Artificial intelligence surgery最新文献_第5页

Artificial intelligence in hepatFIGopancreaticobiliary surgery: a systematic review 人工智能在肝胆胰手术中的应用综述

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2022.20

M. Bektaş, B. Zonderhuis, H. Marquering, Jaime Costa Pereira, G. Burchell, D. L. van der Peet

{"title":"Artificial intelligence in hepatFIGopancreaticobiliary surgery: a systematic review","authors":"M. Bektaş, B. Zonderhuis, H. Marquering, Jaime Costa Pereira, G. Burchell, D. L. van der Peet","doi":"10.20517/ais.2022.20","DOIUrl":"https://doi.org/10.20517/ais.2022.20","url":null,"abstract":"Aim: The aim of this systematic review was to provide an overview of Machine Learning applications within hepatopancreaticobiliary surgery. The secondary aim was to evaluate the predictive performances of applied Machine Learning models. Methods: A systematic search was conducted in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only eligible for inclusion when they described Machine Learning in hepatopancreaticobiliary surgery. The Cochrane and PROBAST risk of bias tools were used to evaluate the quality of studies and included Machine Learning models. Results: Out of 1821 articles, 52 studies have met the inclusion criteria. The majority of Machine Learning models were developed to predict the course of disease, and postoperative complications. The course of disease has been predicted with accuracies up to 99%, and postoperative complications with accuracies up to 89%. Most studies had a retrospective study design, in which external validation was absent for Machine Learning models. Conclusion: Machine learning models have shown promising accuracies in the prediction of short-term and long-term surgical outcomes after hepatopancreaticobiliary surgery. External validation of Machine Learning models is required to facilitate the clinical introduction of Machine Learning.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656220","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: 1

Artificial intelligence for decision support in surgical oncology - a systematic review 人工智能在外科肿瘤学决策支持中的应用综述

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2022.21

M. Wagner, A. Schulze, Michael Haselbeck-Köbler, P. Probst, Johanna M. Brandenburg, E. Kalkum, A. Majlesara, A. Ramouz, R. Klotz, Felix Nickel, K. März, S. Bodenstedt, M. Dugas, L. Maier-Hein, A. Mehrabi, S. Speidel, M. Büchler, B. Müller-Stich

{"title":"Artificial intelligence for decision support in surgical oncology - a systematic review","authors":"M. Wagner, A. Schulze, Michael Haselbeck-Köbler, P. Probst, Johanna M. Brandenburg, E. Kalkum, A. Majlesara, A. Ramouz, R. Klotz, Felix Nickel, K. März, S. Bodenstedt, M. Dugas, L. Maier-Hein, A. Mehrabi, S. Speidel, M. Büchler, B. Müller-Stich","doi":"10.20517/ais.2022.21","DOIUrl":"https://doi.org/10.20517/ais.2022.21","url":null,"abstract":"Aim: We systematically review current clinical applications of artificial intelligence (AI) that use machine learning (ML) methods for decision support in surgical oncology with an emphasis on clinical translation. Methods: MEDLINE, Web of Science, and CENTRAL were searched on 19 January 2021 for a combination of AI and ML-related terms, decision support, and surgical procedures for abdominal malignancies. Data extraction included study characteristics, description of algorithms and their respective purpose, and description of key steps for scientific validation and clinical translation. Results: Out of 8302 articles, 107 studies were included for full-text analysis. Most of the studies were conducted in a retrospective setting (n = 105, 98%), with 45 studies (42%) using data from multiple centers. The most common tumor entities were colorectal cancer (n = 35, 33%), liver cancer (n = 21, 20%), and gastric cancer (n = 17, 16%). The most common prediction task was survival (n = 36, 34%), with artificial neural networks being the most common class of ML algorithms (n = 52, 49%). Key reporting and validation steps included, among others, a complete listing of patient features (n = 95, 89%), training of multiple algorithms (n = 73, 68%), external validation (n = 13, 12%), prospective validation (n = 2, 2%), robustness in terms of cross-validation or resampling (n = 89, 83%), treatment recommendations by ML algorithms (n = 9, 8%), and development of an interface (n = 12, 11%). Conclusion: ML for decision support in surgical oncology is receiving increasing attention with promising results, but robust and prospective clinical validation is mostly lacking. Furthermore, the integration of ML into AI applications is necessary to foster clinical translation.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656277","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: 6

