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Three-dimensional (3D) segmentation, a process involving digitally marking anatomical structures on cross-sectional images such as computed tomography (CT), and 3D printing (3DP) are being increasingly utilized in medical education. Exposure to this technology within medical schools and hospitals remains limited in the United Kingdom. M3dicube UK, a national medical student, and junior doctor-led 3DP interest group piloted a 3D image segmentation workshop to gauge the impact of incorporating 3D segmentation technology on anatomical education. The workshop, piloted with medical students and doctors within the United Kingdom between September 2020 and 2021, introduced participants to 3D segmentation and offered practical experience segmenting anatomical models. Thirty-three participants were recruited, with 33 pre-workshop and 24 post-workshop surveys completed. Two-tailed t-tests were used to compare mean scores. From pre- to post-workshop, increases were noted in participants' confidence in interpreting CT scans (2.36 to 3.13, p = 0.010) and interacting with 3D printing technology (2.15 to 3.33, p = 0.00053), perceived utility of creating 3D models to aid image interpretation (4.18 to 4.45, p = 0.0027), improved anatomical understanding (4.2 to 4.7, p = 0.0018), and utility in medical education (4.45 to 4.79, p = 0.077). This pilot study provides early evidence of the utility of exposing medical students and healthcare professionals in the United Kingdom to 3D segmentation as part of their anatomical education, with additional benefit in imaging interpretation ability.

Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety. However, existing robotic ultrasound systems rely heavily on expensive commercial force sensors or the integrated sensors of commercial robotic arms, limiting their accessibility. To address these challenges, we developed a cost-effective, lightweight, 3D-printed force sensor and a hybrid position–force control strategy tailored for robotic ultrasound scanning. The system integrates patient-to-robot registration, automated scanning path planning, and multi-sensor data fusion, allowing the robot to autonomously locate the patient, target the region of interest, and maintain optimal contact force during scanning. Validation was conducted using an ultrasound-compatible abdominal aortic aneurysm (AAA) phantom created from patient CT data and healthy volunteer testing. For the volunteer testing, during a 1-min scan, 65% of the forces were within the good image range. Both volunteers reported no discomfort or pain during the whole procedure. These results demonstrate the potential of the system to provide safe, precise, and autonomous robotic ultrasound imaging in real-world conditions. [ABSTRACT FROM AUTHOR]

Background: Penile implant surgery is the standard surgical treatment for end-stage erectile dysfunction. However, the growing complexity of modern high-tech penile prostheses has increased the demand for more practical training opportunities. The most advanced contemporary training methods involve simulation training using cadavers, with costs exceeding $5,000 per cadaver, inclusive of biohazard fees. This study introduces an innovative and cost-efficient male urogenital simulator designed to enhance penile implant surgery training. Methods: Utilizing image segmentation of patient pre-operative computed tomography (CT) scans, combined with three-dimensional (3D) printing and silicone molding techniques, we developed a high-fidelity simulator replicating the anatomical structures of the male urogenital system. The simulator incorporates an innovative double-layer structural design encompassing the corpus spongiosum and glans, corpora cavernosa, testes, epididymides, and pelvic bones. Additionally, it utilizes a two-stage skin manufacturing process tailored for different skin regions. The simulator was produced at a low material cost of £10, with an average production time of 3 h. To evaluate its training efficacy, we conducted a penile implant surgery training session involving 15 urology trainees and surgeons ranging from specialty training levels ST3 to ST6. The session began with a demonstration of penile implant surgery and error detection. Trainees, averaging three per simulator, practiced corporotomy, dilation, measurement, penile prosthesis, and scrotal pump placement under expert guidance. Participants' feedback was collected using a Likert scale questionnaire, assessing learning, satisfaction, and anatomical accuracy. Results: Quantitative analysis of the questionnaire responses indicated highly positive feedback from the participants. Satisfaction rates surpassed 96% in learning effectiveness, over 89% in overall satisfaction, and 86% in anatomical accuracy demonstration. The simulator was favourably reviewed by both urology trainees and experienced surgeons, highlighting its utility as a practical training tool. Its low production cost and high precision make it a viable alternative to current training models. Conclusions: The development of this cost-efficient, anatomically accurate urogenital simulator through advanced imaging and additive manufacturing techniques represents a significant advancement in penile implant surgical training. This state-of-the-art simulator not only provides a realistic and practical training experience but also underscores the potential for 3D printing technologies to revolutionize medical education and training. [ABSTRACT FROM AUTHOR]

Cardiac catheterization for congenital heart disease (CHD) performed under fluoroscopic guidance still lacks definition and requires exposure to ionizing radiation and contrast agents, with most patients needing multiple procedures through their lifetime, leading to cumulative radiation risks. While fusion overlay techniques have been employed in the past to aid, these have been limited to a single plane, while interventions are traditionally performed under biplane fluoroscopy. We describe our initial experience performing cardiac catheterizations guided by an enhanced biplane GuideCCI system© (Siemens Healthcare, Germany) augmented by 3D magnetic resonance imaging and computed tomography modeling. Twenty-one children and young adults with CHD undergoing catheterization procedures between October 2019 and May 2021 were chosen based on their degree of complexity of cardiac anatomy. 3D stereolithography models were generated, overlayed, and displayed in real time, alongside angiographs in both planes on the screen during these procedures. We report successful implementation of this novel technology for performance of 26 interventions including stent placements, balloon dilations, vessel occlusion and percutaneous valve and transvenous pacemaker implantation all in patients with various complex cardiac anatomies. A statistically significant reduction in radiation and contrast use was noted for coarctation of the aorta stent angioplasty and transcatheter pulmonary valve replacement when compared with national benchmarks and local institutional metrics (with and without single plane overlay). No complications were encountered with the use of this technology. Use of a tracheal registration technique provided very good correlation in most cases. Operators preferred using biplane augmented catheterization over traditional fluoroscopy in patients with complex cardiac anatomy undergoing interventions. [ABSTRACT FROM AUTHOR]

