MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.

BACKGROUND: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. RESULTS: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. CONCLUSIONS: Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans

Augmenting Ear Accessories for Facial Gesture Input Using Infrared Distance Sensor Array

Simple hands-free input methods using ear accessories have been proposed to broaden the range of scenarios in which information devices can be operated without hands. Although many previous studies use canal-type earphones, few studies focused on the following two points: (1) A method applicable to ear accessories other than canal-type earphones. (2) A method enabling various ear accessories with different styles to have the same hands-free input function. To realize these two points, this study proposes a method to recognize the user’s facial gesture using an infrared distance sensor attached to the ear accessory. The proposed method detects skin movement around the ear and face, which differs for each facial expression gesture. We created a prototype system for three ear accessories for the root of the ear, earlobe, and tragus. The evaluation results for nine gestures and 10 subjects showed that the F-value of each device was 0.95 or more, and the F-value of the pattern combining multiple devices was 0.99 or more, which showed the feasibility of the proposed method. Although many ear accessories could not interact with information devices, our findings enable various ear accessories with different styles to have eye-free and hands-free input ability based on facial gestures

Augmenting Ear Accessories for Facial Gesture Input Using Infrared Distance Sensor Array

Simple hands-free input methods using ear accessories have been proposed to broaden the range of scenarios in which information devices can be operated without hands. Although many previous studies use canal-type earphones, few studies focused on the following two points: (1) A method applicable to ear accessories other than canal-type earphones. (2) A method enabling various ear accessories with different styles to have the same hands-free input function. To realize these two points, this study proposes a method to recognize the user’s facial gesture using an infrared distance sensor attached to the ear accessory. The proposed method detects skin movement around the ear and face, which differs for each facial expression gesture. We created a prototype system for three ear accessories for the root of the ear, earlobe, and tragus. The evaluation results for nine gestures and 10 subjects showed that the F-value of each device was 0.95 or more, and the F-value of the pattern combining multiple devices was 0.99 or more, which showed the feasibility of the proposed method. Although many ear accessories could not interact with information devices, our findings enable various ear accessories with different styles to have eye-free and hands-free input ability based on facial gestures

Clinical Significance of MicroRNAs in Patients with Sepsis: Protocol for a Systematic Review and Meta-Analysis

Sepsis is a dysregulated immune response that leads to organ dysfunction and has high mortality rates despite recent therapeutic advancements. Accurate diagnosis and risk stratification are important for effective sepsis treatment; however, no decisive diagnostic or prognostic biomarkers are currently available. To understand whether microRNA (miRNA) might be useful biomarkers of sepsis, we aim to assess the diagnostic and prognostic accuracy of three miRNAs (122, 150, and 223) in sepsis patients via a meta-analysis of relevant published data. We will search electronic bibliographic databases (MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials) for pertinent retrospective and prospective studies in October 2019. Two reviewers will evaluate the collected titles, abstracts, and full articles, and extract the data. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. If feasible, we will use bivariate random effects and hierarchical summary receiver operating characteristic (ROC) models to estimate summary ROCs, pooled sensitivity and specificity values, and the corresponding 95% confidence intervals. We will evaluate heterogeneity via clinical and methodological subgroup and sensitivity analyses. This systematic review will clarify the diagnostic and prognostic accuracy of select miRNAs in sepsis. It may also identify knowledge gaps in sepsis’ diagnosis and prognosis

Computer-Aided Volumetry of Pulmonary Nodules Exhibiting Ground-Glass Opacity at MDCT

high-resolution CT may help in differential diagnosis. Because the doubling time of BAC is long (average, 457-813 days) With MDCT it is possible to scan a wide range, including areas containing pulmonary nodules, at a detector collimation of 0.500-0.625 mm in one breath-hold. This capability facilitates 3D evaluation of pulmonary nodules. In previous studies C a r d io p u lm o n a r y I m ag i ng • O r ig i n a l R e s e a rc h MATERIALS AND METHODS. To evaluate the accuracy of computer-aided volumetry software, we performed thin-section helical CT of a chest phantom that included simulated 3-, 5-, 8-, 10-, and 12-mm-diameter ground-glass opacity nodules with attenuation of -800, -630, and -450 HU. Three radiologists measured the volume of the nodules and calculated the relative volume measurement error, which was defined as follows: (measured nodule volume minus assumed nodule volume ÷ assumed nodule volume) × 100. Two radiologists performed two independent measurements of 59 nodules in humans. Intraobserver and interobserver agreement was evaluated with Bland-Altman methods. RESULTS. The relative volume measurement error for simulated ground-glass opacity nodules measuring 3 mm ranged from 51.1% to 85.2% and for nodules measuring 5 mm or more in diameter ranged from -4.1% to 7.1%. In the clinical study, for intraobserver agreement, the 95% limits of agreement were -14.9% and -13.7% and -16.6% to 15.7% for observers A and B. For interobserver agreement, these values were -16.3% to 23.7% for nodules 8 mm in diameter or larger. CONCLUSION. With computer-aided volumetry of ground-glass opacity nodules, the relative volume measurement error was small for nodules 5 mm in diameter or larger. Intraobserver and interobserver agreement was relatively high for nodules 8 mm in diameter or larger