A nationwide survey on the perceptions of general surgeons on artificial intelligence 一项关于普通外科医生对人工智能看法的全国性调查

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2021.10

F. Voskens, J. Abbing, A. T. Ruys, J. Ruurda, I. Broeders

{"title":"A nationwide survey on the perceptions of general surgeons on artificial intelligence","authors":"F. Voskens, J. Abbing, A. T. Ruys, J. Ruurda, I. Broeders","doi":"10.20517/ais.2021.10","DOIUrl":"https://doi.org/10.20517/ais.2021.10","url":null,"abstract":"Aim: Artificial intelligence (AI) has the potential to improve perioperative diagnosis and decision making. Despite promising study results, the majority of AI platforms in surgery currently remain in the research setting. Understanding the current knowledge and general attitude of surgeons toward AI applications in their surgical practice is essential and can contribute to the future development and uptake of AI in surgery. Methods: In March 2021, a web-based survey was conducted among members of the Dutch Association of Surgery. The survey measured opinions on the existing knowledge, expectations, and concerns on AI among surgical residents and surgeons. Results: A total of 313 respondents completed the survey. Overall, 85% of the respondents agreed that AI could be of value in the surgical field and 61% expected AI to improve their diagnostic ability. The outpatient clinic (35.8%) and operating room (39.6%) were stated as area of interest for the use of AI. Statistically, surgeons working in an academic hospital were more likely to be aware of the possibilities of AI (P = 0.01). The surgeons in this survey were not worried about job replacement, however they raised the greatest concerns on accountability issues (50.5%), loss of autonomy (46.6%), and risk of bias (43.5%). Conclusion: This survey demonstrates that the majority of the surgeons show a positive and open attitude towards AI. Although various ethical issues and concerns arise, the expectations regarding the implementation of future surgical AI applications are high.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656072","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: 5

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward 医学图像分析的计算机视觉和机器学习:最新进展、挑战和前进方向

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2021.15

Eyad Elyan, Pattaramon Vuttipittayamongkol, Pamela Johnston, Kyle Martin, Kyle McPherson, C. Moreno-García, Chrisina Jayne, Md. Mostafa Kamal Sarker

{"title":"Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward","authors":"Eyad Elyan, Pattaramon Vuttipittayamongkol, Pamela Johnston, Kyle Martin, Kyle McPherson, C. Moreno-García, Chrisina Jayne, Md. Mostafa Kamal Sarker","doi":"10.20517/ais.2021.15","DOIUrl":"https://doi.org/10.20517/ais.2021.15","url":null,"abstract":"The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656148","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: 30

Artificial intelligence HPB surgery - current problems, future solutions? 人工智能HPB手术——当前的问题，未来的解决方案?

