scholarly journals Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19

2021 ◽  
Vol 12 ◽  
Author(s):  
Lan Yu ◽  
Xiaoli Shi ◽  
Xiaoling Liu ◽  
Wen Jin ◽  
Xiaoqing Jia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Roc Curve ◽  
Area Under The Curve ◽  
Lactic Dehydrogenase ◽  
Ct Images ◽  
Image Features ◽  
Clinical Classification ◽  
Laboratory Findings ◽  
Ct Features

Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people’s lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients.Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman’s correlation test was applied to study correlations of CT characteristics and laboratory findings.Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features.Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.

scholarly journals Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Giorgia Silvestri ◽  
Serena Mognetti ◽  
Emanuele Voulaz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Ct Images ◽  
Tnm System ◽  
Positron Emission ◽  
Pet Ct ◽  
Test Sets ◽  
Fdg Pet Ct

Aim. To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods. We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results. The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion. We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.

Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus

2021 ◽  
pp. bjophthalmol-2021-319309
Author(s):  
Gairik Kundu ◽  
Rohit Shetty ◽  
Pooja Khamar ◽  
Ritika Mullick ◽  
Sneha Gupta ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Refractive Surgery ◽  
Three Dimensional ◽  
Area Under The Curve ◽  
Preoperative Imaging ◽  
Imaging Data ◽  
Wavefront Aberrations ◽  
Healthy Eyes ◽  
Fellow Eyes

AimsTo develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI).MethodsPreoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed.ResultsThe AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model.ConclusionsA universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as ‘subclinical’ and ‘forme-fruste’.

scholarly journals Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis

2019 ◽  
Vol 8 (7) ◽  
pp. 986 ◽  
Author(s):  
Owais ◽  
Arsalan ◽  
Choi ◽  
Mahmood ◽  
Park
Keyword(s):  
Artificial Intelligence ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Area Under The Curve ◽  
Gastrointestinal Diseases ◽  
Classification Performance ◽  
Superior Performance ◽  
Temporal Features ◽  
Endoscopic Videos

Various techniques using artificial intelligence (AI) have resulted in a significant contribution to field of medical image and video-based diagnoses, such as radiology, pathology, and endoscopy, including the classification of gastrointestinal (GI) diseases. Most previous studies on the classification of GI diseases use only spatial features, which demonstrate low performance in the classification of multiple GI diseases. Although there are a few previous studies using temporal features based on a three-dimensional convolutional neural network, only a specific part of the GI tract was involved with the limited number of classes. To overcome these problems, we propose a comprehensive AI-based framework for the classification of multiple GI diseases by using endoscopic videos, which can simultaneously extract both spatial and temporal features to achieve better classification performance. Two different residual networks and a long short-term memory model are integrated in a cascaded mode to extract spatial and temporal features, respectively. Experiments were conducted on a combined dataset consisting of one of the largest endoscopic videos with 52,471 frames. The results demonstrate the effectiveness of the proposed classification framework for multi-GI diseases. The experimental results of the proposed model (97.057% area under the curve) demonstrate superior performance over the state-of-the-art methods and indicate its potential for clinical applications.

scholarly journals Establishment and Clinical Application of an Artificial Intelligence Diagnostic Platform for Identifying Rectal Cancer Tumor Budding

2021 ◽  
Vol 11 ◽  
Author(s):  
Shanglong Liu ◽  
Yuejuan Zhang ◽  
Yiheng Ju ◽  
Ying Li ◽  
Xiaoning Kang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Rectal Cancer ◽  
Roc Curve ◽  
Cell Activity ◽  
Area Under The Curve ◽  
Pathological Diagnosis ◽  
Tumor Budding ◽  
Cancer Tumor ◽  
Network Platform

Tumor budding is considered a sign of cancer cell activity and the first step of tumor metastasis. This study aimed to establish an automatic diagnostic platform for rectal cancer budding pathology by training a Faster region-based convolutional neural network (F-R-CNN) on the pathological images of rectal cancer budding. Postoperative pathological section images of 236 patients with rectal cancer from the Affiliated Hospital of Qingdao University, China, taken from January 2015 to January 2017 were used in the analysis. The tumor site was labeled in Label image software. The images of the learning set were trained using Faster R-CNN to establish an automatic diagnostic platform for tumor budding pathology analysis. The images of the test set were used to verify the learning outcome. The diagnostic platform was evaluated through the receiver operating characteristic (ROC) curve. Through training on pathological images of tumor budding, an automatic diagnostic platform for rectal cancer budding pathology was preliminarily established. The precision–recall curves were generated for the precision and recall of the nodule category in the training set. The area under the curve = 0.7414, which indicated that the training of Faster R-CNN was effective. The validation in the validation set yielded an area under the ROC curve of 0.88, indicating that the established artificial intelligence platform performed well at the pathological diagnosis of tumor budding. The established Faster R-CNN deep neural network platform for the pathological diagnosis of rectal cancer tumor budding can help pathologists make more efficient and accurate pathological diagnoses.

scholarly journals Hypertension as a sequela in patients of SARS-CoV-2 infection

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250815
Author(s):  
Ganxiao Chen ◽  
Xun Li ◽  
Zuojiong Gong ◽  
Hao Xia ◽  
Yao Wang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
White Blood Cells ◽  
Cardiac Injury ◽  
Clinical Signs ◽  
Area Under The Curve ◽  
Renin Angiotensin System ◽  
Lactic Dehydrogenase ◽  
Clinical Severity ◽  
Laboratory Findings ◽  
Good Ability

