Effectively Detecting Left Bundle Branch Block False Defects in Myocardial Perfusion Imaging (MPI) with a Convolutional Neural Network (CNN).

Left bundle branch block (LBBB) is a frequent source of false positive MPI reports, in patients evaluated for coronary artery disease. PURPOSE: In this work, we evaluated the ability of a CNN-based solution, using transfer learning, to produce an expert-like judgment in recognizing LBBB false defects. METHODS: We collected retrospectively, MPI polar maps, of patients having small to large fixed anteroseptal perfusion defect. Images were divided into two groups. The LBBB group included patients where this defect was judged as false defect by two experts. The LAD group included patients where this defect was judged as a true defect by two experts. We used a transfer learning approach on a CNN (ResNet50V2) to classify the images into two groups. RESULTS: After 60 iterations, the reached accuracy plateau was 0.98, and the loss was 0.19 (the validation accuracy and loss were 0.91 and 0.25, respectively). A first test set of 23 images was used (11 LBBB, and 12 LAD). The empiric ROC (Receiver operating characteristic) Area was estimated at 0.98. A second test set (18x2 images) was collected after the final results. The ROC area was estimated again at 0.98. CONCLUSION: Artificial intelligence, using CNN and transfer learning, could reproduce an expert-like judgment in differentiating between LBBB false defects, and LAD real defects.

DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning.

Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

Changes in the expression of somatostatin receptor imaging following Y-90 lanreotide therapy for carcinoid tumor: a flare response?

A 53-year-old man with In-111 octreotide-positive metastatic hepatic carcinoid was referred for Y-90 lanreotide therapy. A diagnostic In-111 lanreotide scan, performed to assess suitability for therapy, showed less uptake in lesions compared with In-111 octreotide. After 3 therapy doses of Y-90 lanreotide, a repeat In-111 lanreotide scan showed intense uptake in old lesions, appearance of new lesions, and uptake in the spleen. This was associated with improvement in flushing and regression of liver size. Computed tomography scan showed stable disease. Increased expression of somatostatin receptors has been observed with In-111 octreotide but not with In-111 lanreotide. If this is a flare response, then pretreatment with "cold" lanreotide may be beneficial before Y-90 lanreotide therapy.