Artificial intelligence and simulation in urology

A systematic review of simulation training for lower extremity bypass procedures

OBJECTIVES

Simulation is used across surgical specialties for skill enhancement. The choice and assessment method of a simulator varies across literature. In the age of endovascular approach, trainees have limited exposure to open lower limb bypass procedures which needs attention. This review aims to assess the utility of simulation training in lower limb bypass surgery using Kirkpatrick's model.

METHODS

Using PRISMA statement, we included all the studies done on simulators in lower limb bypass surgical procedures for this systematic review. The primary outcome was to assess the effectiveness of different types of simulation used for lower limb bypass surgery using the Kirkpatrick's model for training evaluation.

RESULTS

An initial search identified 295 articles out of which 7 articles were found to be eligible for this systematic review. A variety of simulators were used including cadavers and synthetic models. Most studies (n=5) found the use of simulation as an effective tool in achieving technical competence. All the five studies we found at level 2 on Kirpatrick's model evaluation.

CONCLUSION

Most of the existing studies are at level 2 of Kirkpatrick's model which reflects learning changes in trainees after simulation. Feedback mechanism needs to be evolved where the improvement after simulation training can be gauged by its replication in clinical practice and improved patient care practices corresponding to the highest level of Kirkpatrick's model.

Current status of robotic training during the urology residency: results from a national survey in Spain

INTRODUCTION AND OBJECTIVES

The increasing number of robotic urological procedures observed in recent years highlights the need to expand training opportunities in robotic surgery. Our objective is to investigate the state of robotic training during urology residency in Spain in order to identify significant deficiencies.

MATERIALS AND METHODS

A 20-item online survey was conducted among urology residents in Spain who were registered in the database of the Residents and Young Urologists Group of the Spanish Association of Urology. The survey assessed subjective opinions, institutional aspects, training resources, and experience regarding robotic surgery. A total of 455 email invitations were sent throughout the year 2021. Descriptive analysis of the responses was performed.

RESULTS

The participation rate reached 30%, with a total of 135 residents. 52% of respondents lacked access to a robotic system in their institution, of which only 48% could compensate for this deficiency through external rotations. Among those with access to a robotic system, 25% and 23% reported having access to theoretical and practical training, respectively. The existence of a formal training program was low (13%). 85% of the respondents considered robotic surgery training in Spain to be deficient.

CONCLUSIONS

Training for Spanish residents in robotic urological surgery is perceived as inadequate, emphasizing the crucial need for improvement in training programs in this field.

Treatment and Staging Intensification Strategies Associated with Radical Prostatectomy for High-Risk Prostate Cancer: Efficacy Evaluation and Exploration of Novel Approaches

The management of high-risk prostate cancer (PCa) presents a significant clinical challenge, often necessitating treatment intensification due to the potential presence of micrometastases. While radical prostatectomy (RP) constitutes one of the primary treatment modalities, the integration of neoadjuvant and adjuvant therapies suggests a paradigm shift towards more aggressive treatment strategies, also guided by new imaging modalities like positron emission tomography using prostate-specific membrane antigen (PSMA-PET). Despite the benefits, treatment intensification raises concerns regarding increased side effects. This review synthesizes the latest evidence on perioperative treatment intensification and de-escalation for high-risk localized and locally advanced PCa patients eligible for surgery. Through a non-systematic literature review conducted via PubMed, Scopus, Web of Science, and ClinicalTrials.gov, we explored various dimensions of perioperative treatments, including neoadjuvant systemic therapies, adjuvant therapies, and the role of novel diagnostic technologies. Emerging evidence provides more support for neoadjuvant systemic therapies. Preliminary results from studies suggest the potential for treatments traditionally reserved for metastatic PCa to show apparent benefit in a non-metastatic setting. The role of adjuvant treatments remains debated, particularly the use of androgen deprivation therapy (ADT) and adjuvant radiotherapy in patients at higher risk of biochemical recurrence. The potential role of radio-guided PSMA lymph node dissection emerges as a cutting-edge approach, offering a targeted method for eradicating disease with greater precision. Innovations such as artificial intelligence and machine learning are potential game-changers, offering new avenues for personalized treatment and improved prognostication. The intensification of surgical treatment in high-risk PCa patients is a dynamic and evolving field, underscored by the integration of traditional and novel therapeutic approaches. As evidence continues to emerge, these strategies will refine patient selection, enhance treatment efficacy, and mitigate the risk of progression, although with an attentive consideration of the associated side effects.

Quality of information about urologic pathology in English and Spanish from ChatGPT, BARD, and Copilot

INTRODUCTION AND OBJECTIVE

Generative artificial intelligence makes it possible to ask about medical pathologies in dialog boxes. Our objective was to analyze the quality of information about the most common urological pathologies provided by ChatGPT (OpenIA), BARD (Google), and Copilot (Microsoft).

METHODS

We analyzed information on the following pathologies and their treatments as provided by AI: prostate cancer, kidney cancer, bladder cancer, urinary lithiasis, and benign prostatic hypertrophy (BPH). Questions in English and Spanish were posed in dialog boxes; the answers were collected and analyzed with DISCERN questionnaires and the overall appropriateness of the response. Surgical procedures were performed with an informed consent questionnaire.

RESULTS

The responses from the three chatbots explained the pathology, detailed risk factors, and described treatments. The difference is that BARD and Copilot provide external information citations, which ChatGPT does not. The highest DISCERN scores, in absolute numbers, were obtained in Copilot; however, on the appropriacy scale it was noted that their responses were not the most appropriate. The best surgical treatment scores were obtained by BARD, followed by ChatGPT, and finally Copilot.

CONCLUSIONS

The answers obtained from generative AI on urological diseases depended on the formulation of the question. The information provided had significant biases, depending on pathology, language, and above all, the dialog box consulted.

Transfer of acquired practical skills from dry lab into live surgery using the avatera robotic system: An experimental study

OBJECTIVE

To evaluate the transfer of the practical skills of robot-assisted surgery acquired in the dry-lab into a real live experimental setting for performing upper and lower urinary tract surgeries.

MATERIAL AND METHODS

An in vivo experimental study design was utilized. Six urology trainees and fellows; two 2nd year trainees with no previous exposure to laparoscopic surgery (Group 1), two 4th year residents with medium exposure to laparoscopic surgery (Group 2) and two fellows trained to perform laparoscopic surgeries (Group 3) performed ureteral reimplantation into the bladder, pyeloplasty, and radical nephrectomy on three female pigs under general anesthesia. Prior to performing the requested procedures, each participant completed 10-14 h dry-lab robotic training acquiring skills in basic surgical tasks, such as suturing, cutting and needle passage. The recorded variables were the successful completion of the procedures, the console time, and the time to perform different steps and major complications.

RESULTS

All procedures were completed successfully by all groups except the pyeloplasty by group 1 which was complicated by bleeding from the renal vein, and the procedure was abandoned. Group 3 achieved shorter console time for all successfully completed procedures and for separate surgical steps compared to all groups, followed by Group 2. The slowest group for all procedures and steps analyzed was Group 3.

CONCLUSIONS

Although further clinical evidence is needed, the robotic-assisted urological procedures and the most challenging steps could be performed safely and effectively after proper training in the dry lab under mentor supervision according to our study.

Three-dimensional automatic artificial intelligence driven augmented-reality selective biopsy during nerve-sparing robot-assisted radical prostatectomy: A feasibility and accuracy study

Objective

To evaluate the accuracy of our new three-dimensional (3D) automatic augmented reality (AAR) system guided by artificial intelligence in the identification of tumour's location at the level of the preserved neurovascular bundle (NVB) at the end of the extirpative phase of nerve-sparing robot-assisted radical prostatectomy.

Methods

In this prospective study, we enrolled patients with prostate cancer (clinical stages cT1c-3, cN0, and cM0) with a positive index lesion at target biopsy, suspicious for capsular contact or extracapsular extension at preoperative multiparametric magnetic resonance imaging. Patients underwent robot-assisted radical prostatectomy at San Luigi Gonzaga Hospital (Orbassano, Turin, Italy), from December 2020 to December 2021. At the end of extirpative phase, thanks to our new AAR artificial intelligence driven system, the virtual prostate 3D model allowed to identify the tumour's location at the level of the preserved NVB and to perform a selective excisional biopsy, sparing the remaining portion of the bundle. Perioperative and postoperative data were evaluated, especially focusing on the positive surgical margin (PSM) rates, potency, continence recovery, and biochemical recurrence.

Results

Thirty-four patients were enrolled. In 15 (44.1%) cases, the target lesion was in contact with the prostatic capsule at multiparametric magnetic resonance imaging (Wheeler grade L2) while in 19 (55.9%) cases extracapsular extension was detected (Wheeler grade L3). 3D AAR guided biopsies were negative in all pathological tumour stage 2 (pT2) patients while they revealed the presence of cancer in 14 cases in the pT3 cohort (14/16; 87.5%). PSM rates were 0% and 7.1% in the pathological stages pT2 and pT3 (<3 mm, Gleason score 3), respectively.

Conclusion

With the proposed 3D AAR system, it is possible to correctly identify the lesion's location on the NVB in 87.5% of pT3 patients and perform a 3D-guided tailored nerve-sparing even in locally advanced diseases, without compromising the oncological safety in terms of PSM rates.

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7-46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.