Artificial intelligence surgery最新文献

Robotic caudo-peripheral approach for liver parenchymal transection in anatomical liver resections for hepatocellular carcinoma 在肝细胞癌解剖性肝脏切除术中采用机器人尾骨外周法横切肝实质

Artificial intelligence surgery Pub Date : 2024-07-10 DOI: 10.20517/ais.2024.21

A. Fassari, V. De Blasi, Benedetto Ielpo, A. Anselmo, Bernardo Dalla Valle, Edoardo Rosso

{"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}

引用次数: 0

Digital twins as a unifying framework for surgical data science: the enabling role of geometric scene understanding 数字孪生作为外科数据科学的统一框架：几何场景理解的促进作用

Artificial intelligence surgery Pub Date : 2024-07-05 DOI: 10.20517/ais.2024.16

Hao Ding, Lalithkumar Seenivasan, Benjamin Killeen, Sue Min Cho, Mathias Unberath

{"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}

引用次数: 0

The health technology assessment in the artificial intelligence era: the AI surgical department 人工智能时代的医疗技术评估：人工智能手术部

Artificial intelligence surgery Pub Date : 2024-03-14 DOI: 10.20517/ais.2024.10

Valentina Bellini, Matteo Panizzi, E. Bignami

引用次数: 0

The 1st Orsi Innotech Surgical AI Day congress report 第一届 Orsi Innotech 外科人工智能日大会报告

Artificial intelligence surgery Pub Date : 2024-02-06 DOI: 10.20517/ais.2024.06

Marco Mezzina, Jasper Hofman, J. Simoens, Alexandre Mottrie, P. Backer

引用次数: 0

The 1st Orsi Innotech Surgical AI Day congress report 第一届 Orsi Innotech 外科人工智能日大会报告

Artificial intelligence surgery Pub Date : 2024-02-06 DOI: 10.20517/ais.2024.06

Marco Mezzina, Jasper Hofman, J. Simoens, Alexandre Mottrie, P. Backer

引用次数: 0

The problem with patient avatars and emotions 病人化身和情感问题

Artificial intelligence surgery Pub Date : 2024-01-22 DOI: 10.20517/ais.2024.01

Andrew A. Gumbs, Sonia Roubeni, S. V. Grasso

引用次数: 0

The future implementation of artificial intelligence technology in esophageal surgery 人工智能技术在食道外科手术中的未来应用

Artificial intelligence surgery Pub Date : 2023-12-28 DOI: 10.20517/ais.2023.56

George Peek, Sharona B. Ross

引用次数: 0

Scoping review: autonomous endoscopic navigation 范围界定审查：自主内窥镜导航

Artificial intelligence surgery Pub Date : 2023-12-11 DOI: 10.20517/ais.2023.36

Aishwarya Boini, Sara Acciuffi, Roland Croner, Alfredo Illanes, Luca Milone, Bruce Turner, ANDREW GUMBS

引用次数: 0

Current state of radiomics in hepatobiliary and pancreatic malignancies 肝胆胰恶性肿瘤放射组学研究现状

Artificial intelligence surgery Pub Date : 2023-11-28 DOI: 10.20517/ais.2023.28

Mahip Grewal, Taha Ahmed, Ammar Asrar Javed

{"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}

引用次数: 0

Can 3D visualisation and navigation techniques improve pancreatic surgery? A systematic review 3D可视化和导航技术能改善胰腺手术吗?系统回顾

Artificial intelligence surgery Pub Date : 2023-10-24 DOI: 10.20517/ais.2022.42

Martyn Stott, Ambareen Kausar

引用次数: 0