G. Capelli, Daunia Verdi, I. Frigerio, Niki Rashidian, Antonella Ficorilli, Vincent Grasso, D. Majidi, ANDREW GUMBS, G. Spolverato
This white paper documents the consensus opinion of the Artificial Intelligence Surgery (AIS) task force on Artificial Intelligence (AI) Ethics and the AIS Editorial Board Study Group on Ethics on the ethical considerations and current trustworthiness of artificial intelligence and autonomous actions in surgery. The ethics were divided into 6 topics defined by the Task Force: Reliability of robotic and AI systems; Respect for privacy and sensitive data; Use of complete and representative (i.e., unbiased) data; Transparencies and uncertainties in AI; Fairness: are we exacerbating inequalities in access to healthcare?; Technology as an equalizer in surgical education. Task Force members were asked to research a topic, draft a section, and come up with several potential consensus statements. These were voted on by members of the Task Force and the Study Group, and all proposals that received > 75 % agreement were adopted and included in the White Paper.
{"title":"White paper: ethics and trustworthiness of artificial intelligence in clinical surgery","authors":"G. Capelli, Daunia Verdi, I. Frigerio, Niki Rashidian, Antonella Ficorilli, Vincent Grasso, D. Majidi, ANDREW GUMBS, G. Spolverato","doi":"10.20517/ais.2023.04","DOIUrl":"https://doi.org/10.20517/ais.2023.04","url":null,"abstract":"This white paper documents the consensus opinion of the Artificial Intelligence Surgery (AIS) task force on Artificial Intelligence (AI) Ethics and the AIS Editorial Board Study Group on Ethics on the ethical considerations and current trustworthiness of artificial intelligence and autonomous actions in surgery. The ethics were divided into 6 topics defined by the Task Force: Reliability of robotic and AI systems; Respect for privacy and sensitive data; Use of complete and representative (i.e., unbiased) data; Transparencies and uncertainties in AI; Fairness: are we exacerbating inequalities in access to healthcare?; Technology as an equalizer in surgical education. Task Force members were asked to research a topic, draft a section, and come up with several potential consensus statements. These were voted on by members of the Task Force and the Study Group, and all proposals that received > 75 % agreement were adopted and included in the White Paper.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656623","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}
Machine Learning (ML) relates to the use of computer-derived algorithms and systems to enhance knowledge in order to facilitate decision making. In surgery, ML has the potential to shape clinical decision making and the management of postoperative complications in three ways: (a) by using the predicted probability of postoperative complications or survival to determine and guide optimal treatment; (b) by identifying anomalous data and patterns representing high-risk physiological states during the perioperative period and taking measures to minimise the impact of the existing risks; (c) to facilitate post-hoc identification of physiological trends, phenotypic patient characteristics, morphological characteristics of diseases, and human factors that may help alert surgeons to relevant risk factors in future patients. The accuracy, validity and integrity of data that are input into ML predictive models are central to its future success. ML could reduce errors by drawing attention to known risks of complications through supervised learning, and gain greater insights by identifying previously under-appreciated aspects of care through unsupervised learning. The success of ML in enhancing patient care will be determined by the human potential to incorporate data science techniques into daily clinical practice.
{"title":"Machine learning for prediction of postoperative complications after hepato-biliary and pancreatic surgery","authors":"I. Shapey, Mustafa Sultan","doi":"10.20517/ais.2022.31","DOIUrl":"https://doi.org/10.20517/ais.2022.31","url":null,"abstract":"Machine Learning (ML) relates to the use of computer-derived algorithms and systems to enhance knowledge in order to facilitate decision making. In surgery, ML has the potential to shape clinical decision making and the management of postoperative complications in three ways: (a) by using the predicted probability of postoperative complications or survival to determine and guide optimal treatment; (b) by identifying anomalous data and patterns representing high-risk physiological states during the perioperative period and taking measures to minimise the impact of the existing risks; (c) to facilitate post-hoc identification of physiological trends, phenotypic patient characteristics, morphological characteristics of diseases, and human factors that may help alert surgeons to relevant risk factors in future patients. The accuracy, validity and integrity of data that are input into ML predictive models are central to its future success. ML could reduce errors by drawing attention to known risks of complications through supervised learning, and gain greater insights by identifying previously under-appreciated aspects of care through unsupervised learning. The success of ML in enhancing patient care will be determined by the human potential to incorporate data science techniques into daily clinical practice.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656407","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}
Pub Date : 2023-01-01Epub Date: 2023-03-20DOI: 10.20517/ais.2022.38
Daniela R Tovar, Michael H Rosenthal, Anirban Maitra, Eugene J Koay
Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.
