Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images.

Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.6% of men and 20.9% of women. It was estimated that this trend will increase to 22.9% and 23.6% for men and women, respectively, by 2025. Controlling blood pressure is therefore important to lower both morbidity and mortality. Computer-aided diagnosis (CAD) is a noninvasive technique which can determine subtle myocardial structural changes at an early stage. In this work, we show how a multi-resolution analysis-based CAD system can be utilized for the detection of early HTN-induced left ventricular heart muscle changes with the help of ultrasound imaging. Firstly, features were extracted from the ultrasound imagery, and then the feature dimensions were reduced using a locality sensitive discriminant analysis (LSDA). The decision tree classifier with contourlet and shearlet transform features was later employed for improved performance and maximized accuracy using only two features. The developed model is applicable for the evaluation of cardiac structural alteration in HTN and can be used as a standalone tool in hospitals and polyclinics.

Novel Radiomics Features for Automated Detection of Cardiac Abnormality in Patients with Pacemaker.

Cardiac pacemakers are used in the treatment of patients with symptomatic bradycardia. The pacemaker paces the heart at the predetermined rate to maintain uninterrupted cardiac activity. Usually, pacemaker lead will be connected to the right atrium (RA) and right ventricle (RV) in dual-chamber pacemaker implantation and RV alone in single-chamber pacemaker implantation. This alters the route of proper conduction across the myocardial cells. The cell-to-cell conduction transmission in pacing delays the activation of selected intraventricular myocardial activation. Pacing-induced cardiomyopathy (PICM) is most commonly defined as a drop in left ventricle ejection fraction (LVEF) in the setting of chronic, high-burden right ventricle (RV) pacing. Currently, very few effective treatments are standard for PICM which rely on the detection of the RV pacing. Such treatments have primarily focused on upgrading to cardiac resynchronization therapy (CRT) when LVEF has dropped. However, the early and accurate detection of these stress factors is challenging. Cardiac desynchrony and interventricular desynchrony can be determined by various echocardiographic techniques, including M-mode, Doppler method, tissue Doppler method, and speckle tracking echocardiography which is subjective measures and shows a significant difference between RV and LV preejection period where the activation of LV is delayed considerably. Computer-aided diagnosis (CAD) is a noninvasive technique that can classify the ultrasound images of the heart in pacemaker-implanted patients and healthy patients with normal left ventricular systolic function and further detect the variations in pacemaker functions in its early stage using heart ultrasound images. Developing such a system requires a vast and diverse database to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of pacemaker variations using machine learning models of decision tree, SVM, random forest, and AdaBoost. The models have been used to extract radiomics features in terms of textures and then screened by their Relief-F scores for selection and ranking to be classified into nine groups consisting of up to 250 radiomics features. Ten best features were fed to the machine learning models. The R-wave dataset achieved a maximum test performance accuracy of 97.73% with four features in the random forest model. The T-wave dataset achieved a maximum test performance accuracy of 96.59% with three features in the SVM model. Our experimental results demonstrate the system's robustness, which can be developed as an early and accurate detection system for pacing-induced cardiomyopathy.

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists' assessment.

PURPOSE: Ultrasonography is the most common imaging modality used to diagnose carpal tunnel syndrome (CTS). Recently artificial intelligence algorithms have been used to diagnose musculoskeletal diseases accurately without human errors using medical images. In this work, a computer-aided diagnosis (CAD) system is developed using radiomics features extracted from median nerves (MN) to diagnose CTS accurately. METHOD: This study is performed on 228 wrists from 65 patients and 57 controls, with an equal number of control and CTS wrists. Nerve conduction study (NCS) is considered as the gold standard in this study. Two radiologists used two guides to evaluate and categorize the pattern and echogenicity of MNs. Radiomics features are extracted from B-mode ultrasound images (Ultrasomics), and the robust features are fed into support vector machine classifier for automated classification. The diagnostic performances of two radiologists and the CAD system are evaluated using ROC analysis. RESULTS: The agreement of two radiologists was excellent for both guide 1 and 2. The honey-comb pattern clearly appeared in control wrists (based on guide 1). In addition, CTS wrists indicated significantly lower number of fascicles in MNs (based on guide 2). The area under ROC curve (AUC) of the radiologist 1 and 2 are 0.658 and 0.667 based on guide 1 and 0.736 and 0.721 based on guide 2, respectively. The CAD system indicated higher performance than two radiologists with AUC of 0.926. CONCLUSION: The proposed CAD system shows the benefit of using ultrasomics features and can assist radiologists to diagnose CTS accurately.

