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Author Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach edit  doi
openurl 
  Title In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy Type Journal Article
  Year 2022 Publication (down) Endoscopy International Open Abbreviated Journal ENDIO  
  Volume 10 Issue 9 Pages E1201-E1207  
  Keywords  
  Abstract Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions.  
  Address 2022 Sep 14  
  Corporate Author Thesis  
  Publisher PMID Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ISE; 600.157 Approved no  
  Call Number Admin @ si @ GTC2022b Serial 3752  
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Author Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach edit  doi
openurl 
  Title First in Vivo Computer-Aided Diagnosis of Colorectal Polyps using White Light Endoscopy Type Journal Article
  Year 2022 Publication (down) Endoscopy Abbreviated Journal END  
  Volume 54 Issue Pages  
  Keywords  
  Abstract  
  Address 2022/04/14  
  Corporate Author Thesis  
  Publisher Georg Thieme Verlag KG Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ GTC2022a Serial 3746  
Permanent link to this record
 

 
Author O.F.Ahmad; Y.Mori; M.Misawa; S.Kudo; J.T.Anderson; Jorge Bernal edit  url
doi  openurl
  Title Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method Type Journal Article
  Year 2021 Publication (down) Endoscopy Abbreviated Journal END  
  Volume 53 Issue 9 Pages 893-901  
  Keywords  
  Abstract BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.  
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  Series Editor Series Title Abbreviated Series Title  
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  Notes ISE Approved no  
  Call Number Admin @ si @ AMM2021 Serial 3670  
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Author Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez edit  url
openurl 
  Title A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection Type Journal Article
  Year 2023 Publication (down) Electronics Abbreviated Journal ELEC  
  Volume 12 Issue 18 Pages 3947  
  Keywords micro-expression spotting; sliding window; key frame extraction  
  Abstract Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores.  
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  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ FAH2023 Serial 3864  
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Author Jordi Gonzalez; J. Varona; Xavier Roca; Juan J. Villanueva edit  openurl
  Title A Comparison Framework for Walking Performances using aSpaces Type Journal
  Year 2005 Publication (down) Electronic Letters on Computer Vision and Image Analysis, Special Issue on articulated Motion, 5(3):105–116 (Electronic Letters: IF: 1.016) Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ GVR2005 Serial 623  
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