Qi Zhang

Bio: Qi Zhang is an academic researcher from Fudan University. The author has contributed to research in topics: Medicine & Ultrasound. The author has an hindex of 20, co-authored 100 publications receiving 1563 citations. Previous affiliations of Qi Zhang include Minjiang University & Duke University.

Patients with Achilles Tendon Rupture Have a Degenerated Contralateral Achilles Tendon: An Elastography Study.

Abstract: Purpose. To evaluate differences of Achilles tendon (AT) hardness and morphology between asymptomatic tendons in patients with acute AT ruptures on the contralateral side and asymptomatic tendons in healthy people by using computer-assisted quantification on axial-strain sonoelastography (ASE). Methods. The study consisted of 33 asymptomatic tendons in 33 patients (study group) and 34 tendons in 19 healthy volunteers (control group). All the tendons were examined by both ASE and conventional ultrasound. Computer-assisted quantification on ASE was applied to extract hardness variables, including the mean (Hmean), percentile (H20), median (H50) and skewness (Hsk) of the hardness within tendon, and the ratio of the mean hardness within tendon to that outside tendon (Hratio) and three morphological variables: the thickness (THK), cross-sectional area, and eccentricity (ECC) of tendons. Results. The Hmean, Hsk, H20, H50, and Hratio in the proximal third of the tendon body in study group were significantly smaller than those in control group (Hmean: 0.43±0.09 vs 0.50±0.07, p=0.001; Hsk: -0.53±0.51 vs -1.09±0.51, p<0.001; H20: 0.31±0.10 vs 0.40±0.10, p=0.001; H50: 0.45±0.10 vs 0.53±0.08, p<0.001; Hratio: 1.01±0.25 vs 1.20±0.23, p=0.003). The THK and cross-sectional area of tendons in the study group were larger than those in the control group (p<0.05). Conclusions. As a quantitative objective method, the computer-assisted ASE reveals that the asymptomatic ATs contralateral to acute rupture are softer than those of healthy control group at the proximal third and the asymptomatic tendons in people with rupture history are thicker, larger, and rounder than those of normal volunteers especially at the middle and distal thirds of AT body.

Customizing Robot-Assisted Passive Neurorehabilitation Exercise Based on Teaching Training Mechanism.

Abstract: Passive movement is an important mean of rehabilitation for stroke survivors in the early stage or with greater paralysis. The upper extremity robot is required to assist therapists with passive movement during clinical rehabilitation, while customizing is one of the crucial issues for robot-assisted upper extremity training, which fits the patient-centeredness. Robot-assisted teaching training could address the need well. However, the existing control strategies of teaching training are usually commanded by position merely, having trouble to achieve the efficacy of treatment by therapists. And deficiency of flexibility and compliance comes to the training trajectory. This research presents a novel motion control strategy for customized robot-assisted passive neurorehabilitation. The teaching training mechanism is developed to coordinate the movement of the shoulder and elbow, ensuring the training trajectory correspondence with human kinematics. Furthermore, the motion trajectory is adjusted by arm strength to realize dexterity and flexibility. Meanwhile, the torque sensor employed in the human-robot interactive system identifies movement intention of human. The goal-directed games and feedbacks promote the motor positivity of stroke survivors. In addition, functional experiments and clinical experiments are investigated with a healthy adult and five recruited stroke survivors, respectively. The experimental results present that the suggested control strategy not only serves with safety training but also presents rehabilitation efficacy.

McIlhagga Edge Detector-Based Anisotropic Diffusion for Speckle Reduction of Ultrasound Images

Abstract: A new speckle reduction method for ultrasound images is proposed based on the McIlhagga edge detector. The edge detector in the traditional an isotropic diffusion was replaced by the McIlhagga edge detector to suppress the speckle noise in ultrasound images. The numerical solution of the McIlhagga edge detector-based an isotropic diffusion (MAD) is derived. Both synthetic and real ultrasound images are used to evaluate the MAD method. The performance of the MAD is compared with six traditional image denoising methods. It is shown that the MAD method is superior to the traditional methods in both noise reduction and detail preservation.

Contrast-enhanced ultrasound image segmentation of atherosclerotic plaques using spatial-temporal analysis and snakes

Abstract: It is valuable to segment carotid atherosclerotic plaques in contrast-enhanced ultrasound (CEUS) images. Traditional methods for plaque segmentation neglect the temporal information in video images, and it limits the accuracy of the segmentation. We propose an algorithm for accurate segmentation of plaques in CEUS images using spatial-temporal analysis and snakes. First, the spatial correlation of time intensity curves is used to capture detailed information from spatial-temporal neighborhoods of plaques and detect initial contours of the plaques. Then the speckle reducing anisotropic diffusion is adopted to modify the edge map of the gradient vector flow snake, and the initial contours are deformed using the modified snake and converged to refined contours. The proposed method was evaluated via 21 in vivo images, and it outperformed the boundary vector field snake by 0.06 mm, 2.0%, 0.04 mm and 5.3%, in terms of the mean distance error, relative mean distance error, mean signed distance error, and relative difference degree, respectively. The results revealed that the proposed method can accurately detect plaques and delineate their contours.

Preliminary Clinical Experience with Shear Wave Dispersion Imaging for Liver Viscosity in Preoperative Diagnosis of Focal Liver Lesions.

Abstract: Background The aim of our study is to analyze viscosity characteristics of focal liver lesions (FLLs) and the diagnostic performance of shear wave dispersion (SWD) in differentiating benign and malignant FLLs. Methods Between January 2018 and April 2018, 58 consecutive patients (median age 57, age range 21–74 years, 37 males) with 58 FLLs located on the right lobe of liver were prospectively studied. The Aplio i900 series diagnostic ultrasound system (Canon Medical systems) equipped with a curvilinear PV1-475BX transducer (1–8 MHz) was used. SWD slope and viscosity measurements were expressed as mean ± standard deviation for both liver tumors and background liver parenchyma. Histopathological results after surgery were regarded as the gold standard for diagnosis. Results Final diagnosis included 40 cases of malignant and 18 cases of benign FLLs. The mean viscosity value were 14.78 ± 1.86 m/s/kHz for hepatocellular carcinoma (n = 30), 14.81 ± 2.35 m/s/kHz for liver metastasis lesions (n = 10), 13.23 ± 1.31 m/s/kHz for hemangioma (n = 13), and 13.67 ± 2.72 m/s/kHz for focal nodular hyperplasia (n = 5). Malignant FLLs showed higher mean viscosity values (14.79 ± 3.15 m/s/KHz) than benign FLLs (13.36 ± 2.76 m/s/KHz) (p Conclusions The analysis of SWD slope and liver viscosity parameters provide additional viscoelastic information about FLLs before operation.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.