M. Bektaş, B. Zonderhuis, H. Marquering, Jaime Costa Pereira, G. Burchell, D. L. van der Peet
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.
目的:本系统综述的目的是概述机器学习在肝胆胰手术中的应用。第二个目的是评估应用机器学习模型的预测性能。方法:系统检索PubMed、EMBASE、Cochrane和Web of Science。研究只有在描述肝胆胰手术中的机器学习时才有资格纳入。使用Cochrane和PROBAST偏倚风险工具评估研究质量，并纳入机器学习模型。结果:1821篇文献中，52篇符合纳入标准。大多数机器学习模型的开发是为了预测疾病的进程和术后并发症。预测病程的准确率高达99%，预测术后并发症的准确率高达89%。大多数研究采用回顾性研究设计，其中缺乏对机器学习模型的外部验证。结论:机器学习模型在预测肝胆胰手术后短期和长期手术结果方面显示出有希望的准确性。为了促进机器学习的临床应用，需要对机器学习模型进行外部验证。
{"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":"1 1","pages":""},"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}
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
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.
{"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":"41 1","pages":""},"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}
F. Voskens, J. Abbing, A. T. Ruys, J. Ruurda, I. Broeders
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.
{"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":"26 1","pages":""},"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}
Eyad Elyan, Pattaramon Vuttipittayamongkol, Pamela Johnston, Kyle Martin, Kyle McPherson, C. Moreno-García, Chrisina Jayne, Md. Mostafa Kamal Sarker
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.
{"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":"1 1","pages":""},"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}
{"title":"Artificial intelligence HPB surgery - current problems, future solutions?","authors":"D. A. O’Reilly, H. Pitt","doi":"10.20517/ais.2022.26","DOIUrl":"https://doi.org/10.20517/ais.2022.26","url":null,"abstract":"<jats:p>NO</jats:p>","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656360","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}
Sam Body, M. Kawka, T. Gall, Andrew A. Gumbs, Henry A. Pitt
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.
{"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":"81 1","pages":""},"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}
H. Ugail, Aliyu Abubakar, Ali Elmahmudi, Colin Wilson, Brian Thomson
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.
{"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":"1 1","pages":""},"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}
Christina Boutros, Vivek Singh, L. Ocuin, J. Marks, Daniel A. Hashimoto
Research and development in artificial intelligence (AI) has been experiencing a resurgence over the past decade. The rapid growth and evolution of AI approaches can leave one feeling overwhelmed and confused about how these technologies will impact hepatopancreaticobiliary (HPB) surgery, the obstacles to its clinical translation, and the role that HPB surgeons can play in accelerating AI’s development and ultimate clinical impact. This review outlines some of the basic terminology and current approaches in surgical AI, obstacles to further development and translation of AI, and how HPB surgeons can influence its future in surgery.
{"title":"Artificial intelligence in hepatopancreaticobiliary surgery - promises and perils","authors":"Christina Boutros, Vivek Singh, L. Ocuin, J. Marks, Daniel A. Hashimoto","doi":"10.20517/ais.2022.32","DOIUrl":"https://doi.org/10.20517/ais.2022.32","url":null,"abstract":"Research and development in artificial intelligence (AI) has been experiencing a resurgence over the past decade. The rapid growth and evolution of AI approaches can leave one feeling overwhelmed and confused about how these technologies will impact hepatopancreaticobiliary (HPB) surgery, the obstacles to its clinical translation, and the role that HPB surgeons can play in accelerating AI’s development and ultimate clinical impact. This review outlines some of the basic terminology and current approaches in surgical AI, obstacles to further development and translation of AI, and how HPB surgeons can influence its future in surgery.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656422","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":"Why Artificial Intelligence Surgery (AIS) is better than current Robotic-Assisted Surgery (RAS)","authors":"ANDREW GUMBS, B. Gayet","doi":"10.20517/ais.2022.41","DOIUrl":"https://doi.org/10.20517/ais.2022.41","url":null,"abstract":"","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656556","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}
Fernando Cervantes-Sanchez, M. Maktabi, H. Köhler, R. Sucher, N. Rayes, J. Aviña-Cervantes, I. Cruz-Aceves, C. Chalopin
{"title":"Automatic tissue segmentation of hyperspectral images in liver and head neck surgeries using machine learning","authors":"Fernando Cervantes-Sanchez, M. Maktabi, H. Köhler, R. Sucher, N. Rayes, J. Aviña-Cervantes, I. Cruz-Aceves, C. Chalopin","doi":"10.20517/ais.2021.05","DOIUrl":"https://doi.org/10.20517/ais.2021.05","url":null,"abstract":"","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42660199","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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