A. Fassari, V. De Blasi, Benedetto Ielpo, A. Anselmo, Bernardo Dalla Valle, Edoardo Rosso
Liver parenchymal transection is a challenging step during hepatic resection, particularly when using robotic platforms that require specific skills to optimize this phase. Pedicle division at the beginning of the liver parenchyma helps to better identify the resection plane and minimizes blood loss. The three-dimensional (3D) high-definition vision and the robotic Maryland allow for clear identification of the hepatic pedicles that could be dissected or divided without the need for a laparoscopic ultrasonic dissector. The caudo-peripheral technique, combined with the Maryland bipolar Kelly clamp crushing technique, is a useful approach to complete parenchymal transection and achieve safe anatomical resections in cases of hepatocellular carcinoma (HCC) with multi-pronged bleeding control. This is essential for expediting the procedure, reducing the number of intermittent clamping times, and minimizing the risk of ischemia-reperfusion injury. In this setting, perfect synchronization between the surgeon operating at the console and the bedside assistant is crucial. Advances in artificial intelligence (AI) systems have shown great potential to redefine clinical care management, preoperative planning, and intraoperative decision making for patients with HCC. This paper describes the most relevant details of our technique, its theoretical background, advantages, and limitations. Moreover, minimally invasive surgery offers the opportunity to share surgical experiences and technical progress through multimedia videos. This represents a modern and effective teaching tool to accelerate the learning process and overcome the challenges of the most complex procedures by offering surgeons various solutions to common technical problems.
{"title":"Robotic caudo-peripheral approach for liver parenchymal transection in anatomical liver resections for hepatocellular carcinoma","authors":"A. Fassari, V. De Blasi, Benedetto Ielpo, A. Anselmo, Bernardo Dalla Valle, Edoardo Rosso","doi":"10.20517/ais.2024.21","DOIUrl":"https://doi.org/10.20517/ais.2024.21","url":null,"abstract":"Liver parenchymal transection is a challenging step during hepatic resection, particularly when using robotic platforms that require specific skills to optimize this phase. Pedicle division at the beginning of the liver parenchyma helps to better identify the resection plane and minimizes blood loss. The three-dimensional (3D) high-definition vision and the robotic Maryland allow for clear identification of the hepatic pedicles that could be dissected or divided without the need for a laparoscopic ultrasonic dissector. The caudo-peripheral technique, combined with the Maryland bipolar Kelly clamp crushing technique, is a useful approach to complete parenchymal transection and achieve safe anatomical resections in cases of hepatocellular carcinoma (HCC) with multi-pronged bleeding control. This is essential for expediting the procedure, reducing the number of intermittent clamping times, and minimizing the risk of ischemia-reperfusion injury. In this setting, perfect synchronization between the surgeon operating at the console and the bedside assistant is crucial. Advances in artificial intelligence (AI) systems have shown great potential to redefine clinical care management, preoperative planning, and intraoperative decision making for patients with HCC. This paper describes the most relevant details of our technique, its theoretical background, advantages, and limitations. Moreover, minimally invasive surgery offers the opportunity to share surgical experiences and technical progress through multimedia videos. This represents a modern and effective teaching tool to accelerate the learning process and overcome the challenges of the most complex procedures by offering surgeons various solutions to common technical problems.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hao Ding, Lalithkumar Seenivasan, Benjamin Killeen, Sue Min Cho, Mathias Unberath
Surgical data science is devoted to enhancing the quality, safety, and efficacy of interventional healthcare. While the use of powerful machine learning algorithms is becoming the standard approach for surgical data science, the underlying end-to-end task models directly infer high-level concepts (e.g., surgical phase or skill) from low-level observations (e.g., endoscopic video). This end-to-end nature of contemporary approaches makes the models vulnerable to non-causal relationships in the data and requires the re-development of all components if new surgical data science tasks are to be solved. The digital twin (DT) paradigm, an approach to building and maintaining computational representations of real-world scenarios, offers a framework for separating low-level processing from high-level inference. In surgical data science, the DT paradigm would allow for the development of generalist surgical data science approaches on top of the universal DT representation, deferring DT model building to low-level computer vision algorithms. In this latter effort of DT model creation, geometric scene understanding plays a central role in building and updating the digital model. In this work, we visit existing geometric representations, geometric scene understanding tasks, and successful applications for building primitive DT frameworks. Although the development of advanced methods is still hindered in surgical data science by the lack of annotations, the complexity and limited observability of the scene, emerging works on synthetic data generation, sim-to-real generalization, and foundation models offer new directions for overcoming these challenges and advancing the DT paradigm.