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2022.26

D. A. O’Reilly, H. Pitt

引用次数: 1

Training in robotic pancreatic surgery 机器人胰腺手术的培训

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2022.28

Sam Body, M. Kawka, T. Gall, Andrew A. Gumbs, Henry A. Pitt

{"title":"Training in robotic pancreatic surgery","authors":"Sam Body, M. Kawka, T. Gall, Andrew A. Gumbs, Henry A. Pitt","doi":"10.20517/ais.2022.28","DOIUrl":"https://doi.org/10.20517/ais.2022.28","url":null,"abstract":"The aim of this narrative review is to discuss current training for the robotic approach to pancreatic surgery and the potential use of machine learning to progress robotic surgical training. A literature search using PubMed and MEDLINE was conducted to investigate training programmes in robotic pancreatic surgery and advances in the use of artificial intelligence for training. The use of virtual reality can assist novice robotic surgeons in learning basic surgical skills. The use of automated video analytics can also improve surgical education to enable self-directed learning both within and outside the operating room. The emerging role and novel applications of machine learning in robotic surgery could shape future training by aiding the autonomous recognition of anatomical structures in the surgical field, instrument tracking and providing feedback on surgical competence. Training should be standardised to ensure the attainment of assessment benchmarks and include virtual simulation basic training in addition to procedural-specific training. Standardised procedural techniques should be used to improve patient safety, theatre efficiency and the continuation of robotic practice.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656374","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 use of pre-trained deep learning models for the photographic assessment of donor livers for transplantation 使用预先训练的深度学习模型对供肝移植进行摄影评估

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2022.06

H. Ugail, Aliyu Abubakar, Ali Elmahmudi, Colin Wilson, Brian Thomson

{"title":"The use of pre-trained deep learning models for the photographic assessment of donor livers for transplantation","authors":"H. Ugail, Aliyu Abubakar, Ali Elmahmudi, Colin Wilson, Brian Thomson","doi":"10.20517/ais.2022.06","DOIUrl":"https://doi.org/10.20517/ais.2022.06","url":null,"abstract":"Aim: Hepatic steatosis is a recognised major risk factor for primary graft failure in liver transplantation. In general, the global fat burden is measured by the surgeon using a visual assessment. However, this can be augmented by a histological assessment, although there is often inter-observer variation in this regard as well. In many situations the assessment of the liver relies heavily on the experience of the observer and more experienced surgeons will accept organs that more junior surgeons feel are unsuitable for transplantation. Often surgeons will err on the side of caution and not accept a liver for fear of exposing recipients to excessive risk of death. Methods: In this study, we present the use of deep learning for the non-invasive evaluation of donor liver organs. Transfer learning, using deep learning models such as the Visual Geometry Group (VGG) Face, VGG16, Residual Neural Network 50 (ResNet50), Dense Convolutional Network 121 (DenseNet121) and MobileNet are utilised for effective pattern extraction from partial and whole liver. Classification algorithms such as Support Vector Machines, k-Nearest Neighbour, Logistic Regression, Decision Tree and Linear Discriminant Analysis are then used for the final classification to identify between acceptable or non-acceptable donor liver organs. Results: The proposed method is distinct in that we make use of image information both from partial and whole liver. We show that common pre-trained deep learning models can be used to quantify the donor liver steatosis with an accuracy of over 92%. Conclusion: Machine learning algorithms offer the tantalising prospect of standardising the assessment and the possibility of using more donor organs for transplantation.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656527","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: 4

Artificial intelligence in hepatopancreaticobiliary surgery - promises and perils 人工智能在肝胆胰手术中的应用——希望与危险

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2022.32

Christina Boutros, Vivek Singh, L. Ocuin, J. Marks, Daniel A. Hashimoto

引用次数: 0

Why Artificial Intelligence Surgery (AIS) is better than current Robotic-Assisted Surgery (RAS) 为什么人工智能手术(AIS)比机器人辅助手术(RAS)更好?

Artificial intelligence surgery Pub Date : 2022-01-01 DOI: 10.20517/ais.2022.41

ANDREW GUMBS, B. Gayet

引用次数: 2

Automatic tissue segmentation of hyperspectral images in liver and head neck surgeries using machine learning 肝脏和头颈部手术中使用机器学习的高光谱图像自动组织分割

Artificial intelligence surgery Pub Date : 2021-08-31 DOI: 10.20517/ais.2021.05

Fernando Cervantes-Sanchez, M. Maktabi, H. Köhler, R. Sucher, N. Rayes, J. Aviña-Cervantes, I. Cruz-Aceves, C. Chalopin

引用次数: 9