Objective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. Results: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. Conclusion: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses.
Background Enterococcus faecalis (E. faecalis) is frequently isolated from root canals with failed root canal treatments. Due to the strong ability of E. faecalis to resist many often-used antimicrobials, coping with E. faecalis infections remains a challenge. The aim of this study was to investigate the synergistic antibacterial effect of low-dose cetylpyridinium chloride (CPC) and silver ions (Ag+) against E. faecalis in vitro. Methods The minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and the fractional inhibitory concentration index (FICI) were used to confirm the existence of the synergic antibacterial activity between low-dose CPC and Ag+. Colony-forming unit (CFU) counting, time-killing curve and dynamic growth curve were used to evaluate the antimicrobial effects of CPC and Ag+ combinations against planktonic E. faecalis. Four weeks biofilms were treated with drug-contained gels to determine the antimicrobial effect on biofilm-resident E.faecalis, and the integrity of E.faecalis and its biofilms were observed by FE-SEM. CCK-8 assays was used to test the cytotoxicity of CPC and Ag+ combinations on MC3T3-E1 cells. Results The results confirmed the synergistic antibacterial effect of low-dose CPC and Ag+ against both planktonic and 4-week biofilm E. faecalis. After the addition of CPC, the sensitivity of both planktonic and biofilm-resident E. faecalis to Ag+ improved, and the combination showed good biocompatibility on MC3T3-E1 cells. Conclusions Low-dose CPC enhanced the antibacterial ability of Ag+ against both planktonic and biofilm E.faecalis with good biocompatibility. It may be developed into a novel and potent antibacterial agent against E.faecalis, with low toxicity for root canal disinfection or other related medical applications.
Prosthesis loosening after THA is a rather common complication. For DDH patients with Crowe IV, the surgical risk and complexity is significant. THA with S-ROM prosthesis combined with subtrochanteric osteotomy is a common treatment. However, loosening of a modular femoral prosthesis (S-rom) is uncommon in THA and has a very low incidence. With modular prostheses distal prosthesis looseness are rarely reported. Non-union osteotomy is a common complication of subtrochanteric osteotomy. We report three patients with Crowe IV DDH who developed prosthesis loosening following THA with an S-ROM prosthesis and subtrochanteric osteotomy. We addressed the management of these patients and prosthesis loosening as likely underlying causes.
Background:The incidence of osteonecrosis of the femoral head (ONFH) is increasing gradually, rapid and accurate grading of ONFH is critical. The existing Steinberg staging criteria grades ONFH according to the proportion of necrosis area to femoral head area. Purpose: In the clinical practice, the necrosis region and femoral head region are mainly estimated by the observation and experience of doctor. This paper proposes a two-stage segmentation and grading framework, which can be used to segment the femoral head and necrosis, as well as to diagnosis. Methods: The core of the proposed two-stage framework is the multiscale geometric embedded convolutional neural network (MsgeCNN), which integrates geometric information into the training process and accurately segments the femoral head region. Then, the necrosis regions are segmented by the adaptive threshold method taking femoral head as the background. The area and proportion of the two are calculated to determine the grade. Results:The accuracy of the proposed MsgeCNN for femoral head segmentation is 97.73%, sensitivity is 91.17%, specificity is 99.40%, dice score is 93.34%. And the segmentation performance is better than the existing five segmentation algorithms. The diagnostic accuracy of the overall framework is 90.80%. Conclusions: The proposed framework can accurately segment the femoral head region and the necrosis region. The area, proportion, and other pathological information of the framework output provide auxiliary strategies for subsequent clinical treatment.
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