A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis

Background Endoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos. Methods 1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured. Results The AI system detected ER (UCEIS = 1) in WLE videos with 72% sensitivity, 87% specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO = 3), the sensitivity was 79 %, specificity 95%, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80% to 85%). The model s stratification of risk of flare was similar to that of physician-assessed endoscopy scores. Conclusions Our system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment

Specifying and Validating Probabilistic Inputs for Prescriptive Models of Decision Making over Time

Optimization models for making decisions over time in uncertain environments rely on probabilistic inputs, such as scenario trees for stochastic mathematical programs. The quality of model outputs, i.e., the solutions obtained, depends on the quality of these inputs. However, solution quality is rarely assessed in a rigorous way. The connection between validation of model inputs and quality of the resulting solution is not immediate. This chapter discusses some efforts to formulate realistic probabilistic inputs and subsequently validate them in terms of the quality of solutions they produce. These include formulating probabilistic models based on statistical descriptions understandable to decision makers; conducting statistical tests to assess the validity of stochastic process models and their discretization; and conducting re-enactments to assess the quality of the formulation in terms of solution performance against observational data. Studies of long-term capacity expansion in service industries, including electric power, and short-term scheduling of thermal electricity generating units provide motivation and illustrations. The chapter concludes with directions for future research

Thermodynamics-Based Computational Design of Al-Mg-Sc-Zr Alloys

Alloying additions of Sc and Zr raise the yield strength of Al-Mg alloys significantly. We have studied the effects of Sc and Zr on the grain refinement and recrystallization resistance of Al-Mg alloys with the aid of computational alloy thermodynamics. The grain refinement potential has been assessed by Scheil-Gulliver simulations of solidification paths, while the recrystallization resistance (Zener drag) has been assessed by calculation of the precipitation driving forces of the Al(3)Sc and Al(3)Zr intermetallics. Microstructural performance indices have been derived, used to rank several alloy composition variants, and finally select the variant with the best combination of grain refinement and recrystallization resistance. The method can be used, with certain limitations, for a thermodynamics-based design of Al-Mg and other alloy compositions