{"title":"Digital twins as a unifying framework for surgical data science: the enabling role of geometric scene understanding","authors":"Hao Ding, Lalithkumar Seenivasan, Benjamin Killeen, Sue Min Cho, Mathias Unberath","doi":"10.20517/ais.2024.16","DOIUrl":"https://doi.org/10.20517/ais.2024.16","url":null,"abstract":"Surgical data science is devoted to enhancing the quality, safety, and efficacy of interventional healthcare. While the use of powerful machine learning algorithms is becoming the standard approach for surgical data science, the underlying end-to-end task models directly infer high-level concepts (e.g., surgical phase or skill) from low-level observations (e.g., endoscopic video). This end-to-end nature of contemporary approaches makes the models vulnerable to non-causal relationships in the data and requires the re-development of all components if new surgical data science tasks are to be solved. The digital twin (DT) paradigm, an approach to building and maintaining computational representations of real-world scenarios, offers a framework for separating low-level processing from high-level inference. In surgical data science, the DT paradigm would allow for the development of generalist surgical data science approaches on top of the universal DT representation, deferring DT model building to low-level computer vision algorithms. In this latter effort of DT model creation, geometric scene understanding plays a central role in building and updating the digital model. In this work, we visit existing geometric representations, geometric scene understanding tasks, and successful applications for building primitive DT frameworks. Although the development of advanced methods is still hindered in surgical data science by the lack of annotations, the complexity and limited observability of the scene, emerging works on synthetic data generation, sim-to-real generalization, and foundation models offer new directions for overcoming these challenges and advancing the DT paradigm.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The health technology assessment in the artificial intelligence era: the AI surgical department","authors":"Valentina Bellini, Matteo Panizzi, E. Bignami","doi":"10.20517/ais.2024.10","DOIUrl":"https://doi.org/10.20517/ais.2024.10","url":null,"abstract":"","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Mezzina, Jasper Hofman, J. Simoens, Alexandre Mottrie, P. Backer
{"title":"The 1st Orsi Innotech Surgical AI Day congress report","authors":"Marco Mezzina, Jasper Hofman, J. Simoens, Alexandre Mottrie, P. Backer","doi":"10.20517/ais.2024.06","DOIUrl":"https://doi.org/10.20517/ais.2024.06","url":null,"abstract":"","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Mezzina, Jasper Hofman, J. Simoens, Alexandre Mottrie, P. Backer
{"title":"The 1st Orsi Innotech Surgical AI Day congress report","authors":"Marco Mezzina, Jasper Hofman, J. Simoens, Alexandre Mottrie, P. Backer","doi":"10.20517/ais.2024.06","DOIUrl":"https://doi.org/10.20517/ais.2024.06","url":null,"abstract":"","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The problem with patient avatars and emotions","authors":"Andrew A. Gumbs, Sonia Roubeni, S. V. Grasso","doi":"10.20517/ais.2024.01","DOIUrl":"https://doi.org/10.20517/ais.2024.01","url":null,"abstract":"","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139607489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aishwarya Boini, Sara Acciuffi, Roland Croner, Alfredo Illanes, Luca Milone, Bruce Turner, ANDREW GUMBS
This is a scoping review of artificial intelligence (AI) in flexible endoscopy (FE), encompassing both computer vision (CV) and autonomous actions (AA). While significant progress has been made in AI and FE, particularly in polyp detection and malignancy prediction, resulting in several available market products, these achievements only scratch the surface potential of AI in flexible endoscopy. Many doctors still do not fully grasp that contemporary robotic FE systems, which operate the endoscope through telemanipulation, represent the most basic autonomy level, specifically categorized as level 1. Although these console systems allow remote control, they lack the more sophisticated forms of autonomy. This manuscript aims to review the current examples of AI applications in FE and hopefully act as a stimulus for more advanced AA in FE.
