Dataset Information

Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50.

ABSTRACT: Capsule endoscopy has been widely used as a non-invasive diagnostic tool for small or large intestinal lesions. In recent years, automated lesion detection systems using machine learning have been devised. This study aimed to develop an automated system for capsule endoscopic severity in patients with ulcerative colitis along the entire length of the colon using ResNet50. Capsule endoscopy videos from patients with ulcerative colitis were collected prospectively. Each single examination video file was partitioned into four segments: the cecum and ascending colon, transverse colon, descending and sigmoid colon, and rectum. Fifty still pictures (576 × 576 pixels) were extracted from each partitioned video. A patch (128 × 128 pixels) was trimmed from the still picture at every 32-pixel-strides. A total of 739,021 patch images were manually classified into six categories: 0) Mayo endoscopic subscore (MES) 0, 1) MES1, 2) MES2, 3) MES3, 4) inadequate quality for evaluation, and 5) ileal mucosa. ResNet50, a deep learning framework, was trained using 483,644 datasets and validated using 255,377 independent datasets. In total, 31 capsule endoscopy videos from 22 patients were collected. The accuracy rates of the training and validation datasets were 0.992 and 0.973, respectively. An automated evaluation system for the capsule endoscopic severity of ulcerative colitis was developed. This could be a useful tool for assessing topographic disease activity, thus decreasing the burden of image interpretation on endoscopists.

SUBMITTER: Higuchi N

PROVIDER: S-EPMC9187078 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50.

Higuchi Naoki N Hiraga Hiroto H Sasaki Yoshihiro Y Hiraga Noriko N Igarashi Shohei S Hasui Keisuke K Ogasawara Kohei K Maeda Takato T Murai Yasuhisa Y Tatsuta Tetsuya T Kikuchi Hidezumi H Chinda Daisuke D Mikami Tatsuya T Matsuzaka Masashi M Sakuraba Hirotake H Fukuda Shinsaku S

PloS one 20220610 6

Capsule endoscopy has been widely used as a non-invasive diagnostic tool for small or large intestinal lesions. In recent years, automated lesion detection systems using machine learning have been devised. This study aimed to develop an automated system for capsule endoscopic severity in patients with ulcerative colitis along the entire length of the colon using ResNet50. Capsule endoscopy videos from patients with ulcerative colitis were collected prospectively. Each single examination video fi ...[more]

PMID: 35687553

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Project description:BackgroundVariability in endoscopic assessment necessitates rigorous investigation of descriptors for scoring severity of ulcerative colitis (UC).ObjectiveTo evaluate variation in the overall endoscopic assessment of severity, the intra- and interindividual variation of descriptive terms and to create an Ulcerative Colitis Endoscopic Index of Severity which could be validated.DesignA two-phase study used a library of 670 video sigmoidoscopies from patients with Mayo Clinic scores 0-11, supplemented by 10 videos from five people without UC and five hospitalised patients with acute severe UC. In phase 1, each of 10 investigators viewed 16/24 videos to assess agreement on the Baron score with a central reader and agreed definitions of 10 endoscopic descriptors. In phase 2, each of 30 different investigators rated 25/60 different videos for the descriptors and assessed overall severity on a 0-100 visual analogue scale. κ Statistics tested inter- and intraobserver variability for each descriptor. A general linear mixed regression model based on logit link and β distribution of variance was used to predict overall endoscopic severity from descriptors.ResultsThere was 76% agreement for 'severe', but 27% agreement for 'normal' appearances between phase I investigators and the central reader. In phase 2, weighted κ values ranged from 0.34 to 0.65 and 0.30 to 0.45 within and between observers for the 10 descriptors. The final model incorporated vascular pattern, (normal/patchy/complete obliteration) bleeding (none/mucosal/luminal mild/luminal moderate or severe), erosions and ulcers (none/erosions/superficial/deep), each with precise definitions, which explained 90% of the variance (pR(2), Akaike Information Criterion) in the overall assessment of endoscopic severity, predictions varying from 4 to 93 on a 100-point scale (from normal to worst endoscopic severity).ConclusionThe Ulcerative Colitis Endoscopic Index of Severity accurately predicts overall assessment of endoscopic severity of UC. Validity and responsiveness need further testing before it can be applied as an outcome measure in clinical trials or clinical practice.

