A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization. - Gareth Gallagher; Ra'ed Malallah; Jonathan P Epperlein; Jeffrey Dalli; Niall Hardy; Abhinav Jindal; Pol G MacAonghusa; Ronan A Cahill (PLoS ONE)
Real-time endoscopic rectal lesion characterization employing artificial intelligence (AI) and near-infrared (NIR) imaging of the fluorescence perfusion indicator agent Indocyanine Green (ICG) has demonstrated promise. However, commercially available fluorescence endoscopes do not possess the flexibility and anatomical reach capabilities of colonoscopy while commercial flexible scopes do not yet provide beyond visible spectral imaging. This limits the application of this AI-NIR classification technology. Here, to close this technical gap, we present our development of a colonoscope-compatible flexible imaging probe for NIR-ICG visualization combined with a full field of view machine learning (ML) algorithm for fluorescence quantification and perfusion pattern cross-correlation (including first in human testing). The imaging probe is capable of 133µm minimum object resolution, with a maximum working distance of 50mm and an excitation illumination power of 52mW with 75o average field of illumination (meaning minimum device tip distance from target is 13 mm for a 2 cm polyp). The system demonstrated ex-vivo and in-vivo NIR visualization of clinically relevant concentrations of ICG in both resected and in situ (extracorporeally) colon in patients undergoing colorectal resection. A previously developed AI-NIR perfusion quantification algorithm was applied to videos of a bench model of varying ICG flow captured with the developed flexible system with added ML features generated full field of view pixel-level fluorescence time-series measurements capable of distinguishing distinct ICG flow regions in the image via correlative dynamic fluorescence intensity profiles. Jaccard Index comparison of the AI -generated flow regions against manually delineated flow regions resulted in 79% accuracy. While further clinical validation of the AI-NIR polyp classification method is on-going (in the Horizon Europe Awarded CLASSICA project), other use case applications of NIR colonoscopy include simpler perioperative perfusion assessment in patients undergoing colorectal resection and combination with targeted agents in development thus encouraging continuing development and design optimization of this flexible NIR imaging probe to enable clinical and commercial translation.