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.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

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.

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

6-month consequences of COVID-19 in patients discharged from hospital: a cohort study.

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.

https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf

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

Abstract Aim To investigate the diagnostic value of resistance index (RI) in differentiating focal liver lesions. Patients and methods In this retrospective study, a total of 576 patients with histologically confirmed focal liver lesions were included. Each patient underwent B-mode ultrasound examination and color Doppler ultrasound examination. The RI values of different focal liver lesions were recorded and compared. Results The mean RI value of benign lesions was significantly lower than that of malignant lesions (0.54 ± 0.10 vs. 0.71 ± 0.12) (p <0.05). In malignant lesions, the RI value of intrahepatic cholangiocarcinoma was significantly lower than that of hepatocellular carcinoma lesions. Furthermore, in hepatocellular carcinoma lesions, the RI of large lesions (group 4: >10 cm) was significantly lower than that of small lesions (group 1: ≤2 cm, group 2: 2–5 cm) (p <0.05). Taken RI of 0.615 as a cutoff value to differentiate malignant and benign lesions, the sensitivity, specificity, positive predictive value and negative predictive value were 82.80%, 81.00%, 81.34% and 82.48%, respectively. Conclusion Color Doppler ultrasound examination is a valuable imaging method in detecting blood flow signal within liver lesions. The RI parameter should be helpful in differentiating malignant and benign liver tumors.

/pdf/the-diagnostic-value-of-doppler-resistive-index-in-the-2g3nnwdh.pdf

The diagnostic value of Doppler Resistive Index in the differential diagnosis of focal liver lesions

The accurate segmentation of prostate region in magnetic resonance imaging (MRI) can provide reliable basis for artificially intelligent diagnosis of prostate cancer. Transformer-based models have been increasingly used in image analysis due to their ability to acquire long-term global contextual features. Although Transformer can provide feature representations of the overall appearance and contour representations at long distance, it does not perform well on small-scale datasets of prostate MRI due to its insensitivity to local variation such as the heterogeneity of the grayscale intensities in the peripheral zone and transition zone across patients; meanwhile, the convolutional neural network (CNN) could retain these local features well. Therefore, a robust prostate segmentation model that can aggregate the characteristics of CNN and Transformer is desired. In this work, a U-shaped network based on the convolution coupled Transformer is proposed for segmentation of peripheral and transition zones in prostate MRI, named the convolution coupled Transformer U-Net (CCT-Unet). The convolutional embedding block is first designed for encoding high-resolution input to retain the edge detail of the image. Then the convolution coupled Transformer block is proposed to enhance the ability of local feature extraction and capture long-term correlation that encompass anatomical information. The feature conversion module is also proposed to alleviate the semantic gap in the process of jumping connection. Extensive experiments have been conducted to compare our CCT-Unet with several state-of-the-art methods on both the ProstateX open dataset and the self-bulit Huashan dataset, and the results have consistently shown the accuracy and robustness of our CCT-Unet in MRI prostate segmentation. The codes are available at https://github.com/git-yan/CCT-Unet.

CCT-Unet: A U-shaped Network based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI.

Abstract Purpose To verify whether radiomics techniques based on dual-modality ultrasound consisting of B-mode and superb microvascular imaging (SMI) can improve the accuracy of the differentiation between gallbladder neoplastic polyps and cholesterol polyps. Methods A total of 100 patients with 100 pathologically proven gallbladder polypoid lesions were enrolled in this retrospective study. Radiomics features on B-mode ultrasound and SMI of each lesion were extracted. Support vector machine was used to classify adenomas and cholesterol polyps of gallbladder for B-mode, SMI and dual-modality ultrasound, respectively, and the classification results were compared among the three groups. Results Six, eight and nine features were extracted for each lesion at B-mode ultrasound, SMI and dual-modality ultrasound, respectively. In dual-modality ultrasound model, the area under the receiver operating characteristic curve (AUC), classification accuracy, sensitivity, specificity, and Youden’s index were 0.850 ± 0.090, 0.828 ± 0.097, 0.892 ± 0.144, 0.803 ± 0.149 and 0.695 ± 0.157, respectively. The AUC and Youden’s index of the dual-modality model were higher than those of the B-mode model ( p &lt; 0.05). The AUC, accuracy, specificity and Youden’s index of the dual-modality model were higher than those of the SMI model ( p &lt; 0.05). Conclusions Radiomics analysis of the dual-modality ultrasound composed of B-mode and SMI can improve the accuracy of classification between gallbladder neoplastic polyps and cholesterol polyps. 

