OBJECTIVE: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities.
METHODS: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively.
RESULTS: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution.
CONCLUSION: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.
METHODS: Patients with an admission diagnosis of suspected or confirmed infection and fulfilling at least two criteria for severe inflammatory response syndrome were included in this study. Patients' characteristics, vital signs, and laboratory values were used to identify prognostic factors for mortality. A scoring system was derived and validated. The primary outcome was the 28-day mortality rate.
RESULTS: A total of 440 patients were included in the study. The 28-day hospital mortality rate was 32.4 and 25.2% for the derivation (293 patients) and validation (147 patients) sets, respectively. Factors associated with a higher mortality were immune-suppressed state (odds ratio 4.7; 95% confidence interval 2.0-11.4), systolic blood pressure on arrival less than 90 mmHg (3.8; 1.7-8.3), body temperature less than 36.0°C (4.1; 1.3-12.9), oxygen saturation less than 90% (2.3; 1.1-4.8), hematocrit less than 0.38 (3.1; 1.6-5.9), blood pH less than 7.35 (2.0; 1.04-3.9), lactate level more than 2.4 mmol/l (2.27; 1.2-4.2), and pneumonia as the source of infection (2.7; 1.5-5.0). The area under the receiver operating characteristic curve was 0.81 (0.75-0.86) in the derivation and 0.81 (0.73-0.90) in the validation set. The SPEED (sepsis patient evaluation in the emergency department) score performed better (P=0.02) than the Mortality in Emergency Department Sepsis score when applied to the complete study population with an area under the curve of 0.81 (0.76-0.85) as compared with 0.74 (0.70-0.79).
CONCLUSION: The SPEED score predicts 28-day mortality in septic patients. It is simple and its predictive value is comparable to that of other scoring systems.
METHODS: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos.
RESULTS: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models.
CONCLUSION: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.
METHODS AND FINDINGS: We searched the major electronic databases Medline, Embase, and Google Scholar (January 1990-October 2018) without language restrictions. We included cohort studies on term pregnancies that provided estimates of stillbirths or neonatal deaths by gestation week. We estimated the additional weekly risk of stillbirth in term pregnancies that continued versus delivered at various gestational ages. We compared week-specific neonatal mortality rates by gestational age at delivery. We used mixed-effects logistic regression models with random intercepts, and computed risk ratios (RRs), odds ratios (ORs), and 95% confidence intervals (CIs). Thirteen studies (15 million pregnancies, 17,830 stillbirths) were included. All studies were from high-income countries. Four studies provided the risks of stillbirth in mothers of White and Black race, 2 in mothers of White and Asian race, 5 in mothers of White race only, and 2 in mothers of Black race only. The prospective risk of stillbirth increased with gestational age from 0.11 per 1,000 pregnancies at 37 weeks (95% CI 0.07 to 0.15) to 3.18 per 1,000 at 42 weeks (95% CI 1.84 to 4.35). Neonatal mortality increased when pregnancies continued beyond 41 weeks; the risk increased significantly for deliveries at 42 versus 41 weeks gestation (RR 1.87, 95% CI 1.07 to 2.86, p = 0.012). One additional stillbirth occurred for every 1,449 (95% CI 1,237 to 1,747) pregnancies that advanced from 40 to 41 weeks. Limitations include variations in the definition of low-risk pregnancy, the wide time span of the studies, the use of registry-based data, and potential confounders affecting the outcome.
CONCLUSIONS: Our findings suggest there is a significant additional risk of stillbirth, with no corresponding reduction in neonatal mortality, when term pregnancies continue to 41 weeks compared to delivery at 40 weeks.
SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015013785.
METHODS: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8.
RESULTS AND CONCLUSION: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.