Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology

Electronic checklists improve referral letters in gastroenterology: a randomized vignette survey

Objective: Investigate whether gastroenterologists rate the quality of referral letters higher if electronic dynamic checklist items are added to a standard free-text referral letter. Assess how this affects the gastroenterologists’ assessment of the patient’s need for healthcare and the agreement between their assessments. Intervention: Between June 2015 and January 2016, participants were recruited through an open invitation to all members of the Norwegian Society of Gastroenterology. They were asked to rate 16 referral letters (vignettes) in a web interface: eight letters in free text following a general template and eight letters based on a general referral template combined with diagnosis-specific checklist items. The study was completed in two subsequent rounds ≥3 months apart. Main Outcome Measures: Quality of referral letters assessed on a rating scale from 0 to 10. Agreement in the referral assessment and accuracy of the selection of correct preliminary diagnosis and appropriate work-up. Results: The mean quality assesses on the rating scale was 7.0 (95% confidence interval [CI] 6.8–7.2) for all letters combined (n = 511), 6.5(CI 6.2–6.8) for the free-text referrals (n = 256) and 7.5 (CI 7.3–7.7) for the checklist referrals (n = 255) (P < 0.001, paired t-test). No difference was observed in the triage of the patients, but fewer gastroenterologists felt the need to collect additional information about the patients in the checklist group. Conclusion: Checklist items may ease the assessment of the referrals for gastroenterologists. We were not able to show that checklists significantly influence the management of patients

Assessment of the effect of an Interactive Dynamic Referral Interface (IDRI) on the quality of referral letters from general practitioners to gastroenterologists: a randomised cross-over vignette trial.

Objectives: We evaluated whether interactive, electronic, dynamic, diagnose-specific checklists improve the quality of referral letters in gastroenterology and assessed the general practitioners’ (GPs’) acceptance of the checklists. Intervention: The GPs participated in the trial and were asked to refer eight clinical vignettes in an internet-based electronic health record simulator. A referral support, consisting of dynamic diagnose-specific checklists, was created for the generation of referral letters to gastroenterologists. The GPs were randomised to refer the eight vignettes with or without the checklists. After a minimum of 3 months, they repeated the referral process with the alternative method. Main outcome measures Difference in quality of the referral letters between referrals with and without checklists, measured with an objective Thirty Point Score (TPS). Difference: in variance in the quality of the referral letters and GPs’ acceptance of the electronic dynamic user interface. Results: The mean TPS was 15.2 (95% CI 13.2 to 16.3) and 22.0 (95% CI 20.6 to 22.8) comparing referrals without and with checklist assistance (p<0.001), respectively. The coefficient of variance was 23.3% for the checklist group and 39.6% for the non-checklist group. Two-thirds (16/24) of the GPs thought they had included more relevant information in the referrals with checklists, and considered implementing this type of checklists in their clinical practices, if available. Conclusions: Dynamic, diagnose-specific checklists improved the quality of referral letters significantly and reduced the variance of the TPS, indicating a more uniform quality when checklists were used. The GPs were generally positive to the checklists

Efficient disease detection in gastrointestinal videos – global features versus neural networks

Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-to-end multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multi-class detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases

Detection of cancers and advanced adenomas in asymptomatic participants in colorectal cancer screening: a cross-sectional study

