Intelligent diagnosis of atrial septal defect in children using echocardiography with deep learning

Background Atrial septal defect(ASD)is one of the most common congenital heart diseases.The diagnosis of ASD via transthoracic echocardiography is subjective and time-consuming.Methods The objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural networks.The proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard view.Among the standard views,we selected two echocardiographic views,i.e.,the subcostal sagittal view of the atrium septum and the low parasternal four-chamber view.The developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 cases.Additionally,an independent test dataset of 112 images corresponding to 56 cases was used,including 101 cases with ASDs and 153 cases with normal hearts.Results The average area under the receiver operating characteristic curve,recall,precision,specificity,F1-score,and accuracy of the proposed ASD detection model were 91.99,80.00,82.22,87.50,79.57,and 83.04,respectively.Conclusions The proposed model can accurately and automatically identify ASD,providing a strong foundation for the intelligent diagnosis of congenital heart diseases.

变压吸附实验装置的小型化、低成本化改进

目前的变压吸附实验中通常使用商品化设备,存在尺寸大、成本高、气路复杂等问题,不利于大规模开展本科实验教学。本文对变压吸附装置进行了小型化、低成本化改进,学生可以使用实验室常见材料自行搭建变压吸附装置,了解并掌握设备搭建、调试、运行的整个流程。以Li交换分子筛作为吸附剂,该设备的制氧效果与商用制氧机相当,出口氧气浓度可达95.8%。该装置具有安全性高、可反复拆装的特点,搭建、调试过程具有一定趣味性,适合用于应用化学、化学工程等本科专业的变压吸附实验教学,有利于激发学生对创制实验装置的兴趣,锻炼学生的动手能力,提高学生理论结合实际解决问题的能力。

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.

Spatial-spectral identication of abnormal leukocytes based on microscopic hyperspectral imaging technology

Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia(ALL).As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution,which signicantly affects the absorption and reflection of light,the spectral feature is proved to be important for leukocytes classication and identication.This paper proposes an accurate identication method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging(HSI)technology which combines the spectral information.The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm.Then,the spectral features are extracted and combined with the spatial features.Based on this,the support vector machine(SVM)is applied for classication ofve types of leukocytes and abnormal leukocytes.Compared with different classication methods,the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes,improving the accuracy in the classication and identication of leukocytes.This paper only selects one subtype of ALL for test,and the proposed method can be applied for detection of other leukemia in the future.

Protective Effect of Shenfu Injection on Rats with Chronic Heart Failure Induced by Doxorubicin

[Objectives] To determine the protective effect of Shenfu injection( SFI) on chronic heart failure( CHF) in rats induced by doxorubicin. [Methods] CHF was induced by doxorubicin via intraperitoneal injection in rats. The cardiac function parameters,the heart index,the serum brain natriuretic peptide( BNP) level and cardiac histopathology were measured 4 weeks after Shenfu injection treatment. [Results]Shenfu injection remarkably improved the heart index and cardiac histopathology,increased the heart rate( HR),left ventricular systolic pressure( LVSP),maximum rising rate of left ventricular pressure( + dp/dtmax) and dropping rate of left ventricular pressure(-dp/dtmax),reduced the left ventricular end-diastolic pressure( LVEDP) and serum BNP level of rats with CHF induced by doxorubicin. [Conclusions]Shenfu injection exerts protective effect on CHF induced by doxorubicin.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

Thyroid cancer,a common endocrine malignancy,is one of the leading death causes among endocrine tumors.The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures.Therefore,we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma(PTC)diagnosis and characterize PTC characteristics.To alleviate any concerns pathologists may have about using the model,we conducted an analysis of the used bands that can be interpreted pathologically.A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients.The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue.Clinical data of the corresponding patients were collected for subsequent model interpretability analysis.The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University.The spectral preprocessing method was used to process the spectra,and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models.The PTC detection model using mean centering(MC)and multiple scattering correction(MSC)has optimal performance,and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide.For model interpretable analysis,the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak,and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients.Moreover,the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients.In addition,the correlation analysis was performed between the selected wavelengths and the clinical data,and the results show:the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure,nucleus size and free thyroxine(FT4),and has a strong correlation with triiodothyronine(T3);the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine(FT3).