Featured Application: A guide to construct and operate an experimental cardiac ablation simulator for interventional electrophysiology training. Cardiac ablation (CA) is an interventional electrophysiological procedure used to disrupt arrhythmic substrates in the myocardium by inducing localized scarring. Current CA training relies on the master–apprentice model. In different fields of medicine including CA, virtual and physical simulators have proven to enhance, and even outperform, conventional training modalities while providing a risk-free learning environment. Despite the benefits, high costs and operational difficulties limit the widespread use of interventional simulators. Our previous research introduced a low-cost CA simulator using a 3D-printed biatrial cardiac model, successfully recording ten ablation lesions on the phantom myocardium. In this work, we present and evaluate an enhanced version: compared to the previous version, the cardiac phantom's electrical behavior and ablation settings were optimized to produce consistent lesions, while 3D-printed components improved the haptic and radiographic properties of the simulator. Seven cardiologists compared the experimental simulator's performance to the leading commercial system from Heartroid in a 24-question survey on a 5-point Likert scale. The four following areas of fidelity were considered: catheter entry, anatomical correctness, radiographic appearance, and mapping and ablation. The experimental simulator significantly outperformed the commercial system (p < 0.01), particularly in radiographic appearance (p < 0.01). The results show the potential for the experimental simulator in routine CA training. [ABSTRACT FROM AUTHOR]

Atrial fibrillation, the most prevalent cardiac arrhythmia, is treated by catheter ablation to isolate electrical triggers. Clinical trials on robotic catheter systems hold promise for improving the safety and efficacy of the procedure. However, expense and proprietary designs hinder accessibility to such systems. This paper details an open-source, modular, three-degree-of-freedom robotic platform for teleoperating commercial ablation catheters through joystick navigation. We also demonstrate a catheter-agnostic handle interface permitting customization with commercial catheters. Collaborating clinicians performed benchtop targeting trials, comparing manual and robotic catheter navigation performance. The robot reduced task duration by 1.59 s across participants and five trials. Validation through mean motion jerk analysis revealed 35.2% smoother robotic navigation for experts (≥10 years experience) compared to the intermediate group. Yet, both groups achieved smoother robot motion relative to the manual approach, with the experts and intermediates exhibiting 42.2% and 13.6% improvements, respectively. These results highlight the potential of this system for enhancing catheter-based procedures. The source code and designs of CardioXplorer have been made publicly available to lower boundaries and drive innovations that enhance procedure efficacy beyond human capabilities. [ABSTRACT FROM AUTHOR]

Background: Autonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment. Objective: To determine from recent literature, through a systematic review, the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous navigation of catheters and guidewires for endovascular interventions. Methods: PubMed and IEEEXplore databases were searched to identify reports of AI applied to autonomous navigation methods in endovascular interventional surgery. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). PROSPERO: CRD42023392259. Results: Four hundred and sixty-two studies fulfilled the search criteria, of which 14 studies were included for analysis. Reinforcement learning (RL) (9/14, 64%) and learning from expert demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. These studies evaluated models on physical phantoms (10/14, 71%) and in-silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while non-anatomical vessel platforms "idealized" for simple navigation were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalizability were present across studies. No procedures were performed on patients in any of the studies reviewed. Moreover, all studies were limited due to the lack of patient selection criteria, reference standards, and reproducibility, which resulted in a low level of evidence for clinical translation. Conclusion: Despite the potential benefits of AI applied to autonomous navigation of endovascular interventions, the field is in an experimental proof-of-concept stage, with a technology readiness level of 3.We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come. Systematic review registration: identifier: CRD42023392259. [ABSTRACT FROM AUTHOR]

Conventional anatomical models and cadaveric specimens can be time-consuming and resource intensive for any anatomical institute. In recent years, there has been a push for more flexible and varied approaches to teaching, including problem-based and computer-aided learning, which includes web-based anatomical models or the use of three-dimensional visualization technology. With advances in hardware, autostereoscopic (AS) 3D screens have become more affordable, portable, and accessible to individuals, not just institutes. At King's College London (KCL), we developed the Virtual Anatomy and Histology (VAH) platform—an online resource which focuses on perspective volumetric 3D viewing of medical scan data and 3D models to facilitate the online teaching and learning of anatomy. This paper presents the features of VAH and details a study that was conducted in 2022, to evaluate the VAH 3D AS viewer configured with The Looking Glass Portrait (TLG) (Looking Glass, New York, NY, USA) 8-inch AS display. We tested the hypothesis that using an AS display can improve spatial understanding of cardiovascular anatomy. A cardiovascular 3D textured model was used from our gallery to carry out a spatial test. Twenty current healthcare students at King's participated in the study and completed a structured questionnaire. Results showed that 47.6% and 52.4% of participants agreed and strongly agreed, respectively, that identifying anatomical structures was easier in 3D compared to 2D. Qualitative feedback was positive as most students found King's VAH and TLG display "useful for people who need help with spatial understanding" and that "it was a good tool to test your anatomical knowledge". In conclusion, based on the quantitative results and feedback, we are optimistic that King's VAH and portable AS displays can be beneficial in anatomy education. With the increasing availability of such systems and competitive pricing, this technology is likely to have a significant impact in education in coming years. [ABSTRACT FROM AUTHOR]