首页|University College Dublin Reports Findings in Artificial Intelligence (Intraoper ative near infrared functional imaging of rectal cancer using artificial intelli gence methods - now and near future state of the art)
University College Dublin Reports Findings in Artificial Intelligence (Intraoper ative near infrared functional imaging of rectal cancer using artificial intelli gence methods - now and near future state of the art)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Dublin, Ireland , by NewsRx editors, research stated, "Colorectal cancer remains a major cause o f cancer death and morbidity worldwide. Surgery is a major treatment modality fo r primary and, increasingly, secondary curative therapy." Financial support for this research came from University College Dublin. Our news journalists obtained a quote from the research from University College Dublin, "However, with more patients being diagnosed with early stage and premal ignant disease manifesting as large polyps, greater accuracy in diagnostic and t herapeutic precision is needed right from the time of first endoscopic encounter . Rapid advancements in the field of artificial intelligence (AI), coupled with widespread availability of near infrared imaging (currently based around indocya nine green (ICG)) can enable colonoscopic tissue classification and prognostic s tratification for significant polyps, in a similar manner to contemporary dynami c radiological perfusion imaging but with the advantage of being able to do so d irectly within interventional procedural time frames. It can provide an explaina ble method for immediate digital biopsies that could guide or even replace tradi tional forceps biopsies and provide guidance re margins (both areas where curren t practice is only approximately 80% accurate prior to definitive excision). Here, we discuss the concept and practice of AI enhanced ICG perfusio n analysis for rectal cancer surgery while highlighting recent and essential nea r-future advancements. These include breakthrough developments in computer visio n and time series analysis that allow for real-time quantification and classific ation of fluorescent perfusion signals of rectal cancer tissue intraoperatively that accurately distinguish between normal, benign, and malignant tissues in sit u endoscopically, which are now undergoing international prospective validation (the Horizon Europe CLASSICA study). Next stage advancements may include detaile d digital characterisation of small rectal malignancy based on intraoperative as sessment of specific intratumoral fluorescent signal pattern. This could include T staging and intratumoral molecular process profiling (e.g. regarding angiogen esis, differentiation, inflammatory component, and tumour to stroma ratio) with the potential to accurately predict the microscopic local response to nonsurgica l treatment enabling personalised therapy via decision support tools. Such advan cements are also applicable to the next generation fluorophores and imaging agen ts currently emerging from clinical trials."
DublinIrelandEuropeArtificial Inte lligenceCancerEmerging TechnologiesGastroenterologyHealth and MedicineMachine LearningOncologyRectal CancerSurgery
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