Harnessing Advanced Imaging Technologies in Robotic Urological Surgery Requires Specialized and Structured Training

In the past decades, health care has witnessed the groundbreaking introduction of new technologies, particularly in surgery. Historically, urology has been one of the pioneering fields at the forefront of these advancements [1].

Surgical advancements often follow a different pathway as compared with the structured, highly regulated process of introducing new drugs. In fact, the traditional four-phase model used in drug development is not directly applicable to surgical innovations. To address this gap, the Innovation, Development, Exploration, Assessment, and Long-term follow-up (IDEAL) framework was introduced to define and safeguard the various stages of surgical innovation, and to establish a robust scientific foundation throughout the process. Despite these structured efforts, from a pragmatic standpoint, the adoption of new technologies within a urology department remains a complex task, requiring systematic evaluation of emerging opportunities and careful consideration of risks, benefits, and costs. Yet, surgeons and health care institutions should be encouraged to embrace innovations to provide cutting-edge care for their patients [2].

Following this direction, leading scientific societies in Europe (European Association of Urology [EAU]) and America (American Urological Association) have dedicated sections and working groups specifically designed to drive the introduction of new technologies in clinical practice, such as the European Section of Urotechnology and the EAU Robotic Urology Section.

Training programs and pathways have been developed to adapt to the continuous evolution of robot-assisted surgery, reflecting a significant shift in surgical education methodologies. These changes underscore the importance of simulators, performance metrics, and comprehensive training curricula. A key advantage of simulation-based training is the proficiency-based progression (PBP) model, whereby surgical skill improvements are assessed quantitatively [3].

The EAU’s Standardization in Surgical Education initiative aims to promote the widespread adoption of evidence-based training programs across all urological subspecialties, ensuring consistency and high standards in surgical education [4].

To date, innovations in the surgical armamentarium are increasingly bolstered by significant advancements in complementary technologies. Notably, new imaging technologies have been developed to further enhance robotic surgery outcomes, with an emphasis on tailoring interventions within the framework of precision surgery. In particular, three-dimensional (3D) patient-specific models and augmented reality (AR) surgery have seen a widespread adoption [5]. While these technologies were initially developed before the coronavirus disease 2019 (COVID-19) pandemic, their ultimate added value in surgical planning and navigation has been demonstrated more recently, particularly in kidney and prostate cancer surgeries [[6], [7], [8], [9]].

However, a dedicated and standardized pathway for a specific training for the use of new imaging technologies is missing in the urological scenario.

Aiming to explore this unmet need, we developed a 59-item, online-based survey (Supplementary material) in accordance with the Checklist for Reporting Results of Internet E-Surveys, using the Google Form platform (https://docs.google.com/forms). The survey was spread in February 2024 to the participants (which include residents, young urologists <40 yr old, and senior urologists) of the 12th edition of the Techno Urology Meeting (TUM; Turin, from February 29 to Mar 1, 2024; http://www.technourologymeeting.com), and it was divided into four sections (Supplementary material). After the collection of baseline characteristics of the responders (section 1), the focus was on the evidence, clinical utility, and training in 3D and AR technologies (sections 2 and 3); lastly, training methods for the use of 3D models and AR during robotic urological surgery were explored (section 4). We reached 126 responders (on 303 participants at the 12th TUM; 41.6% of response rate) of whom 69.8% (88) reported to work at a university hospital and 23.8% (33) in a community hospital or private practice; 64.5% (80) and 40.3% (50) reported to use 3D models and AR technology in their clinical practice, respectively. In particular, 3D models were used for robotic partial nephrectomy (97.6% of responders) and radical prostatectomy (52.9%), especially for complex cases (98.8%), surgical planning (97.6%), and anticipation of surgical challenges (52.9%). Similar results were found regarding the use of AR technology, with a particular application toward solving intraoperative challenges (77.6% of responders) and improvement of intra- and postoperative outcomes (74.1%). Importantly, specific training for understanding 3D models and applying them in surgical practice was warranted by 88.7% of responders (110 answers), aiming to provide a potential impact or influence on surgical team’s performance (97.3%). Most responders reported that a multidisciplinary course would be preferred (71.7%), including hands-on activities (90.3%). Similarly, a dedicated course on the application of AR technologies was requested by 91.8% (113) of responders. Namely, 115 responders (92.1%) expressed their interest in “investing time and resources in learning AR technology,” prioritizing a course that focuses on the PBP methodology (Fig. 1, Table 1, and Supplementary Tables 1 and 2).