Z-scan method to measure the nonlinear optical behavior of cells for evaluating the cytotoxic effects of chemotherapy and hyperthermia treatments.

The effects of new treatments must be investigated in vitro before using clinically or in vivo. The aim of this study was to introduce the Z-scan technique as a fast, accurate, inexpensive, and safe in vitro method to distinguish the cytotoxic effects of various treatments. C6 and OLN-93 cell lines were prepared and treated with Temozolomide (TMZ), radiofrequency hyperthermia (HT), and chemo-hyperthermia (HT+TMZ). The cytotoxic effects of different treatments on both cell lines were evaluated using colony formation assay and Z-scan method. The results of colony assay showed that the surviving fraction (SF) of C6 cells treated with TMZ, HT, and HT + TMZ were significantly decreased compared to the control group. Whereas, hyperthermia treatment had no significant effect on the SF of OLN-93 cells. The results of Z-scan technique indicated that the control group of C6 cells had the negative nonlinear refractive index (n2). Whereas, the C6 cells treated with HT, TMZ, and HT + TMZ had the positive n2 index. The sign of n2 index in the control and HT groups of OLN-93 cells was positive but treatment of cells with TMZ and HT + TMZ changed the sign of it. Moreover, with increasing the cytotoxic effects of different treatments, the SF value of both cell lines decreased and the magnitude of n2 index increased. The results of Z-scan technique were completely in line with the results of colony assay. Therefore, Z-scan method could distinguish the cytotoxic effects of various treatments by examining the nonlinear optical properties of the samples.

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.

Magneto-plasmonic nanoparticle mediated thermo-radiotherapy significantly affects the nonlinear optical properties of treated cancer cells.

In order to determine the level of cell damage in cancerous cells, current cytogenetic tests have limitations such as time consumption and high cost. The aim of this study was to demonstrate the ability of nonlinear refractive (NLR) index as a predictor of breast cell damage caused by magneto-plasmonic nanoparticle based thermo-radiotherapy treatments. MCF-7 breast cancer cells were subjected individually to the treatment of radiation, radio-frequency (RF) hyperthermia, and radiation + RF hyperthermia. These treatments were repeated in the presence of magneto-plasmonic nanoparticle (Au@IONP). The MTT and nonlinear optical assays were used to evaluate the damage induced by different treatment modalities. The results of MTT were correlated with Z-scan, as the magnitude of nonlinear refraction increased with higher intensity of induced cell damages. In this regard, the lowest cell viability (38 %,) and highest magnitude of NLR index (+28.12) were obtained from combination of radiation (at 4 Gy dose) and hyperthermia treatment in the presence of nanoparticles. The proposed optical index (NLR) indicated high capability and can be used as an auxiliary tool to monitor induced cell damage during different treatment strategies. This technique is fast, noninvasive, does not impose cost, and finally does not waste materials.

The capability of nonlinear optical characteristics as a predictor for cellular uptake of nanoparticles and cell damage.

Current methods for determining the cellular effects of a treatment modality need expensive materials and much time to provide a researcher with results. The aim of this study was to evaluate the potential of nonlinear optical characteristics of cancer cells using Z-scan technique to monitor the level of cellular uptake and cell damage caused by a nanotechnology based treatment modality. Two nanocomplexes were synthesized and characterized. The first one was made of alginate hydrogel co-loaded with cisplatin and gold nanoparticles (AuNPs) named as ACA nanocomplex. The second one, named as AA nanocomplex, was the same as ACA, but without cisplatin and this AA nanocomplex was considered as the control for ACA. Different groups of CT26 mouse colon cancer cell line received various treatments of cisplatin, ACA, and AA nanocomplexes and then the samples were prepared for Z-scan studies. The MTT assay was used to evaluate the cytotoxicity induced by different treatment modalities. Transmission electron microscopy (TEM) and inductively coupled plasma-mass spectrometry (ICP-MS) were used for qualitative and quantitative assessments of the level of AuNPs cellular uptake. The trend of nonlinear optical properties changes for treated cells was in agreement with MTT, TEM and ICP-MS results. Z-scan technique was able to successfully indicate the occurrence of cell damage. It was also capable to determine the intensity of cell damage induced by ACA nanocomplex in comparison to free cisplatin. Furthermore, Z-scan results showed that it was able to discriminate the differences of optical properties of the cells incubated with ACA nanocomplex for various incubation times. Nonlinear optical characteristics of a cell may be considered as a reliable indicator to predict the level of cellular effects induced by a nanotechnology based treatment modality. The protocol suggested in this article does not waste materials, not take much time to provide the results, and it is inexpensive technique.

