Accuracy Of Conventional Methods Research Articles

Near-infrared fluorescence clip guided robot-assisted minimally invasive esophagectomy for esophagogastric junction cancer: a case report

BackgroundAlthough treatment strategy differs between countries, surgery remain the mainstay of treatment for localized esophagogastric junction cancer. Tumor proximal margin is one of the critical factors to achieve curative resection during esophagogastric junction cancer surgery. Thus, an accurate preoperative marking of the tumor is essential, however, accuracy of conventional methods was highly dependent upon endoscopists skills. Here, we present a case of a patient with locally advanced Siewert type II esophagogastric junction cancer treated by robot-assisted minimally invasive esophagectomy utilizing near-infrared fluorescence clip as a novel marking method.Case presentationA male patient in his 70 s was diagnosed as having cT3N1M1 esophagogastric junction cancer and was treated by immunochemotherapy followed by surgery. Near-infrared fluorescence marking clips were placed 2 cm above the tumor proximal end under endoscopy prior to surgery. Location of the clips were confirmed by fluorescence imaging during robotic surgery, and the esophagus was dissected by a surgical stapler without intraoperative endoscopy. Histopathologically, the tumor was diagnosed as ypT2N0M0, with negative margins.ConclusionsNear-infrared fluorescence clip may enable easy, safe, and an accurate preoperative marking, and may be considered during endoscopic surgeries.

A hybrid model for depression detection using deep learning

Millions of people are suffering from mental illness due to unavailability of early treatment and services for depression detection. It is the major reason for anxiety disorder, bipolar disorder, sleeping disorder, depression and sometimes it may lead to self-harm and suicide. Thus, it is a very challenging task to recognize people who are suffering from mental health disorders and provide them treatments as early as possible. Conventionally, depression detection was done through patients' interviews and PHQ scores, accuracy of conventional methods is very less. In this work, a hybrid model is proposed for depression detection using deep learning algorithms, which mainly combines textual features and audio features of patient's responses. To study behavioral characteristics of depressed patient's, DAIC-WoZ database is used. Proposed method consists of three components; first, a textual CNN model in which a CNN model is trained with only text features, second, an audio CNN model in which CNN model is trained with only audio features and third, a combination of audio and textual model named as hybrid model in which LSTM algorithms are applied. An improved version of LSTM model named as Bi-LSTM model is also used in the proposed work. In results, training accuracy, training loss, validation accuracy and validation loss is calculated for all the mentioned models. The results shows that deep learning is a better solution for depression detection in which accuracy of textual CNN model is 92% whereas accuracy of audio CNN model is 98% and loss of textual CNN is 0.2 whereas loss of audio CNN is 0.1. These results show that audio CNN is a good model for depression detection. It performs better as compared to textual CNN model. It is also observed that Bi-LSTM has better learning rate as compared to other models with accuracy 88% and validation accuracy 78%. There are some parameters such as precision, F1-score, recall and support are found for evaluation of models. In results, graphs for training loss, validation loss, training accuracy and validation accuracy are plotted. At last, by using confusion matrix depression can be detected for textual CNN Model, audio CNN model, LSTM model and Bi-LSTM against true label and predicted label.

High rates of misidentification of uncommon Candida species causing bloodstream infections using conventional phenotypic methods

Candidemia caused by uncommon Candida species is increasing and misidentification may compromise optimal antifungal therapy. This multicenter study aimed to evaluate the accuracy of species-level identification of uncommon Candida. Uncommon causative species of candidemia identified in routine laboratories using CHROMagar, API-32C and VITEK-2 Yeast ID system were collected from July 2011 to June 2014. These isolates were further identified using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) system and sequencing of the internal transcribed spacer and 28S rRNA gene. Susceptibility of the isolates was determined. Of 85 isolates evaluated, Candida guilliermondii (n=36) was the most common, followed by Candid sake (n=7) and Candida famata (n=4). Using DNA-sequencing analysis as standard, none of C. sake and C. famata was correct, while VITEK MS correctly identified 10 of the 11 isolates. With the exclusion of one unspecified Candida by DNA-sequencing methods, the accuracy of conventional methods and VITEK MS was 64.3% and 86.9%, respectively (p=0.001). Eight isolates were confirmed to be yeasts other than Candida. Compared with other Candida species, C. guilliermondii showed elevated minimal inhibitory concentration of echinocandins. Misidentification of uncommon Candida species was common using the conventional methods, especially for C. sake and C. famata. MALDI-TOF MS assisted by DNA-sequencing methods should be considered.

The Nonlocal Sparse Reconstruction Algorithm by Similarity Measurement with Shearlet Feature Vector

Due to the limited accuracy of conventional methods with image restoration, the paper supplied a nonlocal sparsity reconstruction algorithm with similarity measurement. To improve the performance of restoration results, we proposed two schemes to dictionary learning and sparse coding, respectively. In the part of the dictionary learning, we measured the similarity between patches from degraded image by constructing the Shearlet feature vector. Besides, we classified the patches into different classes with similarity and trained the cluster dictionary for each class, by cascading which we could gain the universal dictionary. In the part of sparse coding, we proposed a novel optimal objective function with the coding residual item, which can suppress the residual between the estimate coding and true sparse coding. Additionally, we show the derivation of self-adaptive regularization parameter in optimization under the Bayesian framework, which can make the performance better. It can be indicated from the experimental results that by taking full advantage of similar local geometric structure feature existing in the nonlocal patches and the coding residual suppression, the proposed method shows advantage both on visual perception and PSNR compared to the conventional methods.

Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine

The purpose of this article is to develop a new method to estimate annual energy output for a given wind turbine in any region which should be easy to use and has satisfactory accuracy. To do this, hourly wind speeds of 25 different stations in Netherlands, output power curve of S47 wind turbine and fuzzy modeling techniques and artificial neural networks were used and a model is developed to estimate annual energy output for S47 wind turbine in different regions. Since this model has three inputs (average wind speed, standard deviation of wind speed, and air density of that region), this model is easy to use. The accuracy of this method is compared with the accuracy of conventional methods and it is shown that this new method performs better. Thereafter, we have shown that by making some small changes to this proposed model, other pitch control wind turbines could be modeled too. As an example, we have modeled E82 wind turbine based on the model developed for S47 and it is shown that this model has still satisfactory accuracy.

Ways of increasing the accuracy of thermophysical measurements. 1. Increasing the accuracy of conventional methods

New methods are described that increase the accuracy and speed of thermophysical measurements. Principles of the design of appropriate instruments and primary measuring transducers are presented, and their metrological evaluation is given.

Three-dimensional dose calculations for radiation treatment planning

We discuss geometric and physical aspects of dose calculational methodologies as developed and implemented in three-dimensional treatment planning systems at four institutions participating in an NCI Contract for the Evaluation of High Energy Photon External Beam Treatment Planning. The geometric aspects include such issues as unconventional beam orientations, 3-D patient geometry, image data requirements, and pathlength calculation in 3-D. The physical aspects deal primarily with the formalisms employed in dose calculations. Exact calculation of dose is impractical due to the complex manner in which radiation interacts with matter. Approximations have to be made which limit the accuracy of dose calculations. For a number of situations of clinical interest, especially for regions where electronic equilibrium does not exist, the accuracy of conventional methods of dose calculations is suspect. New, more accurate methods have been proposed but have not been implemented to date. Three-dimensional dose calculations are very time consuming with currently available general purpose computers. With the development of the next generation of computers and other ancillary hardware and with continuously evolving algorithms, accurate and fast three-dimensional dose calculations should become affordable for the radiotherapy community in the near future.