Ejso最新文献

{"title":"Filler advert- Scival","authors":"","doi":"10.1016/j.ejso.2024.109493","DOIUrl":"10.1016/j.ejso.2024.109493","url":null,"abstract":"","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 109493"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IFC: Filler advert REAXYS","authors":"","doi":"10.1016/S0748-7983(24)01538-5","DOIUrl":"10.1016/S0748-7983(24)01538-5","url":null,"abstract":"","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 109470"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Filler Advert Scopus","authors":"","doi":"10.1016/S0748-7983(24)01539-7","DOIUrl":"10.1016/S0748-7983(24)01539-7","url":null,"abstract":"","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 109471"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technical and functional design considerations for a real-world interpretable AI solution for NIR perfusion analysis (including cancer)","authors":"A. Moynihan ,&nbsp;P. Boland ,&nbsp;J. Cucek ,&nbsp;S. Erzen ,&nbsp;N. Hardy ,&nbsp;P. McEntee ,&nbsp;J. Rojc ,&nbsp;R. Cahill","doi":"10.1016/j.ejso.2024.108273","DOIUrl":"10.1016/j.ejso.2024.108273","url":null,"abstract":"<div><div>Near infrared (NIR) analysis of tissue perfusion via indocyanine green fluorescence assessment is performed clinically during surgery for a range of indications. Its usefulness can potentially be further enhanced through the application of interpretable artificial intelligence (AI) methods to improve dynamic interpretation accuracy in these and also open new applications. While its main use currently is for perfusion assessment as a tissue health check prior to performing an anastomosis, there is increasing interest in using fluorophores for cancer detection during surgical interventions with most research being based on the paradigm of static imaging for fluorophore uptake hours after preoperative dosing. Although some image boosting and relative estimation of fluorescence signals is already inbuilt into commercial NIR systems, fuller implementation of AI methods can enable actionable predictions especially when applied during the dynamic, early inflow-outflow phase that occurs seconds to minutes after ICG (or indeed other fluorophore) administration. Already research has shown that such methods can accurately differentiate cancer from benign tissue in the operating theatre in real time in principle based on their differential signalling and could be useful for tissue perfusion classification more generally. This can be achieved through the generation of fluorescence intensity curves from an intra-operative NIR video stream. These curves are processed to adjust for image disturbances and curve features known to be influential in tissue characterisation are extracted. Existing machine learning based classifiers can then use these features to classify the tissue in question according to prior training sets. The use of this interpretable methodology enables accurate classification algorithms to be built with modest training sets in comparison to those required for deep learning modelling in addition to achieving compliance with medical device regulations. Integration of the multiple algorithms required to achieve this classification into a desktop application or medical device could make the use of this method accessible and useful to (as well as useable by) surgeons without prior training in computer technology. This document details some technical and functional design considerations underlying such a novel recommender system to advance the foundational concept and methodology as software as medical device for in situ cancer characterisation with relevance more broadly also to other tissue perfusion applications.</div></div>","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 108273"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A foundation for evaluating the surgical artificial intelligence literature","authors":"Daniel A. Hashimoto ,&nbsp;Sai Koushik Sambasastry ,&nbsp;Vivek Singh ,&nbsp;Sruthi Kurada ,&nbsp;Maria Altieri ,&nbsp;Takuto Yoshida ,&nbsp;Amin Madani ,&nbsp;Matjaz Jogan","doi":"10.1016/j.ejso.2024.108014","DOIUrl":"10.1016/j.ejso.2024.108014","url":null,"abstract":"<div><div>With increasing growth in applications of artificial intelligence<span> (AI) in surgery, it has become essential for surgeons to gain a foundation of knowledge to critically appraise the scientific literature, commercial claims regarding products, and regulatory and legal frameworks that govern the development and use of AI. This guide offers surgeons a framework with which to evaluate manuscripts that incorporate the use of AI. It provides a glossary of common terms, an overview of prerequisite knowledge to maximize understanding of methodology, and recommendations on how to carefully consider each element of a manuscript to assess the quality of the data on which an algorithm was trained, the appropriateness of the methodological approach, the potential for reproducibility of the experiment, and the applicability to surgical practice, including considerations on generalizability and scalability.</span></div></div>","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 108014"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139740719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Filler advert- Sciencedirect","authors":"","doi":"10.1016/S0748-7983(24)01541-5","DOIUrl":"10.1016/S0748-7983(24)01541-5","url":null,"abstract":"","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 109473"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study","authors":"Fiona R. Kolbinger ,&nbsp;Sebastian Bodenstedt ,&nbsp;Matthias Carstens ,&nbsp;Stefan Leger ,&nbsp;Stefanie Krell ,&nbsp;Franziska M. Rinner ,&nbsp;Thomas P. Nielen ,&nbsp;Johanna Kirchberg ,&nbsp;Johannes Fritzmann ,&nbsp;Jürgen Weitz ,&nbsp;Marius Distler ,&nbsp;Stefanie Speidel","doi":"10.1016/j.ejso.2023.106996","DOIUrl":"10.1016/j.ejso.2023.106996","url":null,"abstract":"<div><h3>Introduction</h3><div>Complex oncological procedures pose various surgical challenges including dissection in distinct tissue planes and preservation of vulnerable anatomical structures throughout different surgical phases. In rectal surgery, violation of dissection planes increases the risk of local recurrence and autonomous nerve damage resulting in incontinence and sexual dysfunction. This work explores the feasibility of phase recognition and target structure segmentation in robot-assisted rectal resection (RARR) using machine learning.