Evaluation of nonlinear optical behavior of mouse colon cancer cell line CT26 in hyperthermia treatment.

Hyperthermia treatment can induce component changes on cell. This study explored the potential of Z-scan to improve accuracy in the identification of subtle differences in mouse colon cancer cell line CT26 during hyperthermia treatment. Twenty-one samples were subjected individually to treatment of hyperthermia at 41, 43, and 45 °C. Each hyperthermia treatment was done in six different time (15, 30, 45, 60, 75, and 90 min). Two optical setups were used to investigate the linear and nonlinear optical behavior of samples. Prior to the Z-scan technique, all samples were fixed with 1 mL of 5% paraformaldehyde. The linear optical setup indicated that extinction coefficient cannot monitor cell changes at different treatment regimes. But the nonlinear behavior of CT26 in all hyperthermia treatment regimens was different. By increasing the time and/or temperature of hyperthermia treatments, change in the sign of nonlinear refractive index from negative to positive occurred in earlier time intervals. This phenomenon was seen for 41, 43, and 45 °C in 75, 60, and 45 min, respectively. The results showed that the Z-scan technique is a reliable method with the potential to characterize cell changes during hyperthermia treatment regimes. Nonlinear refractive index can be used as a new index for evaluation of cell damage.

Scintigraphic texture analysis for assessment of renal allograft function.

PURPOSE: Early detection and monitoring of kidney function during the post-transplantation period is one of the most important issues for improving the accuracy of an initial diagnosis. The aim of this study was to evaluate texture analysis (TA) in scintigraphic imaging to detect changes in kidney status after transplantation. MATERIAL AND METHODS: Scintigraphic images were used for TA from a total of 94 kidney allografts (39 rejected and 55 non-rejected). Images corresponding to the frames at the 2nd, 5th, and 20th minute of the study were used to determine the optimum time point for analysis of differences in texture features between the rejected and non-rejected allografts. RESULTS: Linear discriminant analysis indicated the best performance at the fifth minute frame for classification of the rejected and non-rejected allografts with receiver operating characteristic curve (Az) of 0.982, corresponding to 91.89% sensitivity, 96.49% specificity, and 94.68% accuracy. Also, TA can differentiate acute tubular necrosis from acute rejection with Az of 0.953 corresponding to 88% sensitivity, 92.31% specificity, and 90.62% accuracy at the 5th minute frame. The best correlation between texture feature and kidney function was achieved at the 20th minute frame (r = -0.396) for glomerular filtration rate. CONCLUSIONS: TA has good potential for the characterisation of kidney failure after transplantation and can improve clinical diagnosis.

Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis.

PURPOSE: Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, and cervical lymph nodes (LNs) are the most common extrathyroid metastatic involvement. Early detection and reliable diagnosis of LNs can lead to improved cure rates and management costs. This study explored the potential of texture analysis for texture-based classification of tumour-free and metastatic cervical LNs of PTC in ultrasound imaging. MATERIAL AND METHODS: A total of 274 LNs (137 tumour-free and 137 metastatic) were explored using the texture analysis (TA) method. Up to 300 features were extracted for texture analysis in three normalisations (default, 3sigma, and 1-99%). Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features were classified by the first nearest neighbour classifier. RESULTS: Normalisation reflected improvement on the performance of the classifier; hence, the features under 3sigma normalisation schemes through FFPA (fusion Fisher plus the probability of classification error [POE] + average correlation coefficients [ACC]) features indicated high performance in classifying tumour-free and metastatic LNs with a sensitivity of 99.27%, specificity of 98.54%, accuracy of 98.90%, positive predictive value of 98.55%, and negative predictive value of 99.26%. The area under the receiver operating characteristic curve was 0.996. CONCLUSIONS: TA was determined to be a reliable method with the potential for characterisation. This method can be applied by physicians to differentiate between tumour-free and metastatic LNs in patients with PTC in conventional ultrasound imaging.

Evaluation of nonlinear optical differences between breast cancer cell lines SK-BR-3 and MCF-7; an in vitro study.

