Gastroenterologist and surgeon perceptions of recommendations for optimal endoscopic localization of colorectal neoplasms.

National consensus recommendations have recently been developed to standardize colorectal tumour localization and documentation during colonoscopy. In this qualitative semi-structured interview study, we identified and contrast the perceived barriers and facilitators to using these new recommendations according to gastroenterologists and surgeons in a large central Canadian city. Interviews were analyzed according to the Consolidated Framework for Implementation Research (CFIR) through directed content analysis. Solutions were categorized using the Expert Recommendations for Implementing Change (ERIC) framework. Eleven gastroenterologists and ten surgeons participated. Both specialty groups felt that the new recommendations were clearly written, adequately addressed current care practice tensions, and offered a relative advantage versus existing practices. The new recommendations appeared appropriately complex, applicable to most participants, and could be trialed and adapted prior to full implementation. Major barriers included a lack of relevant external or internal organizational incentives, non-existing formal feedback processes, and a lack of individual familiarity with the evidence behind some recommendations. With application of the ERIC framework, common barriers could be addressed through accessing new funding, altering incentive structures, changing record systems, educational interventions, identifying champions, promoting adaptability, and employing audit/feedback processes. Future research is needed to test strategies for feasibility and effectiveness.

[The effect of glycopyrrolate on intestinal spasm and hemodynamics in painless colonoscopy].

Objective: To investigate the effects of glycopyrrolate on intestinal spasm and hemodynamics in painless colonoscopy. Methods: A total of 100 patients who were scheduled to undergo painless colonoscopy were selected as the study subjects and randomly divided into two groups by a computerized number method. Ten patients in both groups dropped out because of disruption of the study protocol, and 45 patients from each group were included in the final analysis. Before anesthesia induction, patients in group glycopyrrolate (group G) were injected with 0.2 mg glycopyrrolate, while those in congtrol group (group C) were injected with an equal amount of saline. The heart rate, systolic blood pressure, and diastolic blood pressure were recorded at T0 (baseline period), T1 (after anesthesia induction), T2 (colonoscopy over sigmoid colon), T3 (colonoscopy over the liver region), T4 (after the end of examination), and T5 (at the awakening phase), and the degree of intestinal spasm was assessed intraoperatively using the Likert's four-point scale. The numerical rating scale (NRS) was used to assess preoperative and postoperative pain. The incidence of adverse events was recorded. Results: The general data at baseline were not statistically different between the two groups (P>0.05). During the procedure, patients in group G had lower intraoperative intestinal spasm scores than those in group C (P=0.028). Intraoperative hypotension and bradycardia occurrence were lower in group G than in group C (P<0.05), and intraoperative norepinephrine use was also lower than in the group C (P=0.034). Postoperative visual analog scale pain scores were lower in group G (P=0.047), but patients who used glycopyrrolate had a higher proportion of dry mouth (P=0.035). Conclusion: During painless colonoscopy, preoperative administration of glycopyrrolate significantly improved intraoperative hemodynamic fluctuations, reduced the incidence of hypotension and bradycardia, and relieved postoperative pain. However, glycopyrrolate use resulted in the risk of dry mouth.

Comparison of Fentanyl, Ketamine, and Lidocaine Combined with Propofol Anesthesia in Patients with Crohn Disease Undergoing Colonoscopy.