</div></div><div><h3>Materials and methods</h3><div>A total of 57 RARR were recorded and subsets of these were annotated with respect to surgical phases and exact locations of target structures (anatomical structures, tissue types, static structures, and dissection areas). For surgical phase recognition, three machine learning models were trained: LSTM, MSTCN, and Trans-SVNet. Based on pixel-wise annotations of target structures in 9037 images, individual segmentation models based on DeepLabv3 were trained. Model performance was evaluated using F1 score, Intersection-over-Union (IoU), accuracy, precision, recall, and specificity.</div></div><div><h3>Results</h3><div>The best results for phase recognition were achieved with the MSTCN model (F1 score: 0.82 ± 0.01, accuracy: 0.84 ± 0.03). Mean IoUs for target structure segmentation ranged from 0.14 ± 0.22 to 0.80 ± 0.14 for organs and tissue types and from 0.11 ± 0.11 to 0.44 ± 0.30 for dissection areas. Image quality, distorting factors (i.e. blood, smoke), and technical challenges (i.e. lack of depth perception) considerably impacted segmentation performance.</div></div><div><h3>Conclusion</h3><div>Machine learning-based phase recognition and segmentation of selected target structures are feasible in RARR. In the future, such functionalities could be integrated into a context-aware surgical guidance system for rectal surgery.</div></div>","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 106996"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10396150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to: Pioneering combination: Nivolumab and isolated limb perfusion in melanoma in-transit metastases treatment","authors":"Carl-Jacob Holmberg ,&nbsp;Lisanne P. Zijlker ,&nbsp;Dimitrios Katsarelias,&nbsp;Anne E. Huibers,&nbsp;Michel W.J.M. Wouters,&nbsp;Yvonne Schrage,&nbsp;Sophie J.M. Reijers,&nbsp;Johannes V. van Thienen,&nbsp;Dirk J. Grünhagen,&nbsp;Anna Martner,&nbsp;Jonas A. Nilsson,&nbsp;Alexander C.J. van Akkooi,&nbsp;Lars Ny,&nbsp;Winan J. van Houdt,&nbsp;Roger Olofsson Bagge","doi":"10.1016/j.ejso.2024.108653","DOIUrl":"10.1016/j.ejso.2024.108653","url":null,"abstract":"","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"50 12","pages":"Article 108653"},"PeriodicalIF":3.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to the Editor: Reassessing margin standards in breast-conserving therapy.","authors":"Emad A Rakha, Cecily Quinn, Stephen Fox, Yazan A Masannat, Andreas Karakatsanis, J Michael Dixon","doi":"10.1016/j.ejso.2024.109504","DOIUrl":"https://doi.org/10.1016/j.ejso.2024.109504","url":null,"abstract":"","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"51 2","pages":"109504"},"PeriodicalIF":3.5,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of enteral immunonutrition in laparoscopic versus open resections in colorectal cancer surgery: A meta-analysis of randomised controlled trials.","authors":"Chee Siong Wong, Shafquat Zaman, Koushik Siddiraju, Archana Sellvaraj, Tariq Ghattas, Yegor Tryliskyy","doi":"10.1016/j.ejso.2024.109488","DOIUrl":"https://doi.org/10.1016/j.ejso.2024.109488","url":null,"abstract":"<p><strong>Introduction: </strong>Immunonutrition (IMN) modulates the activity of the immune system. However, the effects of IMN on cancer patients following colorectal surgery is still lacking. We performed a systematic review and meta-analysis to evaluate the outcomes of IMN in patients undergoing laparoscopic versus open colorectal surgery.</p><p><strong>Methods: </strong>A systematic search of multiple electronic data sources was conducted in accordance with PRISMA guidelines and included MEDLINE via PubMed, EMBASE, Scopus, and Web of Science. All eligible studies reporting comparative outcomes of immunonutrition in colorectal surgery were included. Subgroup analysis of outcomes of interest was performed and data were analysed using Review Manager (RevMan) Version 5.4.1.</p><p><strong>Results: </strong>Nine randomised controlled trials (RCTs) were identified. The final pooled analysis included 1199 patients (592 IMN group and 592 control group). Of these, 55.3 % (655/1184) had open colorectal surgery (OG) and 44.7 % (529/1184) underwent laparoscopic colorectal surgery (LG). IMN reduced the risk of wound infection significantly in the OG [risk ratio (RR) 0.48, 95 % confidence interval (CI) 0.32 to 0.72; p = 0.0005)] and the open and laparoscopic group (OLG) [RR 0.33, 95 % CI 0.15 to 0.76; p = 0.008]. Moreover, IMN was also associated with a significantly shorter length of hospital stay (MD - 2.37 days, 95 % CI - 3.39 to -1.36; p < 0.0001) in the OG. Other post-operative morbidities (anastomotic leak and ileus) and mortality outcomes in the OG, LG, and OLG were comparable.</p><p><strong>Conclusions: </strong>Pre-operative IMN could reduce the wound infection rate and shorten length of hospital stay in patients following elective colorectal surgery. The benefit of these improved clinical outcomes could be further evaluated with a cost-benefit analysis. IMN should be recommended as nutritional adjunct in the Enhanced Recovery after Surgery (ERAS) pathway following colorectal surgery.</p>","PeriodicalId":11522,"journal":{"name":"Ejso","volume":"51 2","pages":"109488"},"PeriodicalIF":3.5,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}