外侧半月板撕裂关节镜术中保留不同半月板层厚膝关节功能及骨性关节炎发生的影响

目的 探讨外侧半月板撕裂关节镜术中保留不同半月板层厚对膝关节功能及骨性关节炎发生的影响.方法 回顾性分析自2013-03-2015-01采用关节镜治疗的50例膝关节外侧半月板撕裂,19例未保留半月板层厚(未保留组),16例部分保留半月板层厚(部分保留组),15例完全保留半月板层厚(完全保留组).比较3组末次随访时膝关节功能Lysholm评分、疼痛VAS评分、Kellgren-Lawrence影像学分级.结果 50例均获得平均22.7(17～32)个月随访.末次随访时,3组Lysholm评分差异有统计学意义(P＜0.05),进一步两两比较,部分保留组、完全保留组Lysholm评分明显高于未保留组,而部分保留组与完全保留组Lysholm评分差异无统计学意义(P＞0.05).3组疼痛VAS评分、Kellgren-Lawrence影像学分级差异无统计学意义(P＞0.05).结论 外侧半月板撕裂关节镜术中保留不同半月板层厚者术后早期膝关节功能恢复更好,并且保留不同层厚半月板对早期膝关节骨性关节炎发生无明显影响.     

Artificial intelligence to diagnose meniscus tears on MRI

Purpose

The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.

Treatment of degenerative meniscal tear with intrameniscal injection of platelets rich plasma

PurposeThe purpose of this retrospective study was to describe our preliminary results of intra-meniscal administration of platelet rich plasma (PRP) in patients with degenerative meniscal tears of the knee.Material and methodTen patients with degenerative meniscal tears according to the Stoller classification and without knee osteoarthritis were included. There were 7 men and 3 women with a mean age of 40.4 ± 13.6 [SD] years (range: 18–59 years). Patients were prospectively assessed at baseline and 3- and 6-months after intra meniscal PRP administration. Evaluation included the knee injury and osteoarthritis outcome score (KOOS), pain visual analog scale, and return to competition and training. MRI follow-up was performed 6 months after PRP administration. Adverse events were recorded.ResultsVolume of injected PRP was standardized to 4.0 mL. Adverse events during PRP administration was moderate pain in 8 patients (8/10; 80%). Mean KOOS total score significantly improved from 56.6 ± 15.7 (SD) to 72.7 ± 18.5 (SD) (P = 0.0007). All six patients practicing sports regularly were able to recover competition or training. In seven patients who underwent MRI follow-up at 6 months, MRI showed stability of the meniscal tears and similar Stoller grades.ConclusionIntra-meniscal administration of PRP under ultrasound guidance directly into meniscal degenerative lesions is feasible and safe. Further randomized controlled studies are needed to definitely confirm the effectiveness of this procedure.     

Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment

In recent years, the advantages of artificial intelligence (AI) in data processing and model analysis have emerged in the medical field, enabled by computer technology developments and the integration of multiple disciplines. The application of AI in the medical field has gradually deepened and broadened. Among them, the development of clinical medicine intelligent decision-making is the fastest. The advantage of clinical medicine intelligent decision-making is to make the diagnosis faster and more accurate on the basis of certain information. Urine detection technologies, such as urine proteomics, urine metabolomics, and urine RNomics, have developed rapidly with the advancements in omics and medical tests. Advances in urine testing have made it possible to obtain a wealth of information from easily accessible urine. However, it has always been a problem to extract effective information from this information and use it. AI technology provides the possibility to process and use the information in urine. AI, combined with urine detection, not only provides new possibilities for precise and individual diagnosis and disease treatment, but also helps promote non-invasive diagnosis and treatment. This article reviews the research and applications of AI combined with urine detection for disease diagnosis and treatment and discusses its existing problems and future development.     

Automatic detection and classification of abnormalities for artificial hearts using a hierarchical self-organizing map

A hierarchical self-organizing map (SOM) has been developed for automatic detection and classification of abnormalities for artificial hearts. The hierarchical SOM has been applied to the monitoring and analysis of an aortic pressure (AoP) signal measured from an adult goat equipped with a total artificial heart. The architecture of the network actually consists of 2 different SOMs. The first SOM clusters the AoP beat patterns in an unsupervised way. Afterward, the outputs of the first SOM combined with the original time-domain features of beat-to-beat data are fed to the second SOM for final classification. Each input vector of the second SOM is associated with a class vector. This class vector is assigned to every node in the second map as an output weight and learned according to Kohonen's learning rule. Some experimental results revealed that a certain abnormality caused by breakage of sensors could be identified and detected correctly and that the change in the state of the circulatory system could be recognized and predicted to some extent.     

Robust fault detection filter design for a class of neutral‐type neural networks with time‐varying discrete and unbounded distributed delays

This paper is concerned with the problem of robust fault detection filter design for a class of neutral‐type neural networks with time‐varying discrete and unbounded distributed delays. A Luenberger‐type observer is designed for monitoring fault. By introducing an appropriate Lyapunov–Krasovskii functional and by using Jensen's inequality techniques to deal with its derivative, a new sufficient condition for the existence of robust fault detection filter is proposed in the form of LMIs with nonlinear constraints. To solve the nonlinear problem, a cone complementarity linearization algorithm is proposed. In addition, several numerical examples are provided to illustrate the applicability of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning

PurposeThe purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN).Materials and methodsThe method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations.ResultsThe final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring.ConclusionThe deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.     

Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI

PurposeThe purpose of this study was to create an algorithm that combines multiple machine-learning techniques to predict the expanded disability status scale (EDSS) score of patients with multiple sclerosis at two years solely based on age, sex and fluid attenuated inversion recovery (FLAIR) MRI data.Materials and methodsOur algorithm combined several complementary predictors: a pure deep learning predictor based on a convolutional neural network (CNN) that learns from the images, as well as classical machine-learning predictors based on random forest regressors and manifold learning trained using the location of lesion load with respect to white matter tracts. The aggregation of the predictors was done through a weighted average taking into account prediction errors for different EDSS ranges. The training dataset consisted of 971 multiple sclerosis patients from the “Observatoire français de la sclérose en plaques” (OFSEP) cohort with initial FLAIR MRI and corresponding EDSS score at two years. A test dataset (475 subjects) was provided without an EDSS score. Ten percent of the training dataset was used for validation.ResultsOur algorithm predicted EDSS score in patients with multiple sclerosis and achieved a MSE = 2.2 with the validation dataset and a MSE = 3 (mean EDSS error = 1.7) with the test dataset.ConclusionOur method predicts two-year clinical disability in patients with multiple sclerosis with a mean EDSS score error of 1.7, using FLAIR sequence and basic patient demographics. This supports the use of our model to predict EDSS score progression. These promising results should be further validated on an external validation cohort.