{"title":"Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer.","authors":"Daniela R Tovar, Michael H Rosenthal, Anirban Maitra, Eugene J Koay","doi":"10.20517/ais.2022.38","DOIUrl":"10.20517/ais.2022.38","url":null,"abstract":"<p><p>Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.</p>","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9391929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kieran G. McGivern, T. Drake, S. Knight, J. Lucocq, M. Bernabeu, Neil Clark, C. Fairfield, R. Pius, Catherine A Shaw, S. Seth, E. Harrison, M. Prof.Ewen, Harrison, H. Pitt, ANDREW GUMBS
Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets. Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication). Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using “big data” to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning. Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights.
{"title":"Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review","authors":"Kieran G. McGivern, T. Drake, S. Knight, J. Lucocq, M. Bernabeu, Neil Clark, C. Fairfield, R. Pius, Catherine A Shaw, S. Seth, E. Harrison, M. Prof.Ewen, Harrison, H. Pitt, ANDREW GUMBS","doi":"10.20517/ais.2022.39","DOIUrl":"https://doi.org/10.20517/ais.2022.39","url":null,"abstract":"Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets. Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication). Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using “big data” to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning. Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656543","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}
benefits of minimally invasive surgery and equivalent oncological outcomes compared with conventional open PD (OPD). When LPD and RPD are compared, RPD offers better precision with 3D vision and advanced instrumentation. Although the learning curve for RPD is long with a longer operating time compared with OPD, this can be reduced to a duration similar to that for OPD through standardization of techniques and case numbers.Perioperative outcomes such as length of stay, blood loss, and transfusion requirement are significantly improved compared to OPD and fewer cases require conversion to open than LPD. In this article, we describe our approach to RPD through standardizing PD techniques along with tips and tricks for the benefit of surgeons interested in learning robotic pancreatic surgery.
{"title":"Robotic Pancreatoduodenectomy - how I do it: tips, tricks and pitfalls to standardize the technique to reduce postoperative morbidity and mortality","authors":"L. Jiao, R. Vellaisamy, T. Gall","doi":"10.20517/ais.2023.03","DOIUrl":"https://doi.org/10.20517/ais.2023.03","url":null,"abstract":"benefits of minimally invasive surgery and equivalent oncological outcomes compared with conventional open PD (OPD). When LPD and RPD are compared, RPD offers better precision with 3D vision and advanced instrumentation. Although the learning curve for RPD is long with a longer operating time compared with OPD, this can be reduced to a duration similar to that for OPD through standardization of techniques and case numbers.Perioperative outcomes such as length of stay, blood loss, and transfusion requirement are significantly improved compared to OPD and fewer cases require conversion to open than LPD. In this article, we describe our approach to RPD through standardizing PD techniques along with tips and tricks for the benefit of surgeons interested in learning robotic pancreatic surgery.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656609","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}
G. Capelli, Dajana Glavas, L. Ferrari, Daunia Verdi, G. Spolverato
Despite a growing number of women choosing to pursue surgical specialties, surgery is still perceived as a woman-unfriendly career. The difficulties of conciliating a demanding career with the requirements of both personal and family life for women surgeons have been investigated by several authors. The current study aims to summarize existing evidence on the issue of work-life balance for women surgeons, particularly focusing on possible strategies to improve it. Artificial intelligence (AI) has been investigated as a possible means to close the gender gap, acting as an equalizer for women surgeons. Female surgeons have been reported to be unmarried or to have married later in life at a higher rate than their male colleagues; many of them also choose not to have children or to have fewer and to have them later in life. These disparities are partly due to the issues connected to invisible work (e.g. household management), the difficulties of managing pregnancy during surgical residency, the challenges women face when returning to work following maternity leave, and the lack of a supportive environment. Flexible work schedules, implementation of childcare facilities, introduction and encouragement of paternity leave for surgeons, and enforcement of mentorship and sponsorship for female surgeons are some of the proposed solutions for building a fair and equitable work culture for all surgeons and overthrowing old, conventional ideas concerning gender roles. Moreover, technology has been advocated as a possible solution to gender discrimination in surgical departments; technology could facilitate an objective assessment of surgical performances and advanced training for surgeons unable to attend in-person education. A healthy, thriving, organized, supportive, and culturally transformed work environment could benefit surgeon and staff productivity and ultimately improve patient care.