/pdf/differential-diagnosis-of-gallbladder-neoplastic-polyps-and-27i9ag0e.pdf

Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study

Background Automatic segmentation of knee cartilage and quantification of cartilage parameters are crucial for the early detection and treatment of knee osteoarthritis (OA). The aim of this study was to develop an automatic cartilage segmentation method for three-dimensional water-selective (3D_WATS) cartilage magnetic resonance imaging (MRI) and conduct cartilage morphometry and magnetic susceptibility measurements such as cartilage thickness, volume, and susceptibility values for knee OA assessment. Methods Sixty-five consecutively sampled subjects, who had undergone health checks at our hospital, were enrolled in this cross-sectional study and were divided into three groups: 20 normal, 20 mild OA, 25 severe OA. Sagittal 3D_WATS sequence was used to image cartilage at 3T. The raw magnitude images were used for cartilage segmentation and the phase images were used for quantitative susceptibility mapping (QSM)-based assessment. Manual cartilage segmentation was performed by two experienced radiologists, and the automatic segmentation model was constructed using nnU-Net. Quantitative cartilage parameters were extracted from the magnitude and phase images based on the cartilage segmentation. Pearson correlation coefficient and intra-class correlation coefficient (ICC) were then used to assess the consistency of obtained cartilage parameters between automatic and manual segmentation. Cartilage thickness, volume, and susceptibility values among different groups were compared using one-way analysis of variance (ANOVA). Support vector machine (SVM) was used to further verify the classification validity of automatically extracted cartilage parameters. Results The constructed cartilage segmentation model based on nnU-Net achieved an average Dice score of 0.93. The consistency of cartilage thickness, volume, and susceptibility values calculated using automatic and manual segmentations ranged from 0.98 to 0.99 (95% CI: 0.89–1.00) for the Pearson correlation coefficient, and from 0.91–0.99 (95% CI: 0.86–0.99) for ICC, respectively. Significant differences were found in OA patients; including decreases in cartilage thickness, volume, and mean susceptibility values (P<0.05), and increases in standard deviation (SD) of susceptibility values (P<0.01). Moreover, the automatically extracted cartilage parameters can achieve an AUC value of 0.94 (95% CI: 0.89–0.96) for OA classification using the SVM classifier. Conclusions The 3D_WATS cartilage MR imaging allows simultaneously automated assessment of cartilage morphometry and magnetic susceptibility for evaluating the severity of OA using the proposed cartilage segmentation method.

https://qims.amegroups.com/article/viewFile/113477/pdf

Cartilage morphometry and magnetic susceptibility measurement for knee osteoarthritis with automatic cartilage segmentation

In order to evaluate the heart function, it is valuable to detect and visualize the blood flow inside the left ventricle (LV) of the heart. In this paper, a method for flow detection in LV is proposed based on the echocardiography by using the speckle image velocimetry (SIV). It first segments the LV from the echocardiography and then conducts the SIV in the LV and estimates displacement vectors of red blood cell aggregates. Finally, it calculates the flow velocity and depicts the velocity vectors for visualization. The experiments were carried out on both simulated images and clinical video images. The results demonstrate the feasibility of the method, indicating that the method may be helpful for assessment of the fluid dynamics of LV function and dysfunction.

Qi Zhang

Papers

The diagnostic value of Doppler Resistive Index in the differential diagnosis of focal liver lesions

CCT-Unet: A U-shaped Network based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI.

Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study

Cartilage morphometry and magnetic susceptibility measurement for knee osteoarthritis with automatic cartilage segmentation

Detection of blood flow in left ventricle by echocardiography using speckle image velocimetry