Background COVID-19 is a respiratory infectious disease caused by SARS-CoV-2, and cardiovascular damage is commonly observed in affected patients. We sought to investigate the effect of SARS-CoV-2 infection on cardiac injury and hypertension during the current coronavirus pandemic. Study design and methods The clinical data of 366 hospitalized COVID-19-confirmed patients were analyzed. The clinical signs and laboratory findings were extracted from electronic medical records. Two independent, experienced clinicians reviewed and analyzed the data. Results Cardiac injury was found in 11.19% (30/268) of enrolled patients. 93.33% (28/30) of cardiac injury cases were in the severe group. The laboratory findings indicated that white blood cells, neutrophils, procalcitonin, C-reactive protein, lactate, and lactic dehydrogenase were positively associated with cardiac injury marker. Compared with healthy controls, the 190 patients without prior hypertension have higher AngⅡ level, of which 16 (8.42%) patients had a rise in blood pressure to the diagnostic criteria of hypertension during hospitalization, with a significantly increased level of the cTnI, procalcitonin, angiotensin-II (AngⅡ) than those normal blood pressure ones. Multivariate analysis indicated that elevated age, cTnI, the history of hypertension, and diabetes were independent predictors for illness severity. The predictive model, based on the four parameters and gender, has a good ability to identify the clinical severity of COVID-19 in hospitalized patients (area under the curve: 0.932, sensitivity: 98.67%, specificity: 75.68%). Conclusion Hypertension, sometimes accompanied by elevated cTnI, may occur in COVID-19 patients and become a sequela. Enhancing Ang II signaling, driven by SARS-CoV-2 infection, might play an important role in the renin-angiotensin system, and consequently lead to the development of hypertension in COVID-19.

scholarly journals Analysis of Initial Clinical and CT Features of COVID-19 Pneumonia in Different Clinical Types

2020 ◽  
Author(s):  
Wei Li ◽  
Wenjun Yu ◽  
Jianwei Liao ◽  
Lin Yao ◽  
Yijie Fang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Chest Ct ◽  
Lower Body ◽  
Clinical Classification ◽  
Clinical Factors ◽  
Factors Associated ◽  
Critical Patients ◽  
Ct Features

Abstract Background Different clinical classifications of COVID-19 pneumonia patients have different clinical and CT features, which is very important for the treatment after admission. As the epidemic situation in China continues to improve, it is particularly important to re-clarify the correlation between them.Methods 97 confirmed patients with COVID-19 pneumonia were enrolled from January 17, 2019 to February 21, 2020, including 75 mild/ordinary cases and 22 severe/critical cases. The clinical data and initial chest CT images of the patients were reviewed and compared. The risk factors associated with disease severity were analyzed.Results Compared with the mild/ordinary patients, the severe/critical patients had older ages, higher incidence of comorbidities, first CT positive, CT always negative and fever. Mild/ordinary patients had lower body temperature than mild/ordinary patients. The incidences of large/multiple GGO in severe/critical patients were significantly higher than those of the mild/ordinary patients, furthermore, severe/critical patients showed higher incidences of 4-5 lobe infections than the ordinary patients. The CT scores of severe/critical patients were significantly higher than those of the ordinary patients (P < 0.001). The clinical factors of age, sex, comorbidities, hypertension, diabetes mellitus, heart disease, pharyngeal discomfort, abdominal pain/diarrhea, temperature and CT score were risk factors for severe/critical COVID-19 pneumonia.Conclusion The initial clinical and CT characteristics have certain significance for the clinical classification of COVID-19 respiratory infection. Especially in terms of CT score, it can predict the trend of clinical classification of patients to a certain extent.

Classification of papillary thyroid carcinoma histological images based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Image Features ◽  
Papillary Thyroid ◽  
Histological Image ◽  
Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

scholarly journals Measles Virus IgG Avidity Assay for Use in Classification of Measles Vaccine Failure in Measles Elimination Settings

2012 ◽  
Vol 19 (11) ◽  
pp. 1810-1817 ◽  
Author(s):  
Sara Mercader ◽  
Philip Garcia ◽  
William J. Bellini
Keyword(s):  
Measles Virus ◽  
Area Under The Curve ◽  
Epidemiological Data ◽  
Diagnostic Tools ◽  
High Avidity ◽  
Characteristic Analysis ◽  
Vaccine Failure ◽  
Avidity Assay ◽  
Igg Avidity

ABSTRACTIn regions where endemic measles virus has been eliminated, diagnostic assays are needed to assist in correctly classifying measles cases irrespective of vaccination status. A measles IgG avidity assay was configured using a commercially available measles-specific IgG enzyme immunoassay by modifying the protocol to include three 5-min washes with diethylamine (60 mM; pH 10.25) following serum incubation; serum was serially diluted, and the results were expressed as the end titer avidity index. Receiver operating characteristic analysis was used for evaluation and validation and to establish low (≤30%) and high (≥70%) end titer avidity thresholds. Analysis of 319 serum specimens expected to contain either high- or low-avidity antibodies according to clinical and epidemiological data indicated that the assay is highly accurate, with an area under the curve of 0.998 (95% confidence interval [CI], 0.978 to 1.000), sensitivity of 91.9% (95% CI, 83.2% to 97.0%), and specificity of 98.4% (95% CI, 91.6% to 100%). The assay is rapid (<2 h) and precise (standard deviation [SD], 4% to 7%). In 18 samples from an elimination setting outbreak, the assay identified 2 acute measles cases with low-avidity results; both were IgM-positive samples. Additionally, 11 patients (15 samples) with modified measles who were found to have high-avidity IgG results were classified as secondary vaccine failures; one sample with an intermediate-avidity result was not interpretable. In elimination settings, measles IgG avidity assays can complement existing diagnostic tools in confirming unvaccinated acute cases and, in conjunction with adequate clinical and epidemiologic investigation, aid in the classification of vaccine failure cases.

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