{"title":"Women surgeons fighting for work-life balance: how technology might help close the gender gap","authors":"G. Capelli, Dajana Glavas, L. Ferrari, Daunia Verdi, G. Spolverato","doi":"10.20517/ais.2022.40","DOIUrl":"https://doi.org/10.20517/ais.2022.40","url":null,"abstract":"Despite a growing number of women choosing to pursue surgical specialties, surgery is still perceived as a woman-unfriendly career. The difficulties of conciliating a demanding career with the requirements of both personal and family life for women surgeons have been investigated by several authors. The current study aims to summarize existing evidence on the issue of work-life balance for women surgeons, particularly focusing on possible strategies to improve it. Artificial intelligence (AI) has been investigated as a possible means to close the gender gap, acting as an equalizer for women surgeons. Female surgeons have been reported to be unmarried or to have married later in life at a higher rate than their male colleagues; many of them also choose not to have children or to have fewer and to have them later in life. These disparities are partly due to the issues connected to invisible work (e.g. household management), the difficulties of managing pregnancy during surgical residency, the challenges women face when returning to work following maternity leave, and the lack of a supportive environment. Flexible work schedules, implementation of childcare facilities, introduction and encouragement of paternity leave for surgeons, and enforcement of mentorship and sponsorship for female surgeons are some of the proposed solutions for building a fair and equitable work culture for all surgeons and overthrowing old, conventional ideas concerning gender roles. Moreover, technology has been advocated as a possible solution to gender discrimination in surgical departments; technology could facilitate an objective assessment of surgical performances and advanced training for surgeons unable to attend in-person education. A healthy, thriving, organized, supportive, and culturally transformed work environment could benefit surgeon and staff productivity and ultimately improve patient care.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656111","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}
Camille Graëff, T. Lampert, J. Mazellier, N. Padoy, Laëla El Amiri, P. Liverneaux
Aim: As a preliminary stage in the development of an artificial intelligence (AI) algorithm for surgery, this work aimed to study the inter- and intra-individual variability of phase annotations in videos of minimally invasive plate osteosynthesis of distal radius fractures (MIPO). The main hypothesis was that the inter-individual variability was almost perfect if Cohen's kappa coefficient (k) was ≥ 81% overall; the secondary hypothesis was that the intra-individual variability was almost perfect if the F1-score (F1) was ≥ 81%. Methods: The material comprised 9 annotators and three annotated MIPO videos with 5 phases and 4 sub-phases. Each video was presented 3 times to each annotator. The method involved analysing the inter-individual variability of annotations by computing k and F1 from a reference annotator. The intra-individual variability of annotations was analysed by computing F1. Results: Annotation anomalies were noticed: either absences or differences in phase and sub-phase annotations. Regarding the inter-individual variability, an almost perfect agreement between annotators was observed because k ≥ 81% for the three videos. Regarding the intra-individual variability, F1 ≥ 81% for most phases and sub-phases with the nine annotators. Conclusion: The homogeneity of annotations must be as high as possible to develop an AI algorithm in surgery. Therefore, it is necessary to identify the least efficient annotators (measurement of the intra-individual variability) to provide them with individual training and a personalised annotation rhythm. It is also important to optimise the definition of the phases, improve the annotation protocol and choose suitable training videos.