BACKGROUND Colonoscopy is the predominant invasive procedure for Crohn disease (CD) patients. Opioids and propofol carry risks of respiratory and cardiovascular complications. This study aimed to evaluate whether substituting fentanyl with ketamine or lidocaine could diminish propofol usage and minimize adverse events. MATERIAL AND METHODS In total, 146 patients with CD scheduled for elective colonoscopy were assigned to anesthesia with fentanyl (n=47), ketamine (n=47), or lidocaine (n=55). Propofol was administered to achieve sufficient anesthesia. Measured outcomes in each group included propofol consumption, hypotension and desaturation incidents, adverse event types, consciousness recovery time, abdominal pain intensity, Aldrete scale, and Post Anaesthetic Discharge Scoring System (PADSS). RESULTS Patients administered fentanyl needed significantly more propofol (P=0.017) than those on ketamine, with lidocaine showing no notable difference (P=0.28). Desaturation was significantly less common in the ketamine and lidocaine groups than fentanyl group (P<0.001). The ketamine group experienced milder reductions in mean arterial (P=0.018) and systolic blood pressure (P<0.001). Recovery metrics (Aldrete and PADSS scores) were lower for fentanyl (P<0.001), although satisfaction and pain levels were consistent across all groups (P=0.797). Dizziness occurred less frequently with lidocaine than fentanyl (17.2%, P=0.018) and ketamine (15.1%, P=0.019), while metallic taste incidents were more prevalent in the lidocaine group (13.5%, P=0.04) than fentanyl group. CONCLUSIONS Using ketamine or lidocaine instead of fentanyl in anesthesia for colonoscopy in patients with CD significantly lowers propofol use, reduces desaturation events, maintains blood pressure more effectively, without increasing hypotension risk, and accelerates recovery, without negatively impacting adverse events or patient satisfaction.

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at different stages. In early detection and diagnosis, AI leverages its prowess in imaging analysis, scrutinizing CT scans, MRI, and colonoscopy views to identify polyps and tumors. This ability enables timely and accurate diagnoses, initiating treatment at earlier stages. AI has helped in personalized treatment planning because of its ability to integrate diverse patient data, including tumor characteristics, medical history, and genetic information. Integrating AI into clinical decision support systems guarantees evidence-based treatment strategy suggestions in multidisciplinary clinical settings, thus improving patient outcomes. This narrative review explores the multifaceted role of AI, spanning early detection of colorectal cancer, personalized treatment planning, polyp detection, lymph node evaluation, cancer staging, robotic colorectal surgery, and training of colorectal surgeons.

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma.

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Accuracy of optical diagnosis with narrow band imaging in the surveillance of ulcerative colitis: a prospective study comparing Kudo, Kudo-IBD and NICE classifications.

PURPOSE: The diagnostic accuracy of Narrow Band Imaging (NBI) in the endoscopic surveillance of ulcerative colitis (UC) has been disappointing in most trials which used the Kudo classification. We aim to compare the performance of NBI in the lesion characterization of UC, when applied according to three different classifications (NICE, Kudo, Kudo-IBD). METHODS: In a prospective, real-life study, all visible lesions found during consecutive surveillance colonoscopies with NBI (Exera-II CV-180) for UC were classified as suspected or non-suspected for neoplasia according to the NICE, Kudo and Kudo-IBD criteria. The sensitivity (SE), specificity (SP), positive (+LR) and negative (-LR) likelihood ratios of the three classifications were calculated, using histology as the reference standard. RESULTS: 394 lesions (mean size 6 mm, range 2-40 mm) from 84 patients were analysed. Twenty-one neoplastic (5%), 49 hyperplastic (12%), and 324 inflammatory (82%) lesions were found. The diagnostic accuracy of the NICE, Kudo and Kudo-IBD classifications were, respectively: SE 76%-71%-86%; SP 55-69%-79% (p < 0.05 Kudo-IBD vs. both Kudo and NICE); +LR 1.69-2.34-4.15 (p < 0.05 Kudo-IBD vs. both Kudo and NICE); -LR 0.43-0.41-0.18. CONCLUSION: The diagnostic accuracy of NBI in the differentiation of neoplastic and non-neoplastic lesions in UC is low if used with conventional classifications of the general population, but it is significantly better with the modified Kudo classification specific for UC.

REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.

Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.

Effect of alverine citrate plus simethicone in colonoscopy: a randomized controlled trial.

