BioMedical Engineering OnLine最新文献

Diseases of the esophagus affect its function and often lead to replacement of long sections of the organ. Current healing methods involve the use of bioscaffolds processed from other animal models. Although the properties of these animal models are not exactly the same as those of the human esophagus, they nevertheless present a reasonable means of assessing the biomechanical properties of the esophageal tissue. Besides, sheep bear many similarities physiologically to humans and they also suffer from same diseases as humans. The morphology of their esophagus is also comparable to that of humans. Thus, in the study, an ovine esophagus was studied. Studies on the planar biaxial tests of the gross esophageal anatomy are limited. The composite nature of the gross anatomy of the esophagus makes the application of structure-based models such as Holzapfel-type models very difficult. In current studies the tissue is therefore often separated into specific layers with substantial collagen content. The effects of adipose tissue and other non-collagenous tissue often make the mechanical behavior of the esophagus widely diverse and unpredictable using deterministic structure-based models. Thus, it may be very difficult to predict its mechanical behavior. In the study, an NARX neural network was used to predict the stress-strain response of the gross anatomy of the ovine esophagus. The results show that the NARX model was able to achieve a correlation above 99.9% within a fitting error margin of 16%. Therefore, the use of artificial neural networks may provide a more accurate way of predicting the biaxial stress-strain response of the esophageal tissue, and lead to further improvements in the design and development of synthetic replacement materials for esophageal tissue.

Background: Developmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center-edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies.

Methods: This study employed a U-Net-based CNN model to automate the segmentation of hip MRI images in children. The segmentation process was validated using a leave-one-out method during training. Subsequently, the segmented hip joint images were utilized in clinical settings to perform automated measurements of key angles: AI, femoral neck angle (FNA), and CEA. This automated approach aimed to replace manual measurements and provide an objective reference for clinical assessments.

Results: The U-Net-based network demonstrates high effectiveness in hip segmentation compared to manual radiologist segmentations. In test data, it achieves average DSC values of 0.9109 (acetabulum) and 0.9244 (proximal femur), with a 91.76% segmentation success rate. The average ASD values are 0.3160 mm (acetabulum) and 0.6395 mm (proximal femur) in test data, with Ground Truth (GT) edge points and predicted segmentation maps having a mean distance of less than 1 mm. Using automated segmentation models for clinical hip angle measurements (CEA, AI, FNA) shows no statistical difference compared to manual measurements (p > 0.05).

Conclusion: Utilizing U-Net-based image segmentation and automated measurement of morphological parameters significantly enhances the accuracy and efficiency of DDH assessment. These methods improve precision in automatic measurements and provide an objective basis for clinical diagnosis and treatment of DDH.

Objective: This retrospective aims to develop a comprehensive predictive model based on CT radiomic features and clinical parameters, facilitating early preoperative diagnosis of pyonephrosis.

Methods: Clinical and radiological data from 311 patients treated for upper urinary tract stones with obstructive pyelohydronephrosis, between January 2018 and May 2023, were retrospectively collected. Univariate and multivariate logistic regression analyses were conducted on clinical data to identify independent risk factors for pyonephrosis. A clinical model was developed using logistic regression. The 3D Slicer software was employed to manually delineate the region of interest (ROI) in the preoperative CT images, corresponding to the area of pyelohydronephrosis, for feature extraction. The optimal radiomic features were selected to construct radiomic models and calculate the radiomic score (Radscore). Subsequently, a combined clinical-radiomic model-the nomogram-was established by integrating the Radscore with independent risk factors.

Results: Univariate and multivariate logistic regression analyses identified cystatin C, Hounsfield Unit (HU) of pyonephrosis, history of ipsilateral urological surgery, and positive urine culture as independent risk factors for pyonephrosis (P < 0.05). Fourteen optimal radiomic features were selected from CT images to construct four radiomic models, with the Naive Bayes model demonstrating the best predictive performance in both training and validation sets. In the training set, the AUCs for the clinical model, radiomic model, and nomogram were 0.902, 0.939, and 0.991, respectively; in the validation set, they were 0.843, 0.874, and 0.959. Both calibration and decision curves showed good agreement between the predicted probabilities of the nomogram and the actual occurrences.

Conclusion: The nomogram, constructed from CT radiomic features and clinical variables, provides an effective non-invasive predictive tool for pyonephrosis, surpassing both clinical and radiomic models.