{"title":"The preliminary stage in developing an artificial intelligence algorithm: a study of the inter- and intra-individual variability of phase annotations in internal fixation of distal radius fracture videos","authors":"Camille Graëff, T. Lampert, J. Mazellier, N. Padoy, Laëla El Amiri, P. Liverneaux","doi":"10.20517/ais.2023.12","DOIUrl":"https://doi.org/10.20517/ais.2023.12","url":null,"abstract":"Aim: As a preliminary stage in the development of an artificial intelligence (AI) algorithm for surgery, this work aimed to study the inter- and intra-individual variability of phase annotations in videos of minimally invasive plate osteosynthesis of distal radius fractures (MIPO). The main hypothesis was that the inter-individual variability was almost perfect if Cohen's kappa coefficient (k) was ≥ 81% overall; the secondary hypothesis was that the intra-individual variability was almost perfect if the F1-score (F1) was ≥ 81%. Methods: The material comprised 9 annotators and three annotated MIPO videos with 5 phases and 4 sub-phases. Each video was presented 3 times to each annotator. The method involved analysing the inter-individual variability of annotations by computing k and F1 from a reference annotator. The intra-individual variability of annotations was analysed by computing F1. Results: Annotation anomalies were noticed: either absences or differences in phase and sub-phase annotations. Regarding the inter-individual variability, an almost perfect agreement between annotators was observed because k ≥ 81% for the three videos. Regarding the intra-individual variability, F1 ≥ 81% for most phases and sub-phases with the nine annotators. Conclusion: The homogeneity of annotations must be as high as possible to develop an AI algorithm in surgery. Therefore, it is necessary to identify the least efficient annotators (measurement of the intra-individual variability) to provide them with individual training and a personalised annotation rhythm. It is also important to optimise the definition of the phases, improve the annotation protocol and choose suitable training videos.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656964","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}
In the past decade, artificial intelligence (AI)-based technology has been applied to develop a simulation and navigation system and a model for predicting surgical outcomes in hepatobiliary surgery. To identify the intrahepatic vascular structure and accurate liver segmentation and volumetry, AI technology has been applied in three-dimensional (3D) simulation software. Recently, 3D and 4D printing have been used as innovative technologies for tissue and organ fabrication, medical education, and preoperative planning. AI can empower 3D and 4D printing technologies. Attempts have been made to use AI technology in augmented reality for navigating and performing intraoperative ultrasound. To predict surgical outcomes and postoperative early recurrence in patients with hepatocellular carcinoma, a deep learning model can be useful. Indocyanine green fluorescence imaging is used in hepatobiliary surgery to visualize the anatomy of the bile duct, hepatic tumors, and hepatic segmental areas. AI technology was applied to fuse intraoperative near-infrared fluorescence and visible images. Preoperative simulation, intraoperative navigation, and models to predict surgical outcomes using AI technology can be clinically applied in hepatobiliary surgery. As shown in reliable and robust clinical studies, AI can be a useful tool in clinical practice to improve the safety and efficacy of hepatobiliary surgery.
{"title":"Artificial intelligence-based technology for enhancing the quality of simulation, navigation, and outcome prediction for hepatectomy","authors":"H. Shinkawa, T. Ishizawa","doi":"10.20517/ais.2022.37","DOIUrl":"https://doi.org/10.20517/ais.2022.37","url":null,"abstract":"In the past decade, artificial intelligence (AI)-based technology has been applied to develop a simulation and navigation system and a model for predicting surgical outcomes in hepatobiliary surgery. To identify the intrahepatic vascular structure and accurate liver segmentation and volumetry, AI technology has been applied in three-dimensional (3D) simulation software. Recently, 3D and 4D printing have been used as innovative technologies for tissue and organ fabrication, medical education, and preoperative planning. AI can empower 3D and 4D printing technologies. Attempts have been made to use AI technology in augmented reality for navigating and performing intraoperative ultrasound. To predict surgical outcomes and postoperative early recurrence in patients with hepatocellular carcinoma, a deep learning model can be useful. Indocyanine green fluorescence imaging is used in hepatobiliary surgery to visualize the anatomy of the bile duct, hepatic tumors, and hepatic segmental areas. AI technology was applied to fuse intraoperative near-infrared fluorescence and visible images. Preoperative simulation, intraoperative navigation, and models to predict surgical outcomes using AI technology can be clinically applied in hepatobiliary surgery. As shown in reliable and robust clinical studies, AI can be a useful tool in clinical practice to improve the safety and efficacy of hepatobiliary surgery.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656530","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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