这是一篇关于人工智能（AI）在柔性内窥镜（FE）中应用的综述，包括计算机视觉（CV）和自主行动（AA）。虽然人工智能和 FE 取得了重大进展，尤其是在息肉检测和恶性肿瘤预测方面，并由此产生了一些市场产品，但这些成就仅仅触及了人工智能在柔性内窥镜检查中的表面潜力。许多医生仍然没有充分认识到，通过远程操纵操作内窥镜的当代机器人 FE 系统代表了最基本的自主水平，具体归类为一级。虽然这些控制台系统允许远程控制，但缺乏更复杂的自主形式。本手稿旨在回顾当前人工智能在 FE 领域的应用实例，希望能对 FE 领域更先进的 AA 技术起到推动作用。
{"title":"Scoping review: autonomous endoscopic navigation","authors":"Aishwarya Boini, Sara Acciuffi, Roland Croner, Alfredo Illanes, Luca Milone, Bruce Turner, ANDREW GUMBS","doi":"10.20517/ais.2023.36","DOIUrl":"https://doi.org/10.20517/ais.2023.36","url":null,"abstract":"This is a scoping review of artificial intelligence (AI) in flexible endoscopy (FE), encompassing both computer vision (CV) and autonomous actions (AA). While significant progress has been made in AI and FE, particularly in polyp detection and malignancy prediction, resulting in several available market products, these achievements only scratch the surface potential of AI in flexible endoscopy. Many doctors still do not fully grasp that contemporary robotic FE systems, which operate the endoscope through telemanipulation, represent the most basic autonomy level, specifically categorized as level 1. Although these console systems allow remote control, they lack the more sophisticated forms of autonomy. This manuscript aims to review the current examples of AI applications in FE and hopefully act as a stimulus for more advanced AA in FE.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rising in incidence, hepatobiliary and pancreatic (HPB) cancers continue to exhibit dismal long-term survival. The overall poor prognosis of HPB cancers is reflective of the advanced stage at which most patients are diagnosed. Late diagnosis is driven by the often-asymptomatic nature of these diseases, as well as a dearth of screening modalities. Additionally, standard imaging modalities fall short of providing accurate and detailed information regarding specific tumor characteristics, which can better inform surgical planning and sequencing of systemic therapy. Therefore, precise therapeutic planning must be delayed until histopathological examination is performed at the time of resection. Given the current shortcomings in the management of HPB cancers, investigations of numerous noninvasive biomarkers, including circulating tumor cells and DNA, proteomics, immunolomics, and radiomics, are underway. Radiomics encompasses the extraction and analysis of quantitative imaging features. Along with summarizing the general framework of radiomics, this review synthesizes the state of radiomics in HPB cancers, outlining its role in various aspects of management, present limitations, and future applications for clinical integration. Current literature underscores the utility of radiomics in early detection, tumor characterization, therapeutic selection, and prognostication for HPB cancers. Seeing as single-center, small studies constitute the majority of radiomics literature, there is considerable heterogeneity with respect to steps of the radiomics workflow such as segmentation, or delineation of the region of interest on a scan. Nonetheless, the introduction of the radiomics quality score (RQS) demonstrates a step towards greater standardization and reproducibility in the young field of radiomics. Altogether, in the setting of continually improving artificial intelligence algorithms, radiomics represents a promising biomarker avenue for promoting enhanced and tailored management of HPB cancers, with the potential to improve long-term outcomes for patients.
