the first 3 mm of compression for both tissues. This initial compliance occurs as the viscoelastic tissues deform readily with the probe before the material fully engages and provides significant resistance to compression. Between 3–7 mm, the force increased more rapidly and exhibited nonlinear behavior. From 7–10 mm, however, the force increased linearly for both tissues. Differing by only 8.9%, the force gradually stabilized at 12.14 N for the custom tissue and 13.33 N for the Simulex tissue after the probe stopped moving.

Fig. 7.

Fig. 7

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Force response of custom and Simulex tissue phantoms under compression by an ultrasound probe

To compare the effective stiffnesses of each tissue phantom under US compression, linear trendlines were fit to the force response from 7 to 10 mm. The force response during this probe depth range exhibited strong linearity with R2 values of 0.9967 and 0.9954 for the custom and Simulex tissues, respectively. From the trendline slopes, the effective stiffness was estimated to be 1.54 N/mm for the custom tissue and 1.12 N/mm for the Simulex tissue. Although stiffer than the Simulex tissue, the custom tissue phantom may replicate the haptics of thicker, more compression-resistant skin. In the future, the tension of the nylon mesh within the custom tissue could be adjusted to provide haptic feedback during compression representative of diverse patient anatomies.

Over the full 10 mm of US compression, the compressive forces of both tissue phantoms were closely aligned, differing by a mean absolute error of only 0.14 N. This comparable force response, particularly the initial compliance of the custom tissue followed by increasing resistance, allows trainees to experience realistic haptic feedback when practicing vein identification with an US probe. Additionally, these results demonstrate that the custom tissue provides compression resistance comparable to the Simulex tissue despite lacking an underlying support structure.

The needle insertion forces measured in Experiment 3 for the custom and Simulex tissue phantoms are shown in Fig. 8. For both tissue phantoms, the needle insertion force increased steadily with depth due to increasing friction between the tissue and needle shaft. Maximum insertion forces occurred near the maximum insertion depth and measured 1.18 N for the custom tissue and 0.99 N for the Simulex tissue. The mean absolute error between the two force-needle depth curves was only 0.07 N, indicating a high-degree of similarity between the needle insertion forces for both tissues.

Fig. 8.

Fig. 8

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Force response during needle insertion into Simulex and custom tissue phantoms

Although the axial needle insertion forces for the two tissues were comparable, both differed from in-vivo measurements. Cadaveric CVC needle insertion revealed distinct sawtooth-shaped rises and falls in needle insertion force, which were absent in the custom tissue phantom [42]. Moreover, cadaveric CVC needle insertion produced a maximum force of 2.02 N, nearly twice the maximum force measured in the custom tissue. To address this limitation in the design it is proposed for future work to customize needle geometry to reduce insertion force, using a dulled needle. This will allow for increased insertion force and will provide added safety to the user.

Tool Detection Results

The results of Experiment 4 indicate that using a black funnel allows for distinct separation of normalized clear channel sensor readings for the guidewire, catheter, and empty funnel. Fig. 9 shows the mean normalized clear channel readings for each tool and funnel color. Standard deviations and average values are listed in Table 1 for the n=50 samples taken for each condition. For the black funnel, the mean normalized catheter reading was over 14.6 times the empty funnel baseline. The guidewire reading averaged 2.2, over twice the baseline value. To evaluate each of the 8 sensors ability to create distinguishable signals the discrimination index (DI) was calculated as shown in Table 2. The catheter signal compared to empty was more distinguishable than the guidewire signal compared empty for all sensors except the yellow and white sensor. The black sensor demonstrates the highest discrimination between empty and catheter states (DI=83.14) and between guidewire and catheter states (DI=55.79) which indicates exceptional sensitivity and robust signal separation.

Fig. 9.

Fig. 9

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Mean normalized clear channel readings for the guidewire, catheter, and empty funnel across eight different funnel colors. For each condition n=50.

