scholarly journals Bowel ultrasound enhances predictive value based on clinical indicators: a scoring system for moderate-to-severe endoscopic activities in patients with ulcerative colitis

2021 ◽  
Vol 14 ◽  
pp. 175628482110300
Author(s):  
Mengmeng Zhang ◽  
Huimin Zhang ◽  
Qingli Zhu ◽  
Xiaoyin Bai ◽  
Qingyang Zhou ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Predictive Models ◽  
Scoring System ◽  
External Validation ◽  
Model Performance ◽  
Stool Frequency ◽  
Clinical Indicators ◽  
Colonic Wall ◽  
Discrimination Ability ◽  
Bowel Ultrasound

Background and Aim: The aim was to assess non-invasive factors among clinical features, laboratory, and bowel ultrasound (BUS) characteristics and to develop a scoring system to predict endoscopic activities for ulcerative colitis (UC) patients. Methods: We performed a retrospective study collecting UC patients between January 2015 to September 2020. Logistic regression was performed to predict moderate-to-severe endoscopic activities, defined as endoscopic Mayo score ⩾2. Model performance was described with discrimination and calibration ability and validated by internal and external methods. Results: A total of 103 and 29 patients were enrolled in the modeling and validation groups, respectively. Stool frequency ⩾5 times/day, hematochezia, erythrocyte sedimentation rate (ESR), and colonic wall flow in BUS were included into two predictive models for endoscopic activities, both with good discrimination ability [Area under curve (AUC) 0.879 and 0.882, p  < 0.001] and a sensitivity of 76.7% and specificity of 92.3%, which showed an adequate calibration ability by using the Hosmer–Lemeshow test ( p = 0.14 and 0.07). The external validation displayed consistent results with the above mentioned. Nomograms were also established for these models. Conclusion: We developed predictive models for endoscopic disease activities by using noninvasive factors based on stool frequency, hematochezia, ESR, and colonic wall blood flow in BUS. These models performed well in the internal and external validation.

scholarly journals Milan Ultrasound Criteria are accurate in assessing disease activity in ulcerative colitis: External validation

2020 ◽  
pp. 205064062098020
Author(s):  
Mariangela Allocca ◽  
Elisabetta Filippi ◽  
Andrea Costantino ◽  
Stefanos Bonovas ◽  
Gionata Fiorino ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Disease Activity ◽  
External Validation ◽  
Area Under The Curve ◽  
Complete Agreement ◽  
Tertiary Referral ◽  
Endoscopic Remission ◽  
Inflammatory Bowel ◽  
Disease Unit ◽  
Bowel Ultrasound

Introduction The aim of this study was to provide an external validation of bowel ultrasound (US) predictors of activity in ulcerative colitis (UC) and quantitative Milan Ultrasound Criteria (MUC). Methods Forty-three consecutive patients with UC (16 in endoscopic remission and 27 with endoscopic activity) underwent bowel US and colonoscopy in a tertiary referral inflammatory bowel disease unit. Results A MUC score >6.2 discriminated patients with active versus non-active UC with a sensitivity of 0.85 (95% confidence interval (CI) 0.66‒0.96), specificity of 0.94 (95% CI 0.70‒0.99) and an area under the curve of 0.902 (95% CI 0.772‒0.971) in complete agreement with the derivation study. Conclusion The external validation of MUC confirms that it is an accurate tool for assessing disease activity in patients with UC.

CO2 ULTRASONOGRAPHIC ASSESSMENT OF COLONIC WALL IN PEDIATRIC ULCERATIVE COLITIS: COMPARATIVE STUDY WITH ILEO-COLONOSCOPY

2012 ◽  
Vol 44 ◽  
pp. S241-S242
Author(s):  
F. Civitelli ◽  
G. Di Nardo ◽  
S. Oliva ◽  
F. Nuti ◽  
M. Aloi ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Comparative Study ◽  
Colonic Wall ◽  
Ultrasonographic Assessment

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1647
Author(s):  
Anna Kaczmarek ◽  
Małgorzata Muzolf-Panek
Keyword(s):  
Plant Extracts ◽  
Predictive Models ◽  
External Validation ◽  
Thiol Group ◽  
Chicken Meat ◽  
Validation Dataset ◽  
Heat Treated ◽  
Different Temperatures ◽  
Meat Samples ◽  
Network Approaches

