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Patients referred for transanal management of rectal neoplasia received intravenous indocyanine green; near-infrared endoscopic videos were recorded and processed with custom quantification software to generate intensity time-series. From 182 patients (190 recordings), ML on extracted plot features achieved performance that improved when combined with MRI and preoperative clinical predictions. Results benchmarked against endoscopic biopsy, MRI, and expert surgeon opinion.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-classification-of-rectal-neoplasia-by-endoscopic-fluorescence-perfusion-analysis/346392/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/artificial-intelligence-classification-of-rectal-neoplasia-by-endoscopic-fluorescence-perfusion-analysis/346392.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How was fluorescence perfusion data collected for the ML model?","Question",{"text":112,"@type":113},"Intravenous indocyanine green was administered, and near-infrared endoscopic video was recorded. Videos were processed with custom fluorescence quantification software to produce intensity time-series for neoplastic and normal regions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What types of features were extracted for machine learning classification?",{"text":117,"@type":113},"Plot features extracted from the intensity time-series included coefficient of variation (CV) and other algorithm inputs. These features were used for train/test ML classification to estimate cancer sensitivity, specificity, and accuracy.",{"name":119,"@type":110,"acceptedAnswer":120},"How did the ML approach compare with traditional diagnostic methods?",{"text":121,"@type":113},"The study reports sensitivity/specificity/accuracy for traditional endoscopic biopsy, MRI, and expert surgeon opinion at surgery, then compares these to ML algorithms using base and CV features. Combining MRI and clinicians’ preoperative predictions with ML further improved sensitivity, specificity, and accuracy.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},346392,1790132000,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nArtificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis  \nPatrick A. Boland1, Pol MacAonghusa1, Ashokkumar Singaravelu1, Philip D. McEntee1, Jernej Cucek2, Samo Erzen2, Felix Aigner3, Alberto Arezzo4, John P. Burke5, Roel Hompes6, Jurriaan B. Tuynman6, Peter M. Neary7 & RonanA. Cahill1,8􀀍  \nDisordered vascularity is a hallmark of carcinogenesis. Fluorescence microperfusion heterogeneity may discriminate malignant transformation within significant (> 20 mm) rectal polyps enabling in-situ endoscopic classification via machine learning (ML) methods to inform clinical care. Patients referred for transanal management of rectal neoplasia were recruited. Indocyanine green was administered intravenously and near-infrared (NIR) endoscopic video recorded. Videos were processed with bespoke fluorescence quantification software producing intensity-timeseries plots for neoplastic and normal regions of interest in the same patients. Plot features were extracted to train/test ML classification algorithms, including coefficient of variation (CV), reporting cancer characterisation sensitivity, specificity and accuracy. 190 video recordings from 182 patients (57.9% with cancer) from six cancer centres provided usable dataset (91% of 201 consenting patients). Overall, the software accurately tracked and detailed NIR perfusion features from, on average (SD), 74.7%(25.3) of annotated regions of interest over the five-minute recording phase. The sensitivity/specificity/accuracy rates of traditional endoscopic biopsy (n = 172), MRI (n = 139) and expert surgeon opinion (n = 190) at surgery were 70.8%/100%/81.7%, 85.4%/ 44.1%/72.7% and 79.1%/80%/79.5% respectively. In comparison, trained ML sensitivity/specificity/ accuracy was 77.6%/39.8%/61.1% and 73.5%/48.2%/62.6% with base and CV featured algorithms respectively. Combining point of care clinical data (specifically MRI and clinicians’ preoperative predictions) with the ML algorithms improved sensitivity/specificity/accuracy to 86.0%/71.1%/79.5% and 82.2%/74.7%/79.0% respectively. Malignant transformation precipitates discriminant perfusion patterns, in a manner exploitable digitally, that indicate cancer presence insignificant rectal polyps. Combining clinical indicators appears to improve classification accuracy further, especially specificity.  \nTrial registration: Future of Colorectal Cancer Surgery (FOOCCuS1). [Clinicatrials.gov. NCT04220242](Clinicatrials.gov. NCT04220242) . [Clinicaltrials.gov/study/NCT04220242. CLASSICA: Validating AI](Clinicaltrials.gov/study/NCT04220242. CLASSICA: Validating AI) in Classifying Cancer in Real-Time Sur[gery. Clinicaltrials.gov. NCT05793554. Clinicaltrials.gov/study/NCT05793554](gery. Clinicaltrials.gov. NCT05793554. Clinicaltrials.gov/study/NCT05793554) .  \nKeywords Rectal cancer, Transanal minimally invasive surgery (TAMIS), Artificial intelligence, Machine learning, Indocyanine green, Nearinfrared fluoresence angiography  \nAngiogenesis and disordered vascularityare hallmark characteristics of carcinogenesis1–4. Increased angiogenesis, including upregulated vascular endothelial growth factor (VEGF)5,6, provokes aberrant vascularity with “leaky”vessel walls and heterogenous architecture as a key step in adenoma-carcinoma sequence7,8. Such changes result in distinctive perfusion patterns that can be exploited by dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to aid diagnosis and prognosis5,9, 10. Direct visualisation of perfusion should evidence similar discrimination with clinical value for instance in endoscopy for significant (i.e. >20 mm) colorectal neoplasia11, 12 where appropriateness for local excision is a crucial consideration.  \n1UCD Centre for Precision Surgery, 47 Eccles Street, Dublin 7, Ireland. 2Arctur d.o.o, Novo Gorica, Slovenia.  \n3Department of Colorectal Surgery, Krankenhaus der Barmherzigen Brü","cbCaipYCTbscbKOc","https://ap.wps.com/l/cbCaipYCTbscbKOc","pdf",2219176,"English","# Study design and rationale\n## Fluorescence microperfusion and angiogenesis background\n## Data acquisition and software processing\n## Feature extraction and ML classification\n## Performance comparisons and clinical data fusion","[{\"question\":\"How was fluorescence perfusion data collected for the ML model?\",\"answer\":\"Intravenous indocyanine green was administered, and near-infrared endoscopic video was recorded. Videos were processed with custom fluorescence quantification software to produce intensity time-series for neoplastic and normal regions.\"},{\"question\":\"What types of features were extracted for machine learning classification?\",\"answer\":\"Plot features extracted from the intensity time-series included coefficient of variation (CV) and other algorithm inputs. These features were used for train/test ML classification to estimate cancer sensitivity, specificity, and accuracy.\"},{\"question\":\"How did the ML approach compare with traditional diagnostic methods?\",\"answer\":\"The study reports sensitivity/specificity/accuracy for traditional endoscopic biopsy, MRI, and expert surgeon opinion at surgery, then compares these to ML algorithms using base and CV features. Combining MRI and clinicians’ preoperative predictions with ML further improved sensitivity, specificity, and accuracy.\"}]","Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis | PDF",1790061239,25]