Objectives: To assess detection rates for colorectal cancer (CRC) and advanced adenomas in asymptomatic CRC screening participants and bowel symptoms in association with CRC and advanced adenoma. Design: Cross-sectional study. Setting: Two screening centres. Participants: 42 554 men and women, aged 50-74 years, participating in a randomised CRC screening trial. 36 059 participants underwent a sigmoidoscopy (and follow-up colonoscopy if positive sigmoidoscopy) and 6495 underwent a colonoscopy after a positive faecal immunochemical test (FIT). Primary and secondary outcome measures: Proportion of asymptomatic participants diagnosed with CRC or advanced adenomas. Prevalence of bowel symptoms (rectal bleeding, change in bowel habits, diarrhoea, constipation, bloating, alternating bowel habits, general symptoms, other bowel symptoms) recorded by the endoscopist and their association with CRC and advanced adenomas. Results: Among sigmoidoscopy participants, 7336 (20.3%) reported at least one symptom. 120 (60%) out of 200 individuals with screen-detected CRC and 1301 (76.5%) out of 1700 with advanced adenoma were asymptomatic. Rectal bleeding was associated with detection of CRC and advanced adenoma (OR 4.3, 95% CI 3.1 to 6.1 and 1.8, 95% CI 1.5 to 2.1, respectively), while change in bowel habits only with CRC detection (OR 3.8, 95% CI 2.4 to 6.1). Among the FIT positives, 2173 (33.5%) reported at least one symptom. Out of 299 individuals with screen-detected CRC and 1639 with advanced adenoma, 167 (55.9%) and 1 175 (71.7%) were asymptomatic, respectively. Detection of CRC was associated with rectal bleeding (OR 1.8, 95% CI 1.4 to 2.3), change in bowel habits (OR 2.2, 95% CI 1.4 to 3.5) and abdominal pain (OR 1.8, 95% CI 1.2 to 2.7). Conclusions: Some bowel symptoms increased the likelihood of being diagnosed with CRC or advanced adenoma. However, the majority of individuals with these findings were asymptomatic. Asymptomatic individuals should be encouraged to participate in CRC screening

Dissecting deep neural networks for better medical image classification and classification understanding

Neural networks, in the context of deep learning, show much promise in becoming an important tool with the purpose assisting medical doctors in disease detection during patient examinations. However, the current state of deep learning is something of a "black box", making it very difficult to understand what internal processes lead to a given result. This is not only true for non-technical users but among experts as well. This lack of understanding has led to hesitation in the implementation of these methods among mission-critical fields, with many putting interpretability in front of actual performance. Motivated by increasing the acceptance and trust of these methods, and to make qualified decisions, we present a system that allows for the partial opening of this black box. This includes an investigation on what the neural network sees when making a prediction, to both, improve algorithmic understanding, and to gain intuition into what pre-processing steps may lead to better image classification performance. Furthermore, a significant part of a medical expert's time is spent preparing reports after medical examinations, and if we already have a system for dissecting the analysis done by the network, the same tool can be used for automatic examination documentation through content suggestions. In this paper, we present a system that can look into the layers of a deep neural network and present the network's decision in a way that that medical doctors may understand. Furthermore, we present and discuss how this information can possibly be used for automatic reporting. Our initial results are very promising

First quality score for referral letters in gastroenterology—a validation study

Objective To create and validate an objective and reliable score to assess referral quality in gastroenterology. Design An observational multicentre study. Setting and participants 25 gastroenterologists participated in selecting variables for a Thirty Point Score (TPS) for quality assessment of referrals to gastroenterology specialist healthcare for 9 common indications. From May to September 2014, 7 hospitals from the South-Eastern Norway Regional Health Authority participated in collecting and scoring 327 referrals to a gastroenterologist. Main outcome measure Correlation between the TPS and a visual analogue scale (VAS) for referral quality. Results The 327 referrals had an average TPS of 13.2 (range 1–25) and an average VAS of 4.7 (range 0.2–9.5). The reliability of the score was excellent, with an intra-rater intraclass correlation coefficient (ICC) of 0.87 and inter-rater ICC of 0.91. The overall correlation between the TPS and the VAS was moderate (r=0.42), and ranged from fair to substantial for the various indications. Mean agreement was good (ICC=0.47, 95% CI (0.34 to 0.57)), ranging from poor to good. Conclusions The TPS is reliable, objective and shows good agreement with the subjective VAS. The score may be a useful tool for assessing referral quality in gastroenterology, particularly important when evaluating the effect of interventions to improve referral quality

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine