Revealing the Boundaries of Selected Gastro-Intestinal (GI) Organs by Implementing CNNs in Endoscopic Capsule Images.

PURPOSE: The detection of where an organ starts and where it ends is achievable and, since this information can be delivered in real time, it could be quite important for several reasons. For one, by having the practical knowledge of the Wireless Endoscopic Capsule (WEC) transition through an organ's domain, we are able to align and control the endoscopic operation with any other possible protocol, i.e., delivering some form of treatment on the spot. Another is having greater anatomical topography information per session, therefore treating the individual in detail (not "in general"). Even the fact that by gathering more accurate information for a patient by merely implementing clever software procedures is a task worth exploiting, since the problems we have to overcome in real-time processing of the capsule findings (i.e., wireless transfer of images to another unit that will apply the necessary real time computations) are still challenging. This study proposes a computer-aided detection (CAD) tool, a CNN algorithm deployed to run on field programmable gate array (FPGA), able to automatically track the capsule transitions through the entrance (gate) of esophagus, stomach, small intestine and colon, in real time. The input data are the wireless transmitted image shots of the capsule's camera (while the endoscopy capsule is operating). METHODS: We developed and evaluated three distinct multiclass classification CNNs, trained on the same dataset of total 5520 images extracted by 99 capsule videos (total 1380 frames from each organ of interest). The proposed CNNs differ in size and number of convolution filters. The confusion matrix is obtained by training each classifier and evaluating the trained model on an independent test dataset comprising 496 images extracted by 39 capsule videos, 124 from each GI organ. The test dataset was further evaluated by one endoscopist, and his findings were compared with CNN-based results. The statistically significant of predictions between the four classes of each model and the comparison between the three distinct models is evaluated by calculating the p-values and chi-square test for multi class. The comparison between the three models is carried out by calculating the macro average F1 score and Mattheus correlation coefficient (MCC). The quality of the best CNN model is estimated by calculations of sensitivity and specificity. RESULTS: Our experimental results of independent validation demonstrate that the best of our developed models addressed this topological problem by exhibiting an overall sensitivity (96.55%) and specificity of (94.73%) in the esophagus, (81.08% sensitivity and 96.55% specificity) in the stomach, (89.65% sensitivity and 97.89% specificity) in the small intestine and (100% sensitivity and 98.94% specificity) in the colon. The average macro accuracy is 95.56%, the average macro sensitivity is 91.82%.

Tumor Diagnosis against Other Brain Diseases Using T2 MRI Brain Images and CNN Binary Classifier and DWT.

PURPOSE: Brain tumors are diagnosed and classified manually and noninvasively by radiologists using Magnetic Resonance Imaging (MRI) data. The risk of misdiagnosis may exist due to human factors such as lack of time, fatigue, and relatively low experience. Deep learning methods have become increasingly important in MRI classification. To improve diagnostic accuracy, researchers emphasize the need to develop Computer-Aided Diagnosis (CAD) computational diagnostics based on artificial intelligence (AI) systems by using deep learning methods such as convolutional neural networks (CNN) and improving the performance of CNN by combining it with other data analysis tools such as wavelet transform. In this study, a novel diagnostic framework based on CNN and DWT data analysis is developed for the diagnosis of glioma tumors in the brain, among other tumors and other diseases, with T2-SWI MRI scans. It is a binary CNN classifier that treats the disease "glioma tumor" as positive and the other pathologies as negative, resulting in a very unbalanced binary problem. The study includes a comparative analysis of a CNN trained with wavelet transform data of MRIs instead of their pixel intensity values in order to demonstrate the increased performance of the CNN and DWT analysis in diagnosing brain gliomas. The results of the proposed CNN architecture are also compared with a deep CNN pre-trained on VGG16 transfer learning network and with the SVM machine learning method using DWT knowledge. METHODS: To improve the accuracy of the CNN classifier, the proposed CNN model uses as knowledge the spatial and temporal features extracted by converting the original MRI images to the frequency domain by performing Discrete Wavelet Transformation (DWT), instead of the traditionally used original scans in the form of pixel intensities. Moreover, no pre-processing was applied to the original images. The images used are MRIs of type T2-SWI sequences parallel to the axial plane. Firstly, a compression step is applied for each MRI scan applying DWT up to three levels of decomposition. These data are used to train a 2D CNN in order to classify the scans as showing glioma or not. The proposed CNN model is trained on MRI slices originated from 382 various male and female adult patients, showing healthy and pathological images from a selection of diseases (showing glioma, meningioma, pituitary, necrosis, edema, non-enchasing tumor, hemorrhagic foci, edema, ischemic changes, cystic areas, etc.). The images are provided by the database of the Medical Image Computing and Computer-Assisted Intervention (MICCAI) and the Ischemic Stroke Lesion Segmentation (ISLES) challenges on Brain Tumor Segmentation (BraTS) challenges 2016 and 2017, as well as by the numerous records kept in the public general hospital of Chania, Crete, "Saint George". RESULTS: The proposed frameworks are experimentally evaluated by examining MRI slices originating from 190 different patients (not included in the training set), of which 56% are showing gliomas by the longest two axes less than 2 cm and 44% are showing other pathological effects or healthy cases. Results show convincing performance when using as information the spatial and temporal features extracted by the original scans. With the proposed CNN model and with data in DWT format, we achieved the following statistic percentages: accuracy 0.97, sensitivity (recall) 1, specificity 0.93, precision 0.95, FNR 0, and FPR 0.07. These numbers are higher for this data format (respectively: accuracy by 6% higher, recall by 11%, specificity by 7%, precision by 5%, FNR by 0.1%, and FPR is the same) than it would be, had we used as input data the intensity values of the MRIs (instead of the DWT analysis of the MRIs). Additionally, our study showed that when our CNN takes into account the TL of the existing network VGG, the performance values are lower, as follows: accuracy 0.87, sensitivity (recall) 0.91, specificity 0.84, precision 0.86, FNR of 0.08, and FPR 0.14. CONCLUSIONS: The experimental results show the outperformance of the CNN, which is not based on transfer learning, but is using as information the MRI brain scans decomposed into DWT information instead of the pixel intensity of the original scans. The results are promising for the proposed CNN based on DWT knowledge to serve for binary diagnosis of glioma tumors among other tumors and diseases. Moreover, the SVM learning model using DWT data analysis performs with higher accuracy and sensitivity than using pixel values.

The European Renal Association - European Dialysis and Transplant Association (ERA-EDTA) Registry Annual Report 2015: a summary.

BACKGROUND: This article summarizes the European Renal Association - European Dialysis and Transplant Association (ERA-EDTA) Registry's 2015 Annual Report. It describes the epidemiology of renal replacement therapy (RRT) for end-stage renal disease (ESRD) in 2015 within 36 countries. METHODS: In 2016 and 2017, the ERA-EDTA Registry received data on patients who were undergoing RRT for ESRD in 2015, from 52 national or regional renal registries. Thirty-two registries provided individual patient-level data and 20 provided aggregated-level data. The incidence, prevalence and survival probabilities of these patients were determined. RESULTS: In 2015, 81 373 individuals commenced RRT for ESRD, equating to an overall unadjusted incidence rate of 119 per million population (pmp). The incidence ranged by 10-fold, from 24 pmp in Ukraine to 232 pmp in the Czech Republic. Of the patients commencing RRT, almost two-thirds were men, over half were aged ≥65 years and a quarter had diabetes mellitus as their primary renal diagnosis. Treatment modality at the start of RRT was haemodialysis for 85% of the patients, peritoneal dialysis for 11% and a kidney transplant for 4%. By Day 91 of commencing RRT, 82% of patients were receiving haemodialysis, 13% peritoneal dialysis and 5% had a kidney transplant. On 31 December 2015, 546 783 individuals were receiving RRT for ESRD, corresponding to an unadjusted prevalence of 801 pmp. This ranged throughout Europe by more than 10-fold, from 178 pmp in Ukraine to 1824 pmp in Portugal. In 2015, 21 056 kidney transplantations were performed, equating to an overall unadjusted transplant rate of 31 pmp. This varied from 2 pmp in Ukraine to 94 pmp in the Spanish region of Cantabria. For patients commencing RRT during 2006-10, the 5-year unadjusted patient survival probabilities on all RRT modalities combined was 50.0% (95% confidence interval 49.9-50.1).

The European Renal Association - European Dialysis and Transplant Association Registry Annual Report 2014: a summary.

Background: This article summarizes the European Renal Association - European Dialysis and Transplant Association Registry's 2014 annual report. It describes the epidemiology of renal replacement therapy (RRT) for end-stage renal disease (ESRD) in 2014 within 35 countries. Methods: In 2016, the ERA-EDTA Registry received data on patients who in 2014 where undergoing RRT for ESRD, from 51 national or regional renal registries. Thirty-two registries provided individual patient level data and 19 provided aggregated patient level data. The incidence, prevalence and survival probabilities of these patients were determined. Results: In 2014, 70 953 individuals commenced RRT for ESRD, equating to an overall unadjusted incidence rate of 133 per million population (pmp). The incidence ranged by 10-fold; from 23 pmp in the Ukraine to 237 pmp in Portugal. Of the patients commencing RRT, almost two-thirds were men, over half were aged ≥65 years and a quarter had diabetes mellitus as their primary renal diagnosis. By day 91 of commencing RRT, 81% of patients were receiving haemodialysis. On 31 December 2014, 490 743 individuals were receiving RRT for ESRD, equating to an unadjusted prevalence of 924 pmp. This ranged throughout Europe by more than 10-fold, from 157 pmp in the Ukraine to 1794 pmp in Portugal. In 2014, 19 406 kidney transplantations were performed, equating to an overall unadjusted transplant rate of 36 pmp. Again this varied considerably throughout Europe. For patients commencing RRT during 2005-09, the 5-year-adjusted patient survival probabilities on all RRT modalities was 63.3% (95% confidence interval 63.0-63.6). The expected remaining lifetime of a 20- to 24-year-old patient with ESRD receiving dialysis or living with a kidney transplant was 21.9 and 44.0 years, respectively. This was substantially lower than the 61.8 years of expected remaining lifetime of a 20-year-old patient without ESRD.

Renal replacement therapy in Europe: a summary of the 2013 ERA-EDTA Registry Annual Report with a focus on diabetes mellitus.

BACKGROUND: This article provides a summary of the 2013 European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry Annual Report (available at http://www.era-edta-reg.org), with a focus on patients with diabetes mellitus (DM) as the cause of end-stage renal disease (ESRD). METHODS: In 2015, the ERA-EDTA Registry received data on renal replacement therapy (RRT) for ESRD from 49 national or regional renal registries in 34 countries in Europe and bordering the Mediterranean Sea. Individual patient data were provided by 31 registries, while 18 registries provided aggregated data. The total population covered by the participating registries comprised 650 million people. RESULTS: In total, 72 933 patients started RRT for ESRD within the countries and regions reporting to the ERA-EDTA Registry, resulting in an overall incidence of 112 per million population (pmp). The overall prevalence on 31 December 2013 was 738 pmp (n = 478 990). Patients with DM as the cause of ESRD comprised 24% of the incident RRT patients (26 pmp) and 17% of the prevalent RRT patients (122 pmp). When compared with the USA, the incidence of patients starting RRT pmp secondary to DM in Europe was five times lower and the incidence of RRT due to other causes of ESRD was two times lower. Overall, 19 426 kidney transplants were performed (30 pmp). The 5-year adjusted survival for all RRT patients was 60.9% [95% confidence interval (CI) 60.5-61.3] and 50.6% (95% CI 49.9-51.2) for patients with DM as the cause of ESRD.

Renal replacement therapy in Europe: a summary of the 2012 ERA-EDTA Registry Annual Report.

BACKGROUND: This article summarizes the 2012 European Renal Association-European Dialysis and Transplant Association Registry Annual Report (available at www.era-edta-reg.org) with a specific focus on older patients (defined as ≥65 years). METHODS: Data provided by 45 national or regional renal registries in 30 countries in Europe and bordering the Mediterranean Sea were used. Individual patient level data were received from 31 renal registries, whereas 14 renal registries contributed data in an aggregated form. The incidence, prevalence and survival probabilities of patients with end-stage renal disease (ESRD) receiving renal replacement therapy (RRT) and renal transplantation rates for 2012 are presented. RESULTS: In 2012, the overall unadjusted incidence rate of patients with ESRD receiving RRT was 109.6 per million population (pmp) (n = 69 035), ranging from 219.9 pmp in Portugal to 24.2 pmp in Montenegro. The proportion of incident patients ≥75 years varied from 15 to 44% between countries. The overall unadjusted prevalence on 31 December 2012 was 716.7 pmp (n = 451 270), ranging from 1670.2 pmp in Portugal to 146.7 pmp in the Ukraine. The proportion of prevalent patients ≥75 years varied from 11 to 32% between countries. The overall renal transplantation rate in 2012 was 28.3 pmp (n = 15 673), with the highest rate seen in the Spanish region of Catalonia. The proportion of patients ≥65 years receiving a transplant ranged from 0 to 35%. Five-year adjusted survival for all RRT patients was 59.7% (95% confidence interval, CI: 59.3-60.0) which fell to 39.3% (95% CI: 38.7-39.9) in patients 65-74 years and 21.3% (95% CI: 20.8-21.9) in patients ≥75 years.