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The ninjas
These poems open windows into new worlds populated by robots and witches, talking pandas and giant stags. 'The Ninjas, ' offers funny, haunting, heartbreaking poems, the poet's dazzling lyrical instincts balanced by her stinging wit.
Book
Risk of Cardiovascular Disease Due to General Anesthesia and Neuraxial Anesthesia in Lower-Limb Fracture Patients: A Retrospective Population-Based Cohort Study
The purpose of this study was to analyze the relationship between elevated cardiovascular disease (CVD) risk and type of anesthesia by using the National Health Insurance Research Database (NHIRD) of Taiwan in a one-year follow-up period. We assessed whether general anesthesia (GA) or neuraxial anesthesia (NA) increased CVD occurrence in lower-limb fracture patients. Approximately 1 million patients were randomly sampled from the NHIRD registry. We identified and enrolled 3437 lower-limb fracture patients who had received anesthesia during operations conducted in the period from 2010 to 2012. Next, patients were divided into two groups, namely GA (n = 1504) and NA (n = 1933), based on the anesthetic technique received during surgery. Our results revealed that those receiving GA did not differ in their risk of CVD relative to those receiving NA, adjusted HR = 1.24 (95% CI: 0.80–1.92). Patients who received GA for more than 2 h also did not differ in their risk of CVD relative to those receiving NA for less than 2 h, adjusted HR = 1.43 (95% CI: 0.81–2.50). Moreover, in the GA group (i.e., patients aged ≥65 years and women), no significant difference for the risk of CVD events was observed. In conclusion, in our study, the difference in the risk of CVD between lower-limb fracture patients receiving NA and GA was not statistically significant. The incidence rate of CVD seemed to be more correlated with patients’ underlying characteristics such as old age, comorbidities, or admission to the intensive care unit. Due to the limited sample size in this study, a database which reviews a whole national population will be required to verify our results in the future.
Journal Article
Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all
p
< 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.
Journal Article
Deep Venous Thrombosis and Risk of Consequent Sepsis Event: A Retrospective Nationwide Population-Based Cohort Study
Deep vein thrombosis causes several acute and chronic vessel complications and puts patients at risk of subsequent sepsis development. This unique study aimed to estimate the risk of sepsis development in DVT patients compared with non-DVT patients. This population-based cohort study used records of a longitudinal health insurance database containing two million patients defined in Taiwan’s National Health Insurance Research Database (NHIRD). Our study included patients aged over 20 years with a new diagnosis of DVT with at least two outpatient department visits or an admission between 2001 and 2014. Patients with a diagnosis of sepsis before the index date were excluded. Propensity score matching (PSM) was used to homogenize the baseline characteristics between the two groups. To define the independent risk of the DVT group, a multivariate Cox proportional hazard model was used to estimate the hazard ratios. After PSM, the DVT group (n = 5753) exhibited a higher risk of sepsis (adjusted hazard ratio, aHR, 1.74; 95% CI, 1.59–1.90) compared with non-DVT group (n = 5753). Patients with an increased risk of sepsis were associated with being elderly aged, male, having diabetes, chronic kidney disease, chronic obstructive pulmonary disease, stroke, malignancy, and use of antibiotics. In conclusion, this population-based cohort study demonstrated an increased risk of sepsis in DVT patients compared with non-DVT patients. Thus, early prevention and adequate treatment of DVT is necessary in clinical practice.
Journal Article
Solving the achievement gap : overcoming the structure of school inequality
\"This book examines the cause of the student achievement gap, suggesting that the prevailing emphasis on socioeconomic factors, sociocultural influences, and teacher quality is misplaced. The cause of the achievement gap is not differences in parenting styles, or the economic advantages of middle-class parents, or differences in the quality of teachers. Instead, schools present learning tasks and award grades in ways that inadvertently undermine the self-efficacy, engagement, and effort of low-performing students, causing demoralization and exacerbating differences in achievement that are seen to exist as early as kindergarten. This process systematically maintains and widens initial gaps in achievement that might otherwise be expected to disappear over the K-12 years. Misdiagnosis of the nature of the achievement gap has led to misguided solutions. The author draws upon a range of research studies to support this view and to offer recommendations for improvement.\" -- Publisher's description
Book
A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography
Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.
Journal Article
Shape analysis of the human association pathways
Shape analysis has been widely used in digital image processing and computer vision, but they have not been utilized to compare the structural characteristics of the human association pathways. Here we used shape analysis to derive length, area, volume, and shape metrics from diffusion MRI tractography and utilized them to study the morphology of human association pathways. The reliability analysis showed that shape descriptors achieved moderate to good test-retest reliability. Further analysis on association pathways showed left dominance in the arcuate fasciculus, cingulum, uncinate fasciculus, frontal aslant tract, and right dominance in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The superior longitudinal fasciculus has a mixed lateralization profile with different metrics showing either left or right dominance. The analysis of between-subject variations shows that the overall layout of the association pathways does not variate a lot across subjects, as shown by low between-subject variation in length, span, diameter, and radius. In contrast, the area of the pathway innervation region has a considerable between-subject variation. A follow-up analysis is warranted to thoroughly investigate the nature of population variations and their structure-function correlation.
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Journal Article