Colonoscopy is the standard procedure for screening, and surveillance of colorectal cancer, including the treatment for colonic lesions. Colonic spasm is an important problem from colonoscopy that affects both surgeons and patients. The spasm also might be the cause of longer cecal intubation time, difficulty of the procedure, and increased pain. Previous reports indicated that antispasmodic agents can decrease such symptoms. Therefore, we conducted this study to investigate the cecal intubation time of antispasmodic agents. A single blinded randomized controlled trial was conducted from 01/11/2020 to 31/08/2021. One hundred four patients were allocated to antispasmodic agent group and control group, in 1:1 ratio. The efficacy of median (range) cecal intubation time showed similar results of 5 (2, 14) and 5 (2, 15) minutes with no statistically significant difference. The mean scores of all domains i.e., pain, spasm, cleanliness, and difficulty were better in the antispasmodic agent group about 2.6 (1.4), 1.8 (0.8), 2.4 (0.9), and 2.0 (0.9), respectively, than control group but there were spasm and cleanliness showed statistically significant difference. Moreover, the satisfaction scores showed better efficacy in decreased spasm, decreased difficulty, and increased cleanliness than control group. Prescribing of antispasmodic drugs before colonoscopy might be the choice of treatment for the patients. The antispasmodic drugs will be beneficial to both of the patient and the doctor.

Efficacy and safety of salvage endoscopy in the treatment of residual or recurrent colorectal neoplasia after endoscopic resection: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and meta-analyze the efficacy and safety of salvage endoscopy for residual or recurrence of colorectal tumors after endoscopic resection. METHODS: Multiple databases including PubMed, EMBASE and the Cochrane Library were searched to screen for eligible studies and perform data extraction and pooled analysis. RESULTS: Sixteen studies on salvage endoscopy for residual or recurrent colorectal cancer after endoscopic resection were included, covering approximately 994 patients. The results of the meta-analysis demonstrated that salvage endoscopic therapy for residual or recurrent colorectal tumors following endoscopic resection achieved an en bloc resection rate of 92% (95% CI 0.85-0.97; I2 = 91%) and an R0 resection rate of 82% (95% CI 0.75-0.87; I2 = 78%). The rates of intraoperative or postoperative bleeding and perforation were 10%/1% and 5%/2%, and the recurrence rate was 2%. CONCLUSIONS: Salvage endoscopic resection is an effective and safe treatment strategy for residual or recurrent colorectal tumors after endoscopic resection.

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations.

Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations. This paper presents a study asking medical professionals about their opinion of current state-of-the-art explainable artificial intelligence methods when applied to a gastrointestinal disease detection use case. We compare two different categories of explanation methods, intrinsic and extrinsic, and gauge their opinion of the current value of these explanations. The results indicate that intrinsic explanations are preferred and that physicians see value in the explanations. Based on the feedback collected in our study, future explanations of medical deep neural networks can be tailored to the needs and expectations of doctors. Hopefully, this will contribute to solving the issue of black box medical systems and lead to successful implementation of this powerful technology in the clinic.

Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images.

Colonoscopy is the choice procedure to diagnose, screening, and treat the colon and rectum cancer, from early detection of small precancerous lesions (polyps), to confirmation of malign masses. However, the high variability of the organ appearance and the complex shape of both the colon wall and structures of interest make this exploration difficult. Learned visuospatial and perceptual abilities mitigate technical limitations in clinical practice by proper estimation of the intestinal depth. This work introduces a novel methodology to estimate colon depth maps in single frames from monocular colonoscopy videos. The generated depth map is inferred from the shading variation of the colon wall with respect to the light source, as learned from a realistic synthetic database. Briefly, a classic convolutional neural network architecture is trained from scratch to estimate the depth map, improving sharp depth estimations in haustral folds and polyps by a custom loss function that minimizes the estimation error in edges and curvatures. The network was trained by a custom synthetic colonoscopy database herein constructed and released, composed of 248400 frames (47 videos), with depth annotations at the level of pixels. This collection comprehends 5 subsets of videos with progressively higher levels of visual complexity. Evaluation of the depth estimation with the synthetic database reached a threshold accuracy of 95.65%, and a mean-RMSE of 0.451cm, while a qualitative assessment with a real database showed consistent depth estimations, visually evaluated by the expert gastroenterologist coauthoring this paper. Finally, the method achieved competitive performance with respect to another state-of-the-art method using a public synthetic database and comparable results in a set of images with other five state-of-the-art methods. Additionally, three-dimensional reconstructions demonstrated useful approximations of the gastrointestinal tract geometry. Code for reproducing the reported results and the dataset are available at https://github.com/Cimalab-unal/ColonDepthEstimation.