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Abstract The cost estimation and assessment of healthcare waste treatment systems (HCWTSs) for preventing financial and environmental damage are essential. This work reports economic analyses of treatment of hazardous–infectious waste based on WHO approach in HCWTSS of 43 hospitals in Tehran, Iran. The waste generation rate for total hospital waste in 43 HCWTSS was 4.42 ± 2.77 kg/active-bed/day. The mean of chemical, sharps, infectious, and general wastes in 43 HCWTSS were 13.79 ± 19.71, 30.29 ± 37.46, 336.28 ± 291.31, and 539.6 ± 383.13 kg/day, respectively. Economic analyses proved that general hospitals spent 1.63 times more than specialized hospitals on treating hazardous–infectious waste per year. The annual cost of treating each kilogram of hazardous healthcare waste in studied HCWTSS was 0.3 dollars. A range of total annual costs in 43 HCWTSS was limited to 7.9–118 thousand dollars. The results of ANOVA test demonstrated that the age and performance levels of hospitals significantly affect the annual capital and operating costs, respectively. Hence, improving recycling knowledge and increasing source-separated recycling should be considered to control the costs in HCWTSS. The results of this work have implications for the hospital managers in especially developing countries to evaluate previously unknown economic analyses and policies and take action to control wasted costs in HCWTSS.

Abstract The presence or absence of spontaneous retinal venous pulsations (SVP) provides clinically significant insight into the hemodynamic status of the optic nerve head. Reduced SVP amplitudes have been linked to increased intracranial pressure and glaucoma progression. Currently, monitoring for the presence or absence of SVPs is performed subjectively and is highly dependent on trained clinicians. In this study, we developed a novel end-to-end deep model, called U3D-Net, to objectively classify SVPs as present or absent based on retinal fundus videos. The U3D-Net architecture consists of two distinct modules: an optic disc localizer and a classifier. First, a fast attention recurrent residual U-Net model is applied as the optic disc localizer. Then, the localized optic discs are passed on to a deep convolutional network for SVP classification. We trained and tested various time-series classifiers including 3D Inception, 3D Dense-ResNet, 3D ResNet, Long-term Recurrent Convolutional Network, and ConvLSTM. The optic disc localizer achieved a dice score of 95% for locating the optic disc in 30 milliseconds. Amongst the different tested models, the 3D Inception model achieved an accuracy, sensitivity, and F1-Score of 84 ± 5%, 90 ± 8%, and 81 ± 6% respectively, outperforming the other tested models in classifying SVPs. To the best of our knowledge, this research is the first study that utilizes a deep neural network for an autonomous and objective classification of SVPs using retinal fundus videos.

Abstract Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection.