Artificial intelligence and simulation in urology

Rivas, Juan Gómez; Vázquez, Camacho; Ruiz, Cristina Ballesteros; Taratkin, Mark; Marenco, José Luis; Cacciamani, Giovanni; Checcucci, Enrico; Okhunov, Zhamshid; Enikeev, Dmitry; Esperto, Francesco; Grossmann, Rafael J.; Somani, B.; Veneziano, Domenico

doi:10.1016/j.acuroe.2021.07.001

Cited by 7 publications

(6 citation statements)

References 34 publications

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“…AI has been used successfully to measure surgical efficiency and improve skills [ 34 , 35 , 36 , 37 , 37 , 38 ]. Specifically, CNNs use feature extraction to provide objective feedback for trainees on robotic skills such as suturing, knot tying, and needle passing with accuracy in the 90s [ 39 ], and entropy-based models achieves similar levels of success in the automated scoring of suturing and knot-tying [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

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

Eppler

Sayegh

Maas

et al. 2023

JCM

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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.

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Section: Discussionmentioning

confidence: 99%

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

Eppler

Sayegh

Maas

et al. 2023

JCM

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“…Currently, it has had its main impact in the field of surgery; since it is accompanied by robotic surgery, it has become a training tool for urology residents, contributing significantly to improving the results obtained with actual patients. Gómez et al (2021) found that implementing this tool improves students' ability to make complex decisions and understand complex surgeries and improves results, efficiency, and performance in a urological surgical process [35].…”

Section: Chatbots and Urologic Educationmentioning

confidence: 99%

Implications of using conversational robots (chatbots) in uro-oncology

Nieva-Posso,

García-Perdomo

2024

CUAJ

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Chatbots, or conversational robots, have become a strategy or support tool for urologic patient care, diagnostic communication, and treatment. With regard to patients, studies have shown that chatbots can answer medical questions with similar percentages of acceptability as urologists. In addition, they can contribute to patient education, allowing them to ask questions that do not arise during medical consultation. They have also proven to be good tools for health promotion and disease prevention. These benefits can also serve doctors, as robots can support medical consultation and the reading of medical records, making patient care more efficient; however, there are several limitations, including the accuracy of bot-generated answers and the acceptability that urologists give to this type of tool.

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“…The major benefit of such webinars is the availability of high-level content to anyone with internet access [11]. But now that this technology has been successfully implemented, it is time to incorporate new elements in the online learning structure and training of the EAU [12,13,14], such as mobile apps, podcasts, virtual and augmented reality [15], 3D printing [16], remote live surgery [17] big data and artificial intelligence [18] among others.…”

Section: Analysis Of the Impact Of The Covid-19 Pandemicmentioning

confidence: 99%

A new era and future of education: the impact of pandemic on online learning – a study from the European School of Urology

2023

Cent European J Urol.

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Artificial intelligence and simulation in urology

Cited by 7 publications

References 34 publications

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

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

Implications of using conversational robots (chatbots) in uro-oncology

A new era and future of education: the impact of pandemic on online learning – a study from the European School of Urology

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