CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70�75. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80�98, but similar accuracy of 70. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95 compared to radiologists (70). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership. © 2021, The Author(s)

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

OBJECTIVE: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. METHODS: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1-5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. RESULTS: All proposed models provide accuracy greater than 83.0 for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8 accuracy, followed by Xception model with an accuracy of 86.0. The sensitivity of the proposed models was greater than 91.0. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. CONCLUSION: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. ADVANCES IN KNOWLEDGE: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0 for binary classification

船の静復原力の実測

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. © Pol J Radiol

Optimization of Image Quality and Patient Dose in Digital Radiography of the Chest

BACKGROUND AND OBJECTIVE: Digital systems have been replacing with screen-film analogue systems in diagnostic radiology departments, rapidly. Despite the differences in the properties of new x-ray imaging detectors, the same radiographic protocols that had been used for radiographic film-screen are used for digital imaging systems, without any review yet. In this study, the image quality and the patient dose in digital imaging of the chest are evaluated and optimized. METHODS: Two digital radiography machines from two separated hospitals (Imam Khomeini and Bu Ali Hospitals-Sari) have been used in this experimental research. Imaging and dose measurement are carried out at different source to phantom distances and kilo-voltages. For measurement of the image quality, a contrast-detail radiography (CDRAD) phantom is used. For evaluation of optimization, the Inverse Image Quality Figure per patient dose squared (IQFinv/E2) is used. FINDINGS: Evaluation of measured data for optimization shows that for both of these two digital radiography machines, despite of increasing in patent dose, with reducing of kilo-voltage, the IQFinv/E2 is increased. The maximum values of this parameter for Imam Khomeini and Bu Ali Hospitals are measured 0.0180 and 0.0083, respectively.  CONCLUSION: The results of this study indicate that despite the traditional notion of using higher kilo-voltages for chest radiography, with increasing kilo-voltage, the ratio of image quality per patient dose is reduced. So, for optimization of chest radiography, as much as possible the kilo-voltage should be reduced based on the size of patient and clinical purpose

Application of Radiomics in Radiotherapy: Challenges and Future Prospects

Background and Objective: Specific treatment for each patient based on their clinical data is one of the medical prospects of the future. Using data mining and machine learning techniques based on computer science in extracting the quantitative features of an image to improve the process of diagnosis, prognosis, prediction and response to cancer treatment is known as radiomics. This article examines the workflow, findings, challenges ahead, and the role of radiomics in precision medicine and individual therapy. Methods: In this review article, we searched well-known indexes such as ISC, web of science, Google Scholar, Scopus, PubMed without time limit and based on the keywords radiomics, radiotherapy, cancer and quantitative imaging and relevant articles were collected. Findings: Radiomics is a combination of everyday computer-aided diagnosis, machine learning methods, deep learning and human skills that can be used for quantitative description of the phenotypes of cancerous tumors. Image collection and processing, tumor segmentation, extraction of features, processing and modeling are some of the basic steps of the process of radiomics. Computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound (US) methods are among the used images. Conclusion: According to the results of this study, the prerequisite for the clinical implementation of radiomics is the elimination of deficiencies such as the dependence of the features on the imaging parameters, and the unrepeatability of the features. Therefore, a comprehensive approach should be adopted, stable and reproducible patterns should be developed to accept radiomics as a clinical prognostic tool

Rectal retractor application during image-guided dose-escalated prostate radiotherapy Verwendung des Mastdarmretraktors während der bildgeführten dosiseskalierten Strahlentherapie der Prostata

Purpose: To investigate efficacy of a rectal retractor (RR) on rectal dose during image-guided dose-escalated prostate three-dimensional conformal radiotherapy (3DCRT). Patients and methods: In all, 21 patients with localized prostate cancer were treated with a RR for 3DCRT in 40�� 2�Gy. Patient underwent two scans for radiotherapy planning, without and with RR. RR was used for the first half of the treatment sessions. Two plans were created for each patient to compare the effect of RR on rectal doses. PTW-31014 Pinpoint chamber embedded within RR was used for in vivo dosimetry in 6 of 21 patients. The patient tolerance and acute rectal toxicity were surveyed during radiotherapy using Common Terminology Criteria for Adverse Events (CTCAE) v.4.0. Results: Patients tolerated the RR well during 20 fractions with mild degree of anal irritation. Using a RR significantly reduced the rectal wall (RW), anterior RW and posterior RW dose�volume parameters. The average RW D mean was 29.4 and 43.0�Gy for plans with and without RR, respectively. The mean discrepancy between the measured dose and planned dose was �3.8 (±4.9). Grade 1 diarrhea, rectal urgency and proctitis occurred in 4, 2 and 3 cases, respectively. There were no grade �2 acute rectal toxicities during the treatment. Conclusion: Rectal retraction resulted in a significant reduction of rectal doses with a safe toxicity profile, which may reduce rectal toxicity. Dosimeter inserted into the RR providing a practical method for in vivo dosimetric verification. Further prospective clinical studies will be necessary to demonstrate the clinical advantage of RR. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

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. © 2018 Elsevier B.V

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. © 2018 Elsevier B.V