Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis

Background Optical diagnosis of colorectal polyps remains challenging. Image-enhancement techniques such as narrow-band imaging and blue-light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high-definition white-light (HDWL) and BLI images, and compared the system with the optical diagnosis of expert and novice endoscopists.Methods CADx characterized colorectal polyps by exploiting artificial neural networks. Six experts and 13 novices optically diagnosed 60 colorectal polyps based on intuition. After 4 weeks, the same set of images was permuted and optically diagnosed using the BLI Adenoma Serrated International Classification (BASIC).Results CADx had a diagnostic accuracy of 88.3% using HDWL images and 86.7% using BLI images. The overall diagnostic accuracy combining HDWL and BLI (multimodal imaging) was 95.0%, which was significantly higher than that of experts (81.7%, P =0.03) and novices (66.7%, P <0.001). Sensitivity was also higher for CADx (95.6% vs. 61.1% and 55.4%), whereas specificity was higher for experts compared with CADx and novices (95.6% vs. 93.3% and 93.2%). For endoscopists, diagnostic accuracy did not increase when using BASIC, either for experts (intuition 79.5% vs. BASIC 81.7%, P =0.14) or for novices (intuition 66.7% vs. BASIC 66.5%, P =0.95).Conclusion CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of colorectal polyps. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared with intuitive optical diagnosis

Effects of a Personalized Smartphone App on Bowel Preparation Quality: Randomized Controlled Trial

BACKGROUND: Adequate bowel preparation is essential for the visualization of the colonic mucosa during colonoscopy. However, the rate of inadequate bowel preparation is still high, ranging from 18% to 35%; this may lead to a higher risk of missing clinically relevant lesions, procedural difficulties, prolonged procedural time, an increased number of interval colorectal carcinomas, and additional health care costs. OBJECTIVE: The aims of this study are to compare bowel preparation instructions provided via a personalized smartphone app (Prepit, Ferring B V) with regular written instructions for bowel preparation to improve bowel preparation quality and to evaluate patient satisfaction with the bowel preparation procedure. METHODS: Eligible patients scheduled for an outpatient colonoscopy were randomized to a smartphone app group or a control group. Both the groups received identical face-to-face education from a research physician, including instructions about the colonoscopy procedure, diet restrictions, and laxative intake. In addition, the control group received written information, whereas the smartphone app group was instructed to use the smartphone app instead of the written information for the actual steps of the bowel preparation schedule. All patients used bisacodyl and sodium picosulfate with magnesium citrate as laxatives. The quality of bowel preparation was scored using the Boston Bowel Preparation Scale (BBPS) by blinded endoscopists. Patient satisfaction was measured using the Patient Satisfaction Questionnaire-18. RESULTS: A total of 87 patients were included in the smartphone app group and 86 in the control group. The mean total BBPS score was significantly higher in the smartphone app group (mean 8.3, SD 0.9) than in the control group (mean 7.9, SD 1.2; P=.03). The right colon showed a significantly higher bowel preparation score in the smartphone app group (mean 2.7, SD 0.5 vs mean 2.5, SD 0.6; P=.04). No significant differences were observed in segment scores for the mean transverse colon (mean 2.8, SD 0.4 vs mean 2.8, SD 0.4; P=.34) and left colon (mean 2.8, SD 0.4 vs mean 2.6, SD 0.5; P=.07). General patient satisfaction was high for the smartphone app group (mean 4.4, SD 0.7) but showed no significant difference when compared with the control group (mean 4.3, SD 0.8; P=.32). CONCLUSIONS: Our personalized smartphone app significantly improved bowel preparation quality compared with regular written instructions for bowel preparation. In particular, in the right colon, the BBPS score improved, which is of clinical relevance because the right colon is considered more difficult to clean and the polyp detection rate in the right colon improves with improvement of bowel cleansing of the right colon. No further improvement in patient satisfaction was observed compared with patients receiving regular written instructions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03677050; https://clinicaltrials.gov/ct2/show/NCT0367705

Automatic image and text-based description for colorectal polyps using BASIC classification

Colorectal polyps (CRP) are precursor lesions of colorectal cancer (CRC). Correct identification of CRPs during in-vivo colonoscopy is supported by the endoscopist's expertise and medical classification models. A recent developed classification model is the Blue light imaging Adenoma Serrated International Classification (BASIC) which describes the differences between non-neoplastic and neoplastic lesions acquired with blue light imaging (BLI). Computer-aided detection (CADe) and diagnosis (CADx) systems are efficient at visually assisting with medical decisions but fall short at translating decisions into relevant clinical information. The communication between machine and medical expert is of crucial importance to improve diagnosis of CRP during in-vivo procedures. In this work, the combination of a polyp image classification model and a language model is proposed to develop a CADx system that automatically generates text comparable to the human language employed by endoscopists. The developed system generates equivalent sentences as the human-reference and describes CRP images acquired with white light (WL), blue light imaging (BLI) and linked color imaging (LCI). An image feature encoder and a BERT module are employed to build the AI model and an external test set is used to evaluate the results and compute the linguistic metrics. The experimental results show the construction of complete sentences with an established metric scores of BLEU-1 = 0.67, ROUGE-L = 0.83 and METEOR = 0.50. The developed CADx system for automatic CRP image captioning facilitates future advances towards automatic reporting and may help reduce time-consuming histology assessment

