Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy

Abstract

Background/Aims: We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods. Methods: We studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image. Results: The accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001) Conclusions: Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup. Endosc 2021 Sep 23. [Epub ahead of print]

Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials

“…Artificial intelligence (AI), including computer-aided diagnosis (CAD) systems, has recently been developed to assist endoscopists in detecting lesions 62 and predicting lesion histology. 63 Recent meta-analyses have validated the efficacy of AI systems for detecting colorectal lesions. AI-assisted colonoscopies provided a 1.4 to 1.5-fold higher ADR than conventional colonoscopies.…”

Colon polyps: updates in classification and management

“…Multiple uses for artificial intelligence are now being considered including its use in endoscopic polyp classification. Several models incorporating machine learning are currently in the development pipeline to assist in endoscopic pathology assessment and once developed may revolutionize the current schema of endoscopic classification [48–50,51 ▪▪ ]. With a more standardized and reliable technology that can identify polyp pathology using machine learning, opportunities may arise for additional improvement in quality measures such as resect and discard, reducing the number of required pathology assessment and providing patients with real-time surveillance recommendations [52].…”

Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice

“…Racz et al 5 compared the accuracy of a developed artificial intelligence-based polyp histology prediction (AIPHP) method to the NICE classification and pathologic results. The AIPHP software was created using a machine learning method and measured five geometrical and color features of the image at optical maximum magnification.…”