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Background: Precise staging of hepatic fibrosis with MRI is necessary as it can assist precision medicine. Computer aided diagnosis (CAD) system with distinguishing radiomics features and radiologists domain knowledge is expected to obtain 5‐grade meta‐analysis of histological data in viral hepatitis (METAVIR) staging. Purpose: This study aims to obtain the precise staging of hepatic fibrosis based on MRI as it predicts the risk of future liver‐related morbidity and the need for treatment, monitoring and surveillance. Based on METAVIR score, fibrosis can be divided into five stages. Most previous researches focus on binary classification, such as cirrhosis versus non‐cirrhosis, early versus advanced fibrosis, and substantial fibrosis or not. In this paper, a comprehensive CAD system TMM is proposed to precisely class hepatic fibrosis into five stages for precision medicine instead of the common binary classification. Methods: We propose a novel hepatic fibrosis staging CAD system TMM which includes three modules, Two‐level Image Statistical Radiomics Feature (TISRF), Monotonic Error Correcting Output Codes (MECOC) and Monotone Multiclassification with Deep Forest (MMDF). TISRF extracts radiomics features for distinguishing different hepatic fibrosis stages. MECOC is proposed to encode monotonic multiclass by making full use of the progressive severity of hepatic fibrosis and increase the fault tolerance and error correction ability. MMDF combines multiple Deep Forest network to ensure the final five‐class classification, which can achieve more precise classification than the common binary classification. The performance of the proposed hepatic fibrosis CAD system is tested on the hepatic data collected from our rabbits models of fibrosis. Results: A total of 140 regions of interest (ROI) are selected from MRI T1W of liver fibrosis models in 35 rabbits with F0(n = 16), F1(n = 28), F2(n = 29), F3(n = 44) and F4(n = 23). The performance is evaluated by five‐fold cross‐validation. TMM can achieve the highest total accuracy of 72.14% for five fibrosis stages compared with other popular classifications. To make a comprehensive comparison, a binary classification experiment have been carried out. Conclusions: T1WI can obtain precise staging of hepatic fibrosis with the help of comprehensive CAD including radiomics features extraction inspired by radiologists, monotonic multiclass according to the severity of hepatic fibrosis, and deep learning classification. [ABSTRACT FROM AUTHOR]

This study presents a methodology for constructing a quasi-geoid model with millimeter-level accuracy over the Shangyu area in China, following the guidelines of the International Association of Geodesy Joint Working Group 2.2.2, known as "The 1 cm geoid experiment". Our approach combines two steps to ensure exceptional accuracy. First, we employ Molodensky's theory to model the gravity field, accounting for non-level surfaces and considering complex terrain effects. Through an exhaustive analysis of these influential factors, we implement a comprehensive suite of applicable formulae within Molodensky's series solution, enabling a thorough assessment of their impacts on height anomalies within the gravimetric quasi-geoid model. Second, we utilize a hybrid method that involves a multi-surface function using the least-squares method and a robust estimation technique. This approach enables the interpolation of quasi-geoid heights by incorporating ellipsoidal and leveling normal heights, as well as gravimetric quasi-geoid data. Through a numerical example, we demonstrate the efficiency of our solution concept, achieving an accuracy of 0.79 cm compared to independent global navigation satellite system (GNSS)/leveling measurements. By developing this methodology, our study contributes to the advancement of geodesy research and provides a valuable methodology for creating highly precise quasi-geoid models in geodetic applications. [ABSTRACT FROM AUTHOR]

To study the anatomical characteristics of the root and root canal system of the mandibular second molars in the population of Gansu province, and to provide theoretical and clinical references for improving the success rate of root canal therapy (RCT) of mandibular second molars. The number of roots and root canals, root canal type and pulp chamber height of mandibular second molars were determined by observing cone‐beam computed tomography (CBCT) images of people living in Gansu. The most common type of mandibular second molars in the Gansu province population was a double root with three root canals (47.55%), followed by a C‐shaped root (35.56%). There were more females than males with a C‐shaped root (p < 0.05). The most common root canal subtype of the C‐type root was C3 (13.91%). Most of the population (77.11%) had bilateral mandibular second molars with symmetrical root canal morphology. With an increase in age, the height of the pulp chamber decreased significantly. The incidence of root canal variation of the mandibular second molars is relatively high in the population of Gansu province. Preoperative examination with CBCT is essential for mandibular second molars that need RCT to avoid root canal treatment failure and decrease the occurrence of postoperative pain as much as possible. [ABSTRACT FROM AUTHOR]