Table 1:

Average and standard deviation of guidewire and catheter measurement for each sensor

Sensor Average Standard Deviation
Guidewire 1.091 0.0293
Catheter 3.211 0.0913
Guidewire 0.955 0.0089
Catheter 2.278 0.0540
Guidewire 0.830 0.0062
Catheter 1.402 0.0223
Guidewire 1.043 0.0224
Catheter 2.857 0.0456
Guidewire 1.154 0.0317
Catheter 3.744 0.0694
Guidewire 1.082 0.0257
Catheter 3.213 0.0720
Guidewire 0.732 0.0079
Catheter 1.019 0.0084
Guidewire 2.239 0.1562
Catheter 14.612 0.1637
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Table 2:

Discrimination Index (DI) between measured states for each sensor

Sensor Discrimination Index
Empty vs Guidewire Empty vs Catheter Guidewire vs Catheter
Red Sensor 3.12 24.21 21.85
Orange Sensor 5.1 23.66 24.15
Yellow Sensor 27.39 18.03 24.81
Green Sensor 1.92 40.73 35.55
Blue Sensor 4.87 39.53 32.67
Purple Sensor 3.2 30.74 27.97
White Sensor 33.9 2.26 25.52
Black Sensor 7.93 83.14 55.79
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The differences in detection ability and strong differentiation with the black funnel indicate a sensitivity to lighting and the background the sensor sees when there is no tool present. When there are no tools present the sensor light still lights the area and shines in front of the sensor. The black sensor would prevent the most outside light from entering thereby providing a more consistent reading of no tools. The values read for empty black funnel starkly differ from the yellow catheter and silver guidewire colors. This distinct variation in colors translates to the high DI values for the black sensor.

Experiment 5 results revealed the insertion positions on the custom tissue surface that allowed catheters to be detected at the recommended 14 cm for CVC placement. The catheter detection depth, defined as the minimum catheter depth required for detection by the first color sensor, is visualized in Fig. 10. This contour plot, generated by interpolating the measured catheter detection depths, includes a dashed red line indicating the positions where a catheter is correctly detected at 14 cm. The white border surrounding the contour plot denotes regions where catheter insertion is blocked by the 3D-printed frame of the custom tissue.

Fig. 10.

Fig. 10

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Catheter detection depth at different points on the custom tissue surface

Although catheters inserted near the center of the custom tissue phantom were correctly detected at 14 cm, there was significant variation in detection depth near the top and bottom edge of the tissue. Catheters were first detected at approximately 10 cm near the top of the custom tissue and at depths exceeding 17 cm near the bottom. Because of this variation, the vein location on the simulated US display must correspond with a position along the dashed red line on the custom tissue to ensure accurate catheter detection at 14 cm. One limitation of this sensing method is that the first color sensor cannot detect if a catheter was inserted too deeply at over 17 cm. To address this limitation, the first 3 cm of the catheter could be colored differently from the rest of the catheter body. This modification would ensure that the catheter is only detected at depths between 14 and 17 cm. Overall, this tool detection system can determine if a catheter has been inserted to the proper depth across different insertion sites. In addition, this system allows trainees to use the entire custom tissue surface to practice needle tip tracking, vein identification, and needle insertion.

CONCLUSIONS

This study presented the design of a novel central venous catheterization (CVC) insertion training system that enabled measurement on aspiration, guidewire depth, and catheter depth. Residents can also use the system to practice needle tip tracking, vein identification, needle insertion, and full CVC placement with realistic haptics and visual feedback. The sensorized syringe is robust, detects aspiration, simulates aspiration forces, and provides visual indications of arterial and venous flash. A subcutaneous 3D-printed funnel allows for tool insertion at multiple positions across the custom tissue surface. In addition, the optical tool detection subsystem reliably differentiates inserted guidewires from catheters, and provides automated, objective feedback on tool positioning during simulated CVC insertion. Overall, these results demonstrate the feasibility of a sensorized insertion training system capable of delivering quantitative measurement on subcutaneous tool positioning. Although designed for CVC training, this system may inform the design of new training devices for peripheral inserted central catheter (PICC) placement, arterial line insertion, or other procedures where accurate subcutaneous tool tracking is essential to procedural success.

FUNDING DATA

Research reported in this publication was partially supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number R01HL127316. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

CONFLICT OF INTEREST

Coauthor Dr. Moore owns equity in Medulate, which may have future interest in this project. Company ownership has been reviewed by the Pennsylvania State University’s Individual Conflict of Interest Committee and is currently being managed by the University.

Contributor Information

William Worrall, Pennsylvania State University, 204A Patterson Hall, University Park, PA 16802.

Jason Z. Moore, Pennsylvania State University, 318 Leonhard Building, University Park, PA 16802

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