BACKGROUND: This study aimed to explore the potential of the Z-scan technique to improve accuracy in identifying SK-BR-3 and MCF-7 breast cancer cell lines. METHODS: Three in vitro samples were prepared for each breast cancer cell line. A closed-aperture Z-scan technique was used to measure the sign and magnitude of the nonlinear refractive index of each sample. Prior to the Z-scan, all samples were fixed with 1 mL of 5% paraformaldehyde. RESULTS: The sign of the nonlinear refractive indices of MCF-7 and SK-BR-3 breast cancer cell lines were negative and positive, respectively. The repeated Z-scan measurements for all samples of each cell line were similar. CONCLUSION: The results indicated that the proposed bio-optical method is a reliable method for characterizing differences in various breast cancer cell types. It is suggested that the nonlinear refractive index of cells be considered as an indicator for differentiating various breast cell lines from each other.

Predictive quantitative sonographic features on classification of hot and cold thyroid nodules.

PURPOSE: This study investigated the potentiality of ultrasound imaging to classify hot and cold thyroid nodules on the basis of textural and morphological analysis. METHODS: In this research, 42 hypo (hot) and 42 hyper-function (cold) thyroid nodules were evaluated through the proposed method of computer aided diagnosis (CAD) system. To discover the difference between hot and cold nodules, 49 sonographic features (9 morphological, 40 textural) were extracted. A support vector machine classifier was utilized for the classification of LNs based on their extracted features. RESULTS: In the training set data, a combination of morphological and textural features represented the best performance with area under the receiver operating characteristic curve (AUC) of 0.992. Upon testing the data set, the proposed model could classify the hot and cold thyroid nodules with an AUC of 0.948. CONCLUSIONS: CAD method based on textural and morphological features is capable of distinguishing between hot from cold nodules via 2-Dimensional sonography. Therefore, it can be used as a supplementary technique in daily clinical practices to improve the radiologists' understanding of conventional ultrasound imaging for nodules characterization.

Classification of Benign and Malignant Thyroid Nodules Using Wavelet Texture Analysis of Sonograms.

OBJECTIVES: The purpose of this study was to evaluate a computer-aided diagnostic system using texture analysis to improve radiologic accuracy for identification of thyroid nodules as malignant or benign. METHODS: The database comprised 26 benign and 34 malignant thyroid nodules. Wavelet transform was applied to extract texture feature parameters as descriptors for each selected region of interest in 3 normalization schemes (default, µ ± 3σ, and 1%-9%). Linear discriminant analysis and nonlinear discriminant analysis were used for texture analysis of the thyroid nodules. The first-nearest neighbor classifier was applied to features resulting from linear discriminant analysis. Nonlinear discriminant analysis features were classified by using an artificial neural network. Receiver operating characteristic curve analysis was used to examine the performance of the texture analysis methods. RESULTS: Wavelet features under default normalization schemes from nonlinear discriminant analysis indicated the best performance for classification of benign and malignant thyroid nodules and showed 100% sensitivity, specificity, and accuracy; the area under the receiver operating characteristic curve was 1. CONCLUSIONS: Wavelet features have a high potential for effective differentiation of benign from malignant thyroid nodules on sonography.

Classification of breast tumors using sonographic texture analysis.

OBJECTIVES: The purpose of this study was to evaluate a computer-aided diagnostic system with texture analysis to improve radiologists' accuracy in identification of breast tumors as malignant or benign. METHODS: The database included 20 benign and 12 malignant tumors. We extracted 300 statistical texture features as descriptors for each selected region of interest in 3 normalization schemes (default, µ - 3σ, and µ + 3σ, where µ and σ were the mean value and standard deviation, respectively, of the gray-level intensity and 1%-99%). Then features determined by the Fisher coefficient and the lowest probability of classification error + average correlation coefficient yielded the 10 best and most effective features. We analyzed these features under 2 standardization states (standard and nonstandard). For texture analysis of the breast tumors, we applied principle component, linear discriminant, and nonlinear discriminant analyses. First-nearest neighbor classification was performed for the features resulting from the principle component and linear discriminant analyses. Nonlinear discriminant analysis features were classified by an artificial neural network. Receiver operating characteristic curve analysis was used for examining the performance of the texture analysis methods. RESULTS: Standard feature parameters extracted by the Fisher coefficient under the default and 3σ normalization schemes via nonlinear discriminant analysis showed high performance for discrimination between benign and malignant tumors, with sensitivity of 94.28%, specificity of 100%, accuracy of 97.80%, and an area under the receiver operating characteristic curve of 0.9714. CONCLUSIONS: Texture analysis is a reliable method and has the potential to be used effectively for classification of benign and malignant tumors on breast sonography.