RESUMEN
Ultrasound is widely used for tendon assessment due to its safety, affordability, and portability, but its subjective nature poses challenges. This study aimed to develop a new quantitative analysis tool based on artificial intelligence to identify statistical patterns of healthy and pathological tendons. Furthermore, we aimed to validate this new tool by comparing it to experts' subjective assessments. A pilot database including healthy controls and patients with patellar tendinopathy was constructed, involving 14 participants with asymptomatic (n = 7) and symptomatic (n = 7) patellar tendons. Ultrasonographic images were assessed twice, utilizing both the new quantitative tool and the subjective scoring method applied by an expert across five regions of interest. The database contained 61 variables per image. The robustness of the clinical and quantitative assessments was tested via reliability analyses. Lastly, the prediction accuracy of the quantitative features was tested via cross-validated generalized linear mixed-effects logistic regressions. These analyses showed high reliability for quantitative variables related to "Bone" and "Quality", with ICCs above 0.75. The ICCs for "Edges" and "Thickness" varied but mostly exceeded 0.75. The results of this study show that certain quantitative variables are capable of predicting an expert's subjective assessment with generally high cross-validated AUC scores. A new quantitative tool for the ultrasonographic assessment of the tendon was designed. This system is shown to be a reliable and valid method for evaluating the patellar tendon structure.
RESUMEN
One of the reasons for the limited applicability of predictive water quality models is the lack of data from monitoring control stations that are required as input. In this context, the main novelty of the present work is the recovery of information on the state variables present in a water quality model through measured data at a target downstream location. The reconstruction of the upstream boundary condition is the goal of the present work. For this purpose, an adjoint-state method is developed to find the sensitivities of the functional with respect to variations on the upstream boundary conditions of the model. The resolution of both forward and backward problems ensures strong, accurate and reliable solutions in both steady state and unsteady scenarios. The different cases demonstrate that the method is able to reconstruct any observed distribution with little computational effort, including the heat balance with all its external inputs.