Project description:IntroductionThe Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading.MethodsHere we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning-based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis.Results and conclusionOur evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation.Plain language summaryPatient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

Project description:BackgroundThe significance of endoscopic evaluation in the diagnosis and management of ulcerative colitis (UC) has been widely recognized. Over the years, scholars have established several endoscopic scores. Herein, we assessed the clinical application value of the Mayo Endoscopic Subscore (Mayo ES), the Degree of Ulcerative Colitis Burden of Luminal Inflammation (DUBLIN) score, and the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score in UC patients, by comparing their correlation with disease activity and their predictive potential for treatment response and clinical outcomes.MethodsUC patients hospitalized from September 2015 to September 2019 were retrospectively analysed. We employed Spearman's rank correlation coefficient to assess the linear association of the assessed endoscopic scores with the clinical parameters. The receiver-operating characteristic curve was applied to evaluate the predictive capabilities of the endoscopic scores for treatment escalation and 1-year readmission.ResultsA total of 178 patients were enrolled; most of them (82%) suffered moderate or severe colitis. Among them, 48 (27%) patients received treatment escalation and 59 (33%) were readmitted within 1 year. The DUBLIN and UCEIS scores demonstrated higher correlations with clinical parameters than the Mayo ES. The DUBLIN scores significantly differed between patients with mild, moderate, and severe colitis (all P < 0.001). The UCEIS scores demonstrated the best predictabilities for treatment escalation and 1-year readmission with an area under the curve of 0.88 and 0.75, respectively. Compared to the UCEIS and DUBLIN scores, the predictive capabilities of the Mayo ES for treatment escalation (both P < 0.001) and 1-year readmission (P < 0.001 and P = 0.002, respectively) were lower. The UCEIS scores exhibited a significant difference between the steroid-responsive group and the steroid-dependent or steroid-refractory group (both P < 0.001), while no significant differences in the Mayo ES and DUBLIN scores were found among the three groups (both P > 0.05).ConclusionThis study demonstrates that both the DUBLIN and UCEIS scores outperform the Mayo ES in assessing disease severity and predicting treatment response and clinical outcomes in UC patients.

Project description:BackgroundAt present, many indicators reflect the clinical disease activity of ulcerative colitis (UC). However, commonly used inflammatory markers do not show good utility for indicating endoscopic disease activity. The purpose of this study was to evaluate high sensitivity C-reactive protein (hs-CRP), C-reactive protein to albumin ratio (CAR), inflammatory markers, and complete blood count (CBC) related parameters in patients with UC as simple, non-invasive, and independent markers of endoscopic activity (EA).MethodsWe retrospectively collected extensive data from the hospital medical records of 386 patients who presented with UC to the First Affiliated Hospital of Xinjiang Medical University (Urumqi, China) from 2018 to 2022 January. The Mayo endoscopic score (MES) was used to evaluate endoscopic disease activity. All included patients were defined as the MES-All group; those with extensive colitis (E3) were defined as the MES-E3 group. Demographics, laboratory parameters, endoscopic results, the extent of disease, and drug history were recorded and analyzed.ResultsFor patients in the MES-All or MES-E3 group, hs-CRP, CAR, neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR) were significantly higher in EA UC patients than in those with mucosal healing. The mean platelet volume (MPV) and lymphocyte to monocyte ratio were significantly lower in active disease than in the patient's remission (p < 0.001). ROC analysis showed that in the MES-All and MES-E3 groups, the cutoff values of hs-CRP activity under endoscopy were 5.32 mg/L (AUC 0.850, sensitivity 77.6%, specificity 81.9%) and 5.16 mg/L (AUC 0.902, sensitivity 86.9%, specificity 85.4%) respectively, and the cutoff values of CAR were 0.14 (AUC 0.853, sensitivity 76.8%, specificity 84.8%) and 0.18 (AUC 0.904, sensitivity 81.8%, specificity 89.6%) respectively. Multivariate logistic regression analysis showed that hs-CRP, CAR, NLR, and PLR identified UC EA, while decreased MPV reflected inflammatory activity in the UC mucosa.ConclusionEspecially in patients with extensive colitis, hs-CRP and CAR are closely related to EA and show a higher diagnostic value compared to the related CBC parameters. The aforementioned indicators are simple and non-invasive independent markers that reflect the EA in UC.

Dataset Information

Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50.

Publications

Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50.

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