{"title":"Current state of radiomics in hepatobiliary and pancreatic malignancies","authors":"Mahip Grewal, Taha Ahmed, Ammar Asrar Javed","doi":"10.20517/ais.2023.28","DOIUrl":"https://doi.org/10.20517/ais.2023.28","url":null,"abstract":"Rising in incidence, hepatobiliary and pancreatic (HPB) cancers continue to exhibit dismal long-term survival. The overall poor prognosis of HPB cancers is reflective of the advanced stage at which most patients are diagnosed. Late diagnosis is driven by the often-asymptomatic nature of these diseases, as well as a dearth of screening modalities. Additionally, standard imaging modalities fall short of providing accurate and detailed information regarding specific tumor characteristics, which can better inform surgical planning and sequencing of systemic therapy. Therefore, precise therapeutic planning must be delayed until histopathological examination is performed at the time of resection. Given the current shortcomings in the management of HPB cancers, investigations of numerous noninvasive biomarkers, including circulating tumor cells and DNA, proteomics, immunolomics, and radiomics, are underway. Radiomics encompasses the extraction and analysis of quantitative imaging features. Along with summarizing the general framework of radiomics, this review synthesizes the state of radiomics in HPB cancers, outlining its role in various aspects of management, present limitations, and future applications for clinical integration. Current literature underscores the utility of radiomics in early detection, tumor characterization, therapeutic selection, and prognostication for HPB cancers. Seeing as single-center, small studies constitute the majority of radiomics literature, there is considerable heterogeneity with respect to steps of the radiomics workflow such as segmentation, or delineation of the region of interest on a scan. Nonetheless, the introduction of the radiomics quality score (RQS) demonstrates a step towards greater standardization and reproducibility in the young field of radiomics. Altogether, in the setting of continually improving artificial intelligence algorithms, radiomics represents a promising biomarker avenue for promoting enhanced and tailored management of HPB cancers, with the potential to improve long-term outcomes for patients.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139222323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pancreatic cancer, namely pancreatic ductal adenocarcinoma (PDAC), is an intractable cancer with a 5-year survival of around 7%-10%. Surgery with adjuvant chemotherapy remains the mainstay of curative treatment. The pancreas is a retroperitoneal organ that lies close to major arterial and venous structures, and it is the involvement of these structures that currently technically limits surgical resection with curative intent for pancreatic cancer. It is possible to resect venous and arterial structures involved in cancer to expand options for patients for whom surgery was previously deemed infeasible, but this is best performed in high-volume pancreatic surgery centres. Here, we explore the role that 3D visualisation and navigation surgery have in improving preoperative planning and operative execution, the role they may play in training and education and in enabling the development of novel surgical techniques in pancreatic surgery.
{"title":"Can 3D visualisation and navigation techniques improve pancreatic surgery? A systematic review","authors":"Martyn Stott, Ambareen Kausar","doi":"10.20517/ais.2022.42","DOIUrl":"https://doi.org/10.20517/ais.2022.42","url":null,"abstract":"Pancreatic cancer, namely pancreatic ductal adenocarcinoma (PDAC), is an intractable cancer with a 5-year survival of around 7%-10%. Surgery with adjuvant chemotherapy remains the mainstay of curative treatment. The pancreas is a retroperitoneal organ that lies close to major arterial and venous structures, and it is the involvement of these structures that currently technically limits surgical resection with curative intent for pancreatic cancer. It is possible to resect venous and arterial structures involved in cancer to expand options for patients for whom surgery was previously deemed infeasible, but this is best performed in high-volume pancreatic surgery centres. Here, we explore the role that 3D visualisation and navigation surgery have in improving preoperative planning and operative execution, the role they may play in training and education and in enabling the development of novel surgical techniques in pancreatic surgery.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135219616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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