Algorithm combining virtual chromoendoscopy features for colorectal polyp classification

Background and study aims Colonoscopy is considered the gold standard for decreasing colorectal cancer incidence and mortality. Optical diagnosis of colorectal polyps (CRPs) is an ongoing challenge in clinical colonoscopy and its accuracy among endoscopists varies widely. Computer-aided diagnosis (CAD) for CRP characterization may help to improve this accuracy. In this study, we investigated the diagnostic accuracy of a novel algorithm for polyp malignancy classification by exploiting the complementary information revealed by three specific modalities. Methods We developed a CAD algorithm for CRP characterization based on high-definition, non-magnified white light (HDWL), Blue light imaging (BLI) and linked color imaging (LCI) still images from routine exams. All CRPs were collected prospectively and classified into benign or premalignant using histopathology as gold standard. Images and data were used to train the CAD algorithm using triplet network architecture. Our training dataset was validated using a threefold cross validation. Results In total 609 colonoscopy images of 203 CRPs of 154 consecutive patients were collected. A total of 174 CRPs were found to be premalignant and 29 were benign. Combining the triplet network features with all three image enhancement modalities resulted in an accuracy of 90.6 %, 89.7 % sensitivity, 96.6 % specificity, a positive predictive value of 99.4 %, and a negative predictive value of 60.9 % for CRP malignancy classification. The classification time for our CAD algorithm was approximately 90 ms per image. Conclusions Our novel approach and algorithm for CRP classification differentiates accurately between benign and premalignant polyps in non-magnified endoscopic images. This is the first algorithm combining three optical modalities (HDWL/BLI/LCI) exploiting the triplet network approach

Evaluation of polypectomy quality indicators of large nonpedunculated colorectal polyps in a nonexpert, bowel cancer screening cohort

Background and Aims: With the introduction of the national bowel cancer screening program, the detection of sessile and flat colonic lesions >= 20 mmin size, defined as large nonpedunculated colorectal polyps (LNPCPs), has increased. The aim of this study was to examine the quality of endoscopic treatment of LNPCPs in the Dutch screening program.Methods: This investigation comprised 2 related, but separate, substudies (1 with a cross-sectional design and 1 with a longitudinal design). The first examined prevalence and characteristics of LNPCPs in data from the national Dutch screening cohort from February 2014 until January 2017. The second, with screening data from 5 endoscopy units in the Southern part of the Netherlands from February 2014 until August 2015, examined performance on important quality indicators (technical and clinical successes, recurrence rate, adverse event rate, and surgery referral rate). All patients were part of the national Dutch screening cohort.Results: In the national cohort, an LNPCP was detected in 8% of participants. Technical and clinical success decreased with increasing LNPCP size, from 93% and 96% in 20- to 29-mm lesions to 85% and 86% in 30- to 39-mm lesions and to 74% and 81% in >= 40-mm lesions (P = 30-mm polyps. Endoscopic resection of large polyps could benefit from additional training, quality monitoring, and centralization either within or between centers

Robust Colorectal Polyp Characterization Using a Hybrid Bayesian Neural Network

Computer-Aided Diagnosis (CADx) systems can play a crucial role as a second opinion for endoscopists to improve the overall optical diagnostic performance of colonoscopies. While such supportive systems hold great potential, optimal clinical implementation is currently impeded, since deep neural network-based systems often tend to overestimate the confidence about their decisions. In other words, these systems are poorly calibrated, and, hence, may assign high prediction scores to samples associated with incorrect model predictions. For the optimal clinical workflow integration and physician-AI collaboration, a reliable CADx system should provide accurate and well-calibrated classification confidence. An important application of these models is characterization of Colorectal polyps (CRPs), that are potential precursor lesions of Colorectal cancer (CRC). An improved optical diagnosis of CRPs during the colonoscopy procedure is essential for an appropriate treatment strategy. In this paper, we incorporate Bayesian variational inference and investigate the performance of a hybrid Bayesian neural network-based CADx system for the characterization of CRPs. Results of conducted experiments demonstrate that this Bayesian variational inference-based approach is capable of quantifying model uncertainty along with calibration confidence. This framework is able to obtain classification accuracy comparable to the deterministic version of the network, while achieving a 24.65% and 9.14% lower Expected Calibration Error (ECE) compared to the uncalibrated and calibrated deterministic network using a postprocessing calibration technique, respectively