Impact of comprehensive optical diagnosis training using Workgroup serrAted polypS and Polyposis classification on detection of adenoma and sessile serrated lesion

A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart

In this study, we implemented a combination of data augmentation and artificial intelligence (AI) model-Convolutional Neural Network (CNN)-to help physicians classify colonic polyps into traditional adenoma (TA), sessile serrated adenoma (SSA), and hyperplastic polyp (HP). We collected ordinary endoscopy images under both white and NBI lights. Under white light, we collected 257 images of HP, 423 images of SSA, and 60 images of TA. Under NBI light, were collected 238 images of HP, 284 images of SSA, and 71 images of TA. We implemented the CNN-based artificial intelligence model, Inception V4, to build a classification model for the types of colon polyps. Our final AI classification model with data augmentation process is constructed only with white light images. Our classification prediction accuracy of colon polyp type is 94%, and the discriminability of the model (area under the curve) was 98%. Thus, we can conclude that our model can help physicians distinguish between TA, SSA, and HPs and correctly identify precancerous lesions such as TA and SSA.

Optimization of colonoscopy quality: Comprehensive review of the literature and future perspectives

Colonoscopy is crucial in preventing colorectal cancer (CRC) and reducing associated mortality. This comprehensive review examines the importance of high-quality colonoscopy and associated quality indicators, including bowel preparation, cecal intubation rate, withdrawal time, adenoma detection rate (ADR), complete resection, specimen retrieval, complication rates, and patient satisfaction, while also discussing other ADR-related metrics. Additionally, the review draws attention to often overlooked quality aspects, such as nonpolypoid lesion detection, as well as insertion and withdrawal skills. Moreover, it explores the potential of artificial intelligence in enhancing colonoscopy quality and highlights specific considerations for organized screening programs. The review also emphasizes the implications of organized screening programs and the need for continuous quality improvement. A high-quality colonoscopy is crucial for preventing postcolonoscopy CRC- and CRC-related deaths. Health-care professionals must develop a thorough understanding of colonoscopy quality components, including technical quality, patient safety, and patient experience. By prioritizing ongoing evaluation and refinement of these quality indicators, health-care providers can contribute to improved patient outcomes and develop more effective CRC screening programs.

Mucosal imaging in colon polyps: New advances and what the future may hold

An expanding range of advanced mucosal imaging technologies have been developed with the goal of improving the detection and characterization of lesions in the gastrointestinal tract. Many technologies have targeted colorectal neoplasia given the potential for intervention prior to the development of invasive cancer in the setting of widespread surveillance programs. Improvement in adenoma detection reduces miss rates and prevents interval cancer development. Advanced imaging technologies aim to enhance detection without significantly increasing procedural time. Accurate polyp characterisation guides resection techniques for larger polyps, as well as providing the platform for the “resect and discard” and “do not resect” strategies for small and diminutive polyps. This review aims to collate and summarise the evidence regarding these technologies to guide colonoscopic practice in both interventional and non-interventional endoscopists.

"Endoscopic" adenoma detection rate as a quality indicator of colonoscopy: First report from the J-SCOUT study

OBJECTIVES

To examine whether reasonable detection rate of endoscopically diagnosed lesions as adenoma ("endoscopic" adenoma detection rate [ADR]) could be calculated with a database generated from colonoscopy reports and whether it could be used as a surrogate colonoscopy quality indicator of "pathological" ADR.

METHODS

A lesion-by-lesion database of colonoscopies performed between 2010 and 2020 at eight Japanese endoscopy centers and corresponding pathology database were integrated. Differences in numbers of detected polyps, "endoscopic" and "pathological" adenomas, and what these differences could be attributed to were examined. Polyp detection rate (PDR), "endoscopic" and "pathological" ADRs, and correlation coefficients between "pathological" ADR and PDR or "endoscopic" ADR by each endoscopist were calculated.

RESULTS

Overall, 129,065 colonoscopy reports were analyzed. Among a total of 146,854 polyps, more "endoscopic" adenomas (n = 117,359) were observed than "pathological" adenomas (n = 70,076), primarily because adenomas were not resected on site, rather than because of a misdiagnosis. In all patients analyzed, PDR, "endoscopic" and "pathological" ADRs were 56.4% (95% confidence interval [CI] 56.2-56.7), 48.0% (95% CI 47.7-48.3), and 32.7% (95% CI 32.5-33.0), respectively. "Endoscopic" and "pathological" ADRs from each endoscopist showed a high correlation in hospitals where adenomas were usually resected at the time of examination.

CONCLUSIONS

By appropriately describing endoscopically diagnosed lesions as "adenomas" in endoscopy reports, "endoscopic" ADR might be used as a surrogate colonoscopy quality indicator of "pathological" ADR (UMIN000040690).

Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.

Development of a Natural Language Processing System for Assessing Quality Indicators from Free-Text Colonoscopy and Pathology Reports: Methodology Development and Applications (Preprint)

Background

Manual data extraction of colonoscopy quality indicators is time and labor intensive. Natural language processing (NLP), a computer-based linguistics technique, can automate the extraction of important clinical information, such as adverse events, from unstructured free-text reports. NLP information extraction can facilitate the optimization of clinical work by helping to improve quality control and patient management.

Objective

We developed an NLP pipeline to analyze free-text colonoscopy and pathology reports and evaluated its ability to automatically assess adenoma detection rate (ADR), sessile serrated lesion detection rate (SDR), and postcolonoscopy surveillance intervals.

Methods

The NLP tool for extracting colonoscopy quality indicators was developed using a data set of 2000 screening colonoscopy reports from a single health care system, with an associated 1425 pathology reports. The NLP system was then tested on a data set of 1000 colonoscopy reports and its performance was compared with that of 5 human annotators. Additionally, data from 54,562 colonoscopies performed between 2010 and 2019 were analyzed using the NLP pipeline.

Results

The NLP pipeline achieved an overall accuracy of 0.99-1.00 for identifying polyp subtypes, 0.99-1.00 for identifying the anatomical location of polyps, and 0.98 for counting the number of neoplastic polyps. The NLP pipeline achieved performance similar to clinical experts for assessing ADR, SDR, and surveillance intervals. NLP analysis of a 10-year colonoscopy data set identified great individual variance in colonoscopy quality indicators among 25 endoscopists.

Conclusions

The NLP pipeline could accurately extract information from colonoscopy and pathology reports and demonstrated clinical efficacy for assessing ADR, SDR, and surveillance intervals in these reports. Implementation of the system enabled automated analysis and feedback on quality indicators, which could motivate endoscopists to improve the quality of their performance and improve clinical decision-making in colorectal cancer screening programs.