Nanoobject mass measurement using the node displacement of the second mode of the nanomechanical resonator

This work suggests a new approach to weighting the nanoscale objects placed at the tip of cantilever vibrating inside the camera of scanning electron microscope. In contrast to traditional approach to mass determination, we suggest tracing the shift of the node of the second vibration mode as an alternative to frequency shift measurement. We demonstrate the applicability of our approach to carbon nanowhisker cantilevers grown on tungsten needles by focused electron beam induced deposition. We compare experimentally the performance of the suggested approach with the traditional frequency shift-based method.

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

Introduction The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. This study aimed to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG) using pre-trained CNN. Method This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e.,non-ischemic) and the end (i.e.,ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia. Results Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection. Conclusions When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy. FUNDunding Acknowledgement Type of funding sources: None.

Simulation of Single Channel Magnetic Induction Tomography for Meningitis Detection By Using COMSOL Multiphysics

Meningitis is a inflammation of the meninges and the most common central nervous system (CNS) due to bacterial infection. Numbers of children who have bacterial meningitis are still high in recent 15 years regardless of the availability of newer antibiotics and preventive strategies. This research focuses on simulation using COMSOL Multiphysics on the design and development of magnetic induction tomography (MIT) system that emphasizes on a single channel rotatable of brain tissue imaging. The purpose of this simulation is to test the capability of the developed MIT system in detecting the change in conductivity and to identify the suitable transmitter-receiver pair and the optimum frequency based on phase shift measurement technique for detecting the conductivity property distribution of brain tissues. The obtained result verified that the performance of the square coil with 12 number of turns (5Tx-12Rx) with 10MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.

Smartphone Imaging Repository: A Novel, Cost-Effective Method For Creating a CT Image Bank

BackgroundImaging repositories are commonly attached to ongoing clinical trials for efficiency and cost savings, but capturing, transmitting and storing images can be cumbersome and increase costs. Typical methods include outdated technologies such as compact discs. Electronic file transfer is becoming more common, but even this requires hours of staff time on dedicated computers in the radiology department.MethodsWe describe and test an image capture method using smartphone camera video images of brain computed tomography (CT) scans of traumatic intracranial hemorrhage. The deidentified videos are emailed from the emergency department for central adjudication. We selected eight scans, mild moderate and severe subdural and multicompartmental hematomas and mild and moderate intraparenchymal hematomas. Eighty videos were made by 10 people on 7 different smartphones. We measured the time in seconds it took to capture and email the files. The primary outcomes were hematoma volume measured by ABC/2, Marshal Scale, midline shift measurement, image quality by contrast-to-noise ratio (CNR) and time to capture. A radiologist and an imaging scientist applied ABC/2 method, calculated the Marshall scale and midline shift on the video images and on the PACS in a randomized order. We calculate the intraclass correlation coefficient (ICC). We measured image quality by calculating contrast-to-noise ratio (CNR). We report summary statistics on time to capture in the smartphone group without a comparator.ResultsICC for lesion volume, midline shift and Marshall score were 0.991 (95% CI 0.976, 0.998), 0.998 (95% CI: 0.996, 0.999) and 0.973 (0.931, 0.994) respectively. Lesion conspicuity was not different among the image types via assessment of CNR using the Friedman test, Bof 10.6, P=.061, with a small Kendall’s W effect size (0.264). Mean (standard deviation) time to capture and email the video was 60.1 (24.3) seconds.ConclusionsTypical smartphones may produce video image quality high enough for use in a clinical trial imaging repository. Video capture and transfer takes only seconds, and hematoma volumes, Marshal scales and image quality measured on the videos did not differ significantly from those calculated on the PACS.

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

Introduction The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG). Method This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia. Results Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection. Conclusions When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.