Automated Assessment of Cardiothoracic Ratios on Chest Radiographs Using Deep Learning

Introduction: The cardiothoracic ratio (CTR) is a quantitative measure of cardiac size that can measured from chest radiography (CXR). Although radiologists using digital workstations possess the ability to calculate CTR, clinical demands prevent calculation for every case. In this study, the efficacy of a deep convolutional neural network (dCNN) to assess CTR was evaluated. Methods: 611 HIPAA-compliant de-identified CXRs were obtained from [institution blinded] and public databases. Using ImageJ, a board-certified radiologist (reader #1) and a medical student (reader #2), measured the CTR by marking four pixels on all CXRs: the right- and left-most chest wall, the right- and left-most heart border. The Tensorflow framework (v2.0, Google LLC, Mountain View, CA) and the Keras library (v2.3, https://keras.io) were used to train the dCNN. The images were split into training (511 images), validation (50 images), and test (50 images). U-Net network architecture with an Intersection over Union loss function was employed to predict oval masks on new CXRs and calculate the CTR. Results: 45 test cases were analyzed. The mean absolute difference in the calculated CTR was 0.026 (stdev: 0.039) for reader 1 vs dCNN, 0.024 (stdev: 0.039) for reader 2 vs. dCNN, and 0.022 (stdev: 0.024) for reader 1 vs. reader 2. The intra-class correlation coefficient was 0.84 (95% CI: 0.73-0.91), 0.84 (95% CI: 0.72-0.91), 0.92 (95% CI: 0.822-0.958) for reader 1 vs. dCNN, reader 2 vs. dCNN, and reader 1 vs. reader 2, respectively. Discussion: The dCNN trained in this study outputted similar CTR measurements to the human readers with the dCNN achieving good reliability with the human readers and the human readers achieving excellent reliability among themselves. This study proves the feasibility of using a dCNN to perform automated CTR assessment from CXR. Future improvements to the algorithm can allow the dCNN to closely approach the expected limits of inter-observer human agreement

Hepatocellular Carcinoma Treated with Microwave Ablation Prior to Liver Transplantation

Introduction: Ablation is a minimally invasive procedure that limits local liver tumor progression and prolongs patients’ transplantation eligibility. Microwave ablation (MWA) utilizes higher temperatures than the standard of care, radiofrequency ablation (RFA), which increases efficiency. Meta-analyses compared MWA with RFA for the treatment of HCC and showed similar efficacy and safety between these modalities. However, limited pathologic data exists determining whether explanted tumors remained viable after MWA. Methods: Our database was reviewed retrospectively for patients with HCC who underwent MWA prior to liver transplantation between 2013 and 2019. Patient demographics, etiology of disease, tumor size, procedure details, bilirubin, MELD, and Child-Pugh score were reviewed. Tumors were classified as viable or nonviable based on pathology. Imaging and clinical follow-up were available for surveillance and post-transplant. Results: 29 patients (23 males, 6 females) with 40 tumors underwent MWA. The average patient age was 60 years. The mean tumor size was 2.2 cm (range 1-3.7). Twenty-six patients were alive at follow-up. Pathological analysis showed 38 of the 40 tumors ablated to be non-viable at explant. Imaging prior to transplant reported one case with recurrent tumor at the ablation site and another case as equivocal. No cases of metastatic HCC were identified by imaging post-transplant. Discussion: Previous studies have not included this pathologic data. Determining tumor viability provides valuable information regarding whether tumors are likely to recur locally, even after transplantation. These results suggest that MWA is an effective treatment of small HCC prior to transplant with a low incidence of local tumor recurrence

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. The radiographs were fed into each deep convolutional neural network, which included untrained and pretrained models. The Tensorflow framework was used for Inception V3, ResNet50, and DenseNet. Images were split into training (4745), validation (630), and test (100). Both real-time and preprocessing image augmentation strategies were performed. Receiver operating characteristic (ROC) and area under the curve (AUC) on the test data were used to assess the models. Statistical differences among the AUCs were obtained. p<0.05 was considered statistically significant. The pretrained Inception V3, which had an AUC of 0.87 (95 CI; 0.80-0.94), performed statistically significantly better (p<.001) than the untrained Inception V3, with an AUC of 0.60 (95 CI; 0.52-0.68). The pretrained Inception V3 also had the highest AUC overall, as compared with ResNet50 and DenseNet121, with AUC values ranging from 0.82 to 0.85. Each pretrained network outperformed its untrained counterpart. (p<0.05). Deep learning demonstrates promise in differentiating critical vs. non-critical placement with an AUC of 0.87. Pretrained networks outperformed untrained ones in all cases. DCNNs may allow for more rapid identification and communication of critical feeding tube malpositions

Service deployment strategy for predictive analysis of FinTech IoT applications in edge networks

The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for financial technology (FinTech). The emergence of the next-generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyze the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this article, we develop a ranked-based service deployment strategy and an artificial intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users' satisfaction ratio. Besides that, an efficient service deployment strategy is developed using $Hall's$ theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVMs) algorithm is used at edge networks for analyzing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively. © 2014 IEEE

Reversing the effects of androgen-deprivation therapy in men with metastatic castration-resistant prostate cancer

OBJECTIVE: To investigate whether bipolar androgen therapy (BAT), involving rapid cyclic administration of high-dose testosterone, as a novel treatment for metastatic castration-resistant prostate cancer (mCRPC) promotes improvements in body composition and associated improvements in lipid profiles and quality of life. PATIENTS AND METHODS: Men from two completed trials with computed tomography imaging at baseline and after three cycles of BAT were included. Cross-sectional areas of psoas muscle, visceral and subcutaneous fat were measured at the L3 vertebral level. Functional Assessment of Chronic Illness Therapy - Fatigue questionnaire and 36-item short-form health survey were used to assess quality of life. RESULTS: The 60 included patients lost a mean (sd) of 7.8 (8.2)% of subcutaneous fat, 9.8 (18.2)% of visceral fat, and gained 12.2 (6.7)% muscle mass. Changes in subcutaneous and visceral fat were positively correlated with each other (Spearman\u27s correlation coefficient 0.58, 95% confidence interval 0.35-0.71) independent of the effects of age, body mass index, and duration of androgen-deprivation therapy. Energy, physical function, and measures of limitations due to physical health were all significantly improved at 3 months. The improvements in body composition were not correlated with decreases in lipid levels or observed improvements in quality of life. CONCLUSIONS: In the present study, BAT was associated with significant improvements in body composition, lipid parameters, and quality of life. This has promising implications for the long-term health of men with mCRPC