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General General

Convolutional neural network for automatically segmenting magnetic resonance images of the shoulder joint.

In Computer methods and programs in biomedicine

BACKGROUND : Magnetic resonance imaging (MRI) has been known to replace computed tomography (CT) for bone and skeletal joint examination. The accurate automatic segmentation of bone structure in shoulder MRI is important for the measurement and diagnosis of bone injury and disease. Existing bone segmentation algorithms cannot achieve automatic segmentation without any prior knowledge, and their versatility and accuracy are relatively low. Therefore, an automatic segmentation combining pulse coupled neural network (PCNN) and full convolutional neural networks (FCN) is proposed.

METHODOLOGY : By constructing the block-based AlexNet segmentation model and U-Net-based bone segmentation module, we implemented the humeral segmentation model, articular bone segmentation model, humeral head and articular bone segmentation model synthesis model. We use this four kinds of segmentation models to obtain candidate bone regions, and accurately detect the positions of humerus and articular bone by voting. Finally, we perform an AlexNet segmentation model in the detected bone area in one step to segment accuracy at the pixel level.

RESULTS : The experimental data came from 8 groups of patients in Shengjing Hospital affiliated to China Medical University. The scanning volume of each group is approximately 100 images. Five fold cross-validations and for training were recorded, and five sets of data were carefully separated. After using our technique in the three groups of patients tested, the positive predictive value of dice coefficient (PPV) and the average accuracy of sensitivity were very significant, which reached 0.96±0.02, 0.97±0.02 and 0.94±0.03, respectively.

CONCLUSION : The method used in the experiment in this paper is based on a small amount of patient sample data. The deep learning required for the experiment needs to be performed through 2D medical images. The shoulder segmentation data obtained in this way can be very accurate.

Wang Guangbin, Han Yaxin


Convolutional neural network, Deep learning, Magnetic resonance image, Medical image examination, Orthopedic diagnosis

General General

Pay-for-performance reduces bypassing of health facilities: Evidence from Tanzania.

In Social science & medicine (1982)

Many patients and expectant mothers in low-income countries bypass local health facilities in search of better-quality services. This study examines the impact of a payment-for-performance (P4P) scheme on bypassing practices among expectant women in Tanzania. We expect the P4P intervention to reduce incidences of bypassing by improving the quality of services in local health facilities, thereby reducing the incentive to migrate. We used a difference-in-difference regression model to assess the impact of P4P on bypassing after one year and after three years. In addition, we implemented a machine learning approach to identify factors that predict bypassing. Overall, 38% of women bypassed their local health service provider to deliver in another facility. Our analysis shows that the P4P scheme significantly reduced bypassing. On average, P4P reduced bypassing in the study area by 17% (8 percentage points) over three years. We also identified two main predictors of bypassing - facility type and the distance to the closest hospital. Women are more likely to bypass if their local facility is a dispensary instead of a hospital or a health center. Women are less likely to bypass if they live close to a hospital.

Bezu Sosina, Binyaruka Peter, M├Žstad Ottar, Somville Vincent


Africa, Health financing, Health governance, Health service use, Maternal care, Pay for performance, Tanzania, bypassing

General General

Computational Analysis of Multidimensional Behavioral Alterations After Chronic Social Defeat Stress.

In Biological psychiatry ; h5-index 105.0

BACKGROUND : The study of depression in humans depends on animal models that attempt to mimic specific features of the human syndrome. Most studies focus on one or a few behavioral domains, with time and practical considerations prohibiting a comprehensive evaluation. Although machine learning has enabled unbiased analysis of behavior in animals, this has not yet been applied to animal models of psychiatric disease.

METHODS : We performed chronic social defeat stress (CSDS) in mice and evaluated behavior with PsychoGenics' SmartCube, a high-throughput unbiased automated phenotyping platform that collects >2000 behavioral features based on machine learning. We evaluated group differences at several times post-CSDS and after administration of the antidepressant medication imipramine.

RESULTS : SmartCube analysis after CSDS successfully separated control and defeated-susceptible mice, and defeated-resilient mice more resembled control mice. We observed a potentiation of CSDS effects over time. Treatment of susceptible mice with imipramine induced a 40.2% recovery of the defeated-susceptible phenotype as assessed by SmartCube.

CONCLUSIONS : High-throughput analysis can simultaneously evaluate multiple behavioral alterations in an animal model for the study of depression, which provides a more unbiased and holistic approach to evaluating group differences after CSDS and perhaps can be applied to other mouse models of psychiatric disease.

Lorsch Zachary S, Ambesi-Impiombato Alberto, Zenowich Rebecca, Morganstern Irene, Leahy Emer, Bansal Mukesh, Nestler Eric J, Hanania Taleen


Antidepressants, Behavior, Bioinformatics, Chronic social defeat stress, Depression, Translational models

oncology Oncology

Status and perspectives of biomarker validation for diagnosis, stratification, and treatment.