Even if affected by the bias of a selected population of responders, our data reproduce the current trend in the urological community, which has shown growing interest and adoption of these imaging tools. It was evident at the most recent EAU annual meeting in Paris, where 15 abstracts or videos from eight different countries showcased the use of 3D models and/or AR in urological surgery (source: https://urosource.uroweb.org/). Therefore, new technological tools are being introduced in the perioperative setting, enhancing both the preoperative and the intraoperative phase of surgical procedures.

Of note, mirroring the evolution of other fields including aviation and manufacturing, the introduction of new technologies requires specific training to ensure that personnel can effectively and safely utilize such innovations. For example, in aviation, training is essential when incorporating advanced technologies such as AR, virtual reality, and artificial intelligence (AI) into operations and maintenance. These tools have significantly enhanced the efficiency of training processes, but these require specialized instruction to ensure that technicians and pilots can adapt and fully exploit the capabilities of the new technology. In Formula 1, a similarly high-tech industry, the introduction of new vehicle technologies demands intense and specialized training for both engineers and drivers to ensure that the systems are used correctly under high-pressure conditions. This training is critical for maximizing performance while ensuring safety. Thus, specialized training is not just beneficial but also essential when integrating new technologies across safety-critical industries. Translating these principles in health care scenarios, one can realize that a technology-based training philosophy should be encouraged, going beyond disease-driven training programs [10]. This would be particularly important for the new generation of urologists.

Following these considerations, the PBP methodology should be applied rigorously in teaching the use and application of 3D models and AR for robotic urological surgery. A dedicated course could be designed based on the following principles:

• 1.

  E-learning modules followed by hands-on dry lab sessions, where urologists and bioengineers teach how to create a 3D model starting from two-dimensional computed tomography (CT) or magnetic resonance imaging (MRI) scans, should be encouraged.

• 2.

  The basis of segmentation process (the process in which the different anatomical areas are identified and countered on the CT or MRI images) and the use of a dedicated 3D mouse (with eight degrees of movements) should be evaluated with defined metrics to ensure a learning path based on the PBP methodology.

• 3.

  Dedicated exercises on prerecorded videos can be proposed to train the manual overlapping of AR images over the real anatomy during the surgical interventions.

• 4.

  Finally, wet lab modules on in vivo porcine models can be created with specifically designed exercises retracing the different phases of the intervention, in which AR images could be used for specific tasks such as the identification of the tumor lesion and its landmarks, renal pedicle dissection, and tumor resection.

The ideal setting could be a dedicated laboratory, equipped with multimedia classrooms for the e-learning and dry lab components. Additionally, dedicated operating rooms, in collaboration with veterinary colleagues, would enable the execution of exercises in the wet lab.

In conclusions, we believe that proper understanding of existing technologies by a urologist is and will increasingly be paramount for mastering these in the operating room and offer real clinical benefits to patients. The development of well-structured, multidisciplinary courses that equip medical professionals with basic engineering skills will help them understand the core principles behind these technological advancements. Likewise, hands-on training in AR will allow certified users to fully optimize its use in the operating room.



Author contributions: Enrico Checcucci had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.



Study concept and design: Checcucci, Amparore, Puliatti, Campi.

Acquisition of data: Volpi, Checcucci, Bignante.

Analysis and interpretation of data: None.

Drafting of the manuscript: : Checcucci, Amparore.

Critical revision of the manuscript for important intellectual content: Campi, Puliatti.

Statistical analysis: None.

Obtaining funding: None.

Administrative, technical, or material support: None.

Supervision: Porpiglia.

Other: None.



Financial disclosures: Enrico Checcucci certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None.



Funding/Support and role of the sponsor: None.



Acknowledgments: We would like to thank Andrea Bellin, Loic Baekelandt, and Matteo Olimpo for their support. Thanks to the support of FPRC 5X1000 Ministero della Salute 2021 - EMAGEN (RETURN Trial) and Ricerca Corrente 2024-2025.