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Quantification of Indocyanine Green Fluorescence Imaging in General, Visceral and Transplant Surgery

Near-infrared (NIR) imaging with indocyanine green (ICG) has proven to be useful in general, visceral, and transplant surgery. However, most studies have performed only qualitative assessments. Therefore, a systematic overview of all studies performing quantitative indocyanine green evaluation in ge...

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Detalles Bibliográficos
Autores principales: Pollmann, Lukas, Juratli, Mazen, Roushansarai, Nicola, Pascher, Andreas, Hölzen, Jens Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219543/
https://www.ncbi.nlm.nih.gov/pubmed/37240657
http://dx.doi.org/10.3390/jcm12103550
Descripción
Sumario:Near-infrared (NIR) imaging with indocyanine green (ICG) has proven to be useful in general, visceral, and transplant surgery. However, most studies have performed only qualitative assessments. Therefore, a systematic overview of all studies performing quantitative indocyanine green evaluation in general, visceral, and transplant surgeries should be conducted. Free term and medical subject heading (MeSH) term searches were performed in the Medline and Cochrane databases until October 2022. The main categories of ICG quantification were esophageal surgery (24.6%), reconstructive surgery (24.6%), and colorectal surgery (21.3%). Concordantly, anastomotic leak (41%) was the main endpoint, followed by the assessment of flap perfusion (23%) and the identification of structures and organs (14.8%). Most studies examined open surgery (67.6%) or laparoscopic surgery (23.1%). The analysis was mainly carried out using manufacturer software (44.3%) and open-source software (15.6%). The most frequently analyzed parameter was intensity over time for blood flow assessment, followed by intensity alone or intensity-to-background ratios for structure and organ identification. Intraoperative ICG quantification could become more important with the increasing impact of robotic surgery and machine learning algorithms for image and video analysis.