In Public health

OBJECTIVES : The aim of this study was to discuss the status of and perspective for biomarker validation in view of the challenges imposed on national healthcare systems due to an increasing number of citizens with chronic diseases and new expensive drugs with effects that are sometimes poorly documented. The demand for a paradigm shift toward stratification of patients or even 'personalized medicine' (PM) is rising, and the implementation of such novel strategies has the potential to increase patient outcomes and cost efficiency of treatments. The implementation of PM depends on relevant and reliable biomarkers correlated to disease states, prognosis, or effect of treatment. Beyond biomarkers of disease, personalized prevention strategies (such as individualized nutrition guidance) are likely to depend on novel biomarkers.

STUDY DESIGN : We discuss the current status of the use of biomarkers and the need for standardization and integration of biomarkers based on multi-omics approaches.

METHODS : We present representative cases from laboratory medicine, oncology, and nutrition, where present and emerging biomarkers have or may present opportunities for PM or prevention.

RESULTS : Biomarkers vary greatly in complexity, from single genomic mutations to metagenomic analyses of the composition of the gut microbiota and comprehensive analyses of metabolites, metabolomics. Using biomarkers for decision-making has previously often relied on measurements of single biomolecules. The current development now moves toward the use of multiple biomarkers requiring the use of machine learning or artificial intelligence. Still, the usefulness of biomarkers is often challenged by suboptimal validation, and the discovery of new biomarkers moves much faster than standardization efforts. To reap the potential benefits of personalization of treatment and prevention, healthcare systems and regulatory authorities need to focus on validation and standardization of biomarkers.

CONCLUSION : There is a great public health need for better understanding of the usefulness, but also limitations, of biomarkers among policy makers, clinicians, and scientists, and efforts securing effective validation are key to the future use of novel sets of complex biomarkers.

Skov J, Kristiansen K, Jespersen J, Olesen P


Biomarker, Genomics, Metabolomics, Metagenomics, Multi-omics, Nutrigenomics, Personalized medicine, Prevention

Public Health Public Health

Racialized algorithms for kidney function: Erasing social experience.

In Social science & medicine (1982)

The rise of evidence-based medicine, medical informatics, and genomics --- together with growing enthusiasm for machine learning and other types of algorithms to standardize medical decision-making --- has lent increasing credibility to biomedical knowledge as a guide to the practice of medicine. At the same time, concern over the lack of attention to the underlying assumptions and unintended health consequences of such practices, particularly the widespread use of race-based algorithms, from the simple to the complex, has caught the attention of both physicians and social scientists. Epistemological debates over the meaning of "the social" and "the scientific" are consequential in discussions of race and racism in medicine. In this paper, we examine the socio-scientific processes by which one algorithm that "corrects" for kidney function in African Americans became central to knowledge production about chronic kidney disease (CKD). Correction factors are now used extensively and routinely in clinical laboratories and medical practices throughout the US. Drawing on close textual analysis of the biomedical literature, we use the theoretical frameworks of science and technology studies to critically analyze the initial development of the race-based algorithm, its uptake, and its normalization. We argue that race correction of kidney function is a racialized biomedical practice that contributes to the consolidation of a long-established hierarchy of difference in medicine. Consequentially, correcting for race in the assessment of kidney function masks the complexity of the lived experience of societal neglect that damages health.

Braun Lundy, Wentz Anna, Baker Reuben, Richardson Ellen, Tsai Jennifer


Algorithms, Chronic kidney disease, Estimated glomerular filtration rate, Racialization

Radiology Radiology

Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet.

In Computer methods and programs in biomedicine

BACKGROUND : Prostate cancer is a disease with a high incidence of tumors in men. Due to the long incubation time and insidious condition, early diagnosis is difficult; especially imaging diagnosis is more difficult. In actual clinical practice, the method of manual segmentation by medical experts is mainly used, which is time-consuming and labor-intensive and relies heavily on the experience and ability of medical experts. The rapid, accurate and repeatable segmentation of the prostate area is still a challenging problem. It is important to explore the automated segmentation of prostate images based on the 3D AlexNet network.

METHOD : Taking the medical image of prostate cancer as the entry point, the three-dimensional data is introduced into the deep learning convolutional neural network. This paper proposes a 3D AlexNet method for the automatic segmentation of prostate cancer magnetic resonance images, and the general network ResNet 50, Inception -V4 compares network performance.

RESULTS : Based on the training samples of magnetic resonance images of 500 prostate cancer patients, a set of 3D AlexNet with simple structure and excellent performance was established through adaptive improvement on the basis of classic AlexNet. The accuracy rate was as high as 0.921, the specificity was 0.896, and the sensitivity It is 0.902 and the area under the receiver operating characteristic curve (AUC) is 0.964. The Mean Absolute Distance (MAD) between the segmentation result and the medical expert's gold standard is 0.356 mm, and the Hausdorff distance (HD) is 1.024 mm, the Dice similarity coefficient is 0.9768.

CONCLUSION : The improved 3D AlexNet can automatically complete the structured segmentation of prostate magnetic resonance images. Compared with traditional segmentation methods and depth segmentation methods, the performance of the 3D AlexNet network is superior in terms of training time and parameter amount, or network performance evaluation. Compared with the algorithm, it proves the effectiveness of this method.

Chen Jun, Wan Zhechao, Zhang Jiacheng, Li Wenhua, Chen Yanbing, Li Yuebing, Duan Yue


3D AlexNet, Convolutional Neural Network, Prostate Cancer, Three-dimensional reconstruction