The aim of the study was to develop predictive models of thiol group (SH) level changes in minced raw and heat-treated chicken meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary, and thyme) during storage at different temperatures. Meat samples with extract addition were stored under various temperatures (4, 8, 12, 16, and 20 °C). SH changes were measured spectrophotometrically using Ellman’s reagent. Samples stored at 12 °C were used as the external validation dataset. SH content decreased with storage time and temperature. The dependence of SH changes on temperature was adequately modeled by the Arrhenius equation with average high R2 coefficients for raw meat (R2 = 0.951) and heat-treated meat (R2 = 0.968). Kinetic models and artificial neural networks (ANNs) were used to build the predictive models of thiol group decay during meat storage. The obtained results demonstrate that both kinetic Arrhenius (R2 = 0.853 and 0.872 for raw and cooked meat, respectively) and ANN (R2 = 0.803) models can predict thiol group changes in raw and cooked ground chicken meat during storage.

scholarly journals External validation of the R.I.R.S. scoring system to predict stone-free rate after retrograde intrarenal surgery

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Cong Wang ◽  
ShouTong Wang ◽  
Xuemei Wang ◽  
Jun Lu
Keyword(s):  
Scoring System ◽  
Kidney Stone ◽  
Medical Center ◽  
External Validation ◽  
Retrograde Intrarenal Surgery ◽  
Stone Burden ◽  
Stone Free Rate ◽  
Stone Density ◽  
Free Rate ◽  
Free Status

Abstract Background The R.I.R.S. scoring system is defined as a novel and straightforward scoring system that uses the main parameters (kidney stone density, inferior pole stones, stone burden, and renal infundibular length) to identify most appropriate patients for retrograde intrarenal surgery (RIRS). We strived to evaluate the accuracy of the R.I.R.S. scoring system in predicting the stone-free rate (SFR) after RIRS. Methods In our medical center, we retrospectively analyzed charts of patients who had, between September 2018 and December 2019, been treated by RIRS for kidney stones. A total of 147 patients were enrolled in the study. Parameters were measured for each of the four specified variables. Results Stone-free status was achieved in 105 patients (71.43%), and 42 patients had one or more residual fragments (28.57%). Differences in stone characteristics, including renal infundibulopelvic angle, renal infundibular length, lower pole stone, kidney stone density, and stone burden were statistically significant in patients whether RIRS achieved stone-free status or not (P < 0.001, P: 0.005, P < 0.001, P < 0.001, P: 0.003, respectively). R.I.R.S. scores were significantly lower in patients treated successfully with RIRS than patients in which RIRS failed (P < 0.001). Binary logistic regression analyses revealed that R.I.R.S. scores were independent factors affecting RIRS success (P = 0.033). The area under the curve of the R.I.R.S. scoring system was 0.737. Conclusions Our study retrospectively validates that the R.I.R.S. scoring system is associated with SFR after RIRS in the treatment of renal stones, and can predict accurately.

scholarly journals OC154: Prospective external validation of an ultrasound scoring system to differentiate benign from malignant masses in specific subgroups of adnexal tumors

2008 ◽  
Vol 32 (3) ◽  
pp. 293-293
Author(s):  
L. Valentin ◽  
L. Ameye ◽  
R. Fruscio ◽  
C. Van Holsbeke ◽  
A. Czekierdowski ◽  
...  
Keyword(s):  
Scoring System ◽  
External Validation ◽  
Adnexal Tumors

scholarly journals Towards scalable and reusable predictive models for cyber twins in manufacturing systems

2021 ◽  
Author(s):  
Cinzia Giannetti ◽  
Aniekan Essien
Keyword(s):  
Data Streams ◽  
Predictive Models ◽  
Manufacturing Systems ◽  
Model Building ◽  
Model Performance ◽  
Cyberphysical Systems ◽  
Pragmatic Approach ◽  
Analyse Data ◽  
Fully Connected ◽  
Smart Factories

AbstractSmart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories.

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