Chemotherapy initiation with single-course methotrexate alone or combined with dactinomycin versus multi-course methotrexate for low-risk gestational trophoblastic neoplasia: a multi-centric randomized clinical trial

We aimed to evaluate the effectiveness and safety of single-course initial regimens in patients with low-risk gestational trophoblastic neoplasia(GTN).In this trial (NCT01823315),276 patients were analyzed.Patients were allocated to three initiated regimens:single-course methotrexate(MTX),single-course MTX+dactinomycin(ACTD),and multi-course MTX(control arm).The primary endpoint was the complete remission(CR)rate by initial drug(s).The primary CR rate was 64.4%with multi-course MTX in the control arm.For the single-course MTX arm,the CR rate was 35.8%by one course;it increased to 59.3%after subsequent multi-course MTX,with non-inferiority to the control(difference-5.1%,95%confidence interval(CI)-19.4%to 9.2%,P=0.014).After further treatment with multi-course ACTD,the CR rate(93.3%)was similar to that of the control(95.2%,P=0.577).For the single-course MTX+ACTD arm,the CR rate was 46.7%by one course,which increased to 89.1%after subsequent multi-course,with non-inferiority(difference 24.7%,95%CI 12.8%-36.6%,P<0.001)to the control.It was similar to the CR rate by MTX and further ACTD in the control arm(89.1%vs.95.2%,P=0.135).Four patients experienced recurrence,with no death,during the 2-year follow-up.We demonstrated that chemotherapy initiation with single-course MTX may be an alternative regimen for patients with low-risk GTN.

Identification of various food residuals on denim based on hyperspectral imaging system and combination optimal strategy

As the science and technology develop,crime methods and scenes have become increasingly complex and diverse.Trace evidence analysis has become amore and more important criminal investigation technology and liquid is the main form of trace evidence.Food can provide not only energy,but clues to solve crimes.In this study,we build a hyperspectral imaging system to detect liquid residue traces,including apple juice,coffee,cola,milk and tea,on denims with light,middle and dark colors.The obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data pretreatment.Subsequently,Partial Least Squares(PLS)is applied to select the informative wavelengths from the preprocessed spectra.For modeling phase,the combination optimal strategy,support vector machine(SVM)combined with random forest(RF),is developed to establish classification models.The experimental results demonstrate that the combination optimal model can achieve TPR,FPR,Precision,Recall,F1,and AUC of 83.5%,2.30%,79.7%,83.5%,81.6%,and 94.7%for classifying fabrics contaminated by various food residuals.With respect to the classification of liquid and fabric types,the combination optimalmodel also yields satisfactory classification performance.In future work,wewill expand the types of liquid,and make appropriate adjustment to algorithms for improving the robustness of classification models.This research may play a positive role in the construction of a harmonious society.

PCA-U-Net based breast cancer nest segmentation from microarray(jj)hyperspectral images

The incidence of breast cancer is tending younger globally,and tumor development,clinical treatment,and prognosis are largely influenced by histopathological diagnosis.For diagnosed patients,the distinction between the cancer nests and normal tissue is the basis of breast cancer treatment.Microscopic hyperspectral imaging technology has shown its potential in auxiliary pathological examinations due to the superior imaging modality and data characteristics.This paper presents a method for cancer nest segmentation from hyperspectral images of breast cancer tissue microarray samples.The scheme combines the strengths of the U-Net neural network and unsupervised principal component analysis,which reduces the amount of calculation and improves the recognition accuracy.The experimental accuracy of cancer nest segmentation reaches 87.14%.Furthermore,a set of quantitative pathological characteristic parameters reflects the degree of breast cancer lesions from multiple angles,providing a relatively comprehensive reference for the pathologist’s diagnosis.In-depth exploration of the combined development of deep learning and microscopic hyperspectral imaging technology is worthy to promote efficient diagnosis of breast tumors and concern for human health.