Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation

Nwoye, E. O.; Woo, Wai Lok; Gao, Bin; Anyanwu, Tobenna

doi:10.1109/rbme.2022.3183852

Cited by 13 publications

(6 citation statements)

References 119 publications

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“…Preliminary experiences with AI in robotic surgery have already been presented, including the adoption of CNN for segmenting lung, bladder, and breast cancer types from imaging, as well as navigation assistance in endoscopic pancreatic and biliary procedures [21].…”

Section: Discussionmentioning

confidence: 99%

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy

Checcucci,

Piazzolla,

Marullo

et al. 2023

JCM

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Background: Addressing intraoperative bleeding remains a significant challenge in the field of robotic surgery. This research endeavors to pioneer a groundbreaking solution utilizing convolutional neural networks (CNNs). The objective is to establish a system capable of forecasting instances of intraoperative bleeding during robot-assisted radical prostatectomy (RARP) and promptly notify the surgeon about bleeding risks. Methods: To achieve this, a multi-task learning (MTL) CNN was introduced, leveraging a modified version of the U-Net architecture. The aim was to categorize video input as either “absence of blood accumulation” (0) or “presence of blood accumulation” (1). To facilitate seamless interaction with the neural networks, the Bleeding Artificial Intelligence-based Detector (BLAIR) software was created using the Python Keras API and built upon the PyQT framework. A subsequent clinical assessment of BLAIR’s efficacy was performed, comparing its bleeding identification performance against that of a urologist. Various perioperative variables were also gathered. For optimal MTL-CNN training parameterization, a multi-task loss function was adopted to enhance the accuracy of event detection by taking advantage of surgical tools’ semantic segmentation. Additionally, the Multiple Correspondence Analysis (MCA) approach was employed to assess software performance. Results: The MTL-CNN demonstrated a remarkable event recognition accuracy of 90.63%. When evaluating BLAIR’s predictive ability and its capacity to pre-warn surgeons of potential bleeding incidents, the density plot highlighted a striking similarity between BLAIR and human assessments. In fact, BLAIR exhibited a faster response. Notably, the MCA analysis revealed no discernible distinction between the software and human performance in accurately identifying instances of bleeding. Conclusion: The BLAIR software proved its competence by achieving over 90% accuracy in predicting bleeding events during RARP. This accomplishment underscores the potential of AI to assist surgeons during interventions. This study exemplifies the positive impact AI applications can have on surgical procedures.

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Section: Discussionmentioning

confidence: 99%

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy

Checcucci,

Piazzolla,

Marullo

et al. 2023

JCM

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“…[173,174] AI-aided SRS surpasses traditional and robot-only surgeries, offering a safer and more efficient surgical system with fewer or no complications. [175] The extensive application of AI is expected to bolster human performance. [176,177] Given the complexity and risks associated with surgical procedures, including human participants in AI-aided SRS studies has been rigorously regulated, slowing the advancement pace.…”

Section: Clinical Impactmentioning

confidence: 99%

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Liu,

Wu,

Sang

et al. 2024

Advanced Intelligent Systems

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Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots.

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“…A noticeable trend in recent developments revolves around the competitive drive to enhance surgical procedures through the integration of AI. Robotic technologies, in particular, have garnered significant attention among researchers and AI inventors, reflecting a concentrated effort to revolutionize surgical practices (Nwoye et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

First deployment of artificial intelligence recommendations in orthopedic surgery

Al Zoubi,

Kashanian,

Beaule

et al. 2024

Front. Artif. Intell.

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Scant research has delved into the non-clinical facets of artificial intelligence (AI), concentrating on leveraging data to enhance the efficiency of healthcare systems and operating rooms. Notably, there is a gap in the literature regarding the implementation and outcomes of AI solutions. The absence of published results demonstrating the practical application and effectiveness of AI in domains beyond clinical settings, particularly in the field of surgery, served as the impetus for our undertaking in this area. Within the realm of non-clinical strategies aimed at enhancing operating room efficiency, we characterize OR efficiency as the capacity to successfully perform four uncomplicated arthroplasty surgeries within an 8-h timeframe. This Community Case Study addresses this gap by presenting the results of incorporating AI recommendations at our clinical institute on 228 patient arthroplasty surgeries. The implementation of a prescriptive analytics system (PAS), utilizing supervised machine learning techniques, led to a significant improvement in the overall efficiency of the operating room, increasing it from 39 to 93%. This noteworthy achievement highlights the impact of AI in optimizing surgery workflows.

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Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation

Cited by 13 publications

References 119 publications

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

First deployment of artificial intelligence recommendations in orthopedic surgery

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