Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

A 672-nW, 670-n<italic>Vrms</italic> ECG Acquisition AFE With Noise-Tolerant Heartbeat Detector

This paper presents an electrocardiogram acquisition analog front-end (AFE) with a noise tolerant heartbeat (HB) detector. Source degradation and transconductance bootstrap techniques are incorporated into the AFE to reduce the 1/f noise of the amplifier. Furthermore, the chopper modulation, DC-servo loop (DSL) and pre-charge technology are combined to reduce interference from the environment. A mixed-signal implementation of HB detector with the symmetric-comparison loop is proposed to reduce the power consumption and area, which also suppresses motion artifact interference by adaptive thresholds. Implemented in $0.18 ~\mu \text{m}$ CMOS process, the circuit only occupies an area of $0.122 mm^{2}$ and consumes $0.62 ~\mu \text{W}$ at a 1.2-V supply, of which AFE and HB detector consume 507 nW and 110 nW, respectively. Simulation results show that the gain and the CMRR of AFE range from 30&#x2013;45 dB and 65&#x2013;105 dB, respectively. The input-referred noise is 670 nVrms with a mid-band gain of 42 dB and a bandwidth ranging from 0.5 Hz to 1 kHz

Direct ink writing of fluoropolymer/CNT-based superhydrophobic and corrosion-resistant electrodes for droplet energy harvesters and self-powered electronic skins

Self-powered devices and systems that operate by harnessing environmental mechanical energies including raindrops and body motions have been extensively explored owing to their promising applications. In practical applications, these devices are often exposed to humid conditions or directly contact aqueous solutions. Here, we report the development of chemically inert and superhydrophobic electrode based on fluoropolymer (FP)/carbon-nanotube (CNT) that circumvents undesired metal electrode corrosion, deformation, and damage in harsh environments. The electrode surface can be patterned on flexible surfaces via direct ink writing (DIW), and no damage or corrosion is detected even being bent 10,000 times or immersed into salt/acid/alkaline solutions for 20 h. The integration of such robust electrodes with hydrophobic tribo-materials enables the construction of droplet-based electricity generators (DEGs) that exhibit an instantaneous current and power outputs of 2 mA and 0.12 W, respectively, and that light up 50 LEDs by one water droplet. Self-powered touch sensing function is also demonstrated on FP/CNT-based electronic skin, offering the broad applicability of the proposed electrode preparation strategy for self-powered devices