Seung Joo Kang, Chung Hyun Tae, Chang Seok Bang, Cheol Min Shin, Young-Hoon Jeong, Miyoung Choi, Joo Ha Hwang, Yutaka Saito, Philip Wai Yan Chiu, Rungsun Rerknimitr, Christopher Khor, Vu Van Khien, Kee Don Choi, Ki-Nam Shim, Geun Am Song, Oh Young Lee, The Korean Society of Gastrointestinal Endoscopy Task Force on Clinical Practice Guidelines
Clin Endosc 2024;57(2):141-157. Published online March 14, 2024
Antithrombotic agents, including antiplatelet agents and anticoagulants, are widely used in Korea because of the increasing incidence of cardiocerebrovascular disease and the aging population. The management of patients using antithrombotic agents during endoscopic procedures is an important clinical challenge. The clinical practice guidelines for this issue, developed by the Korean Society of Gastrointestinal Endoscopy, were published in 2020. However, new evidence on the use of dual antiplatelet therapy and direct anticoagulant management has emerged, and revised guidelines have been issued in the United States and Europe. Accordingly, the previous guidelines were revised. Cardiologists were part of the group that developed the guideline, and the recommendations went through a consensus-reaching process among international experts. This guideline presents 14 recommendations made based on the Grading of Recommendations, Assessment, Development, and Evaluation methodology and was reviewed by multidisciplinary experts. These guidelines provide useful information that can assist endoscopists in the management of patients receiving antithrombotic agents who require diagnostic and elective therapeutic endoscopy. It will be revised as necessary to cover changes in technology, evidence, or other aspects of clinical practice.
Background /Aims: This study aims to compare polyp detection performance of “Deep-GI,” a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model.
Methods Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen.
Results In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%.
Conclusions Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.
Background /Aims: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI model with inference speeds faster than 25 frames per second that maintains a high level of accuracy.
Methods Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Four strategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, an image preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third, data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIM areas in real time. The results were analyzed using different validity values.
Results From the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity, specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%, 80%, 82%, 92%, 87%, and 57%, respectively.
Conclusions The bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization, and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation.
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Background /Aims: The Thai Association for Gastrointestinal Endoscopy published recommendations on safe endoscopy during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to assess the practicality and applicability of the recommendations and the perceptions of endoscopy personnel on them.
Methods A validated questionnaire was sent to 1290 endoscopy personnel globally. Of these, the data of all 330 responders (25.6%) from 15 countries, related to the current recommendations on proper personal protective equipment (PPE), case selection, scope cleaning, and safety perception, were analyzed. Ordinal logistic regression was used to determine the relationships between the variables.
Results Despite an overwhelming agreement with the recommendations on PPE (94.5%) and case selection (95.5%), their practicality and applicability on PPE recommendations and case selection were significantly lower (p=0.001, p=0.047, p<0.001, and p=0.032, respectively). Factors that were associated with lower sense of safety in endoscopy units were younger age (p=0.004), less working experience (p=0.008), in-training status (p=0.04), and higher national prevalence of COVID-19 (p=0.003). High prevalent countries also had more difficulty implementing the guidelines (p<0.001) and they considered the PPE recommendations less practical and showed lower agreement with them (p<0.001 and p=0.008, respectively). A higher number of in-hospital COVID-19 patients was associated with less agreement with PPE recommendations (p=0.039).
Conclusions Using appropriate PPE and case selection in endoscopic practice during a pandemic remains a challenge. Resource availability and local prevalence are critical factors influencing the adoption of the current guidelines.
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Background /Aims: This study aimed to evaluate the diagnostic accuracy of dual-focus narrow-band imaging (dNBI) and Lugol’schromoendoscopy (LCE) combined with probe-based confocal laser endomicroscopy (pCLE) to screen for esophageal squamous cell neoplasms (ESCNs) in patients with a history of head and neck cancer.
Methods From March to August 2016, dNBI was performed. Next, LCE was performed, followed by pCLE and biopsy. Histology has historically been the gold standard to diagnose ESCN. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of dNBI and LCE adjunct with pCLE were determined.
Results Twenty-four patients were included. Ten ESCNs were found in 8 patients (33%). Forty percent of high-graded intraepithelial neoplasias and all low-grade intraepithelial neoplasias were overlooked by dNBI. The sensitivity, specificity, PPV, NPV, and accuracy of dNBI vs. LCE combined with pCLE were 50% vs. 80%, 62% vs. 67%, 36% vs. 44%, 75% vs. 91%, and 83% vs. 70%, respectively.
Conclusions The use of dNBI to detect ESCN was suboptimal. LCE with pCLE following dNBI had additional value for detecting esophageal dysplasia not detected by dNBI. The use of pCLE to detect dNBI-missed lesions yielded a high NPV, while pCLE-guided biopsy could reduce the number of unnecessary biopsies.
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