Re: Deep Fritz 14 Crack Download
Malena Bower
Background: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality.
Methods: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data.
Conclusions: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.
Whether you have been amazed by the TikToks of Tom Cruise dating Paris Hilton or concerned by President Obama laying out uncharacteristic statements, you must have heard about deep fake technology and its ability to create realistic videos by now.
While the results from deep fake are nothing but amazing, they also bring about a variety of questions about its ethical and societal impact. But before you get into exploring those topics, you have to understand exactly what is deep fake and how does it create realistic videos that make you look twice.
With that being said, not every deep fake that is generated and distributed online has the disclosure of being a deep fake in the first place. This makes it crucial for every internet user to learn deep fake meaning as well as its overall impact on the media that we consume.
A deep fake is made with the help of AI and manual manipulation of visual media. Through AI, the creators of deep fakes are able to train the image or video generation system on hundreds or thousands of images of the person that they are going to integrate in the deep fake. Through manual manipulation, they are able to adjust contrasting issues such as video quality and face edges to make the video seem as real as possible.
This means that creating a deep fake is not an easy feat. However, many online deep fake generators allow you to make your own deep fake within a few clicks, especially when it comes to celebrities. It is because these systems have been trained with the hundreds and thousands of images of celebrities that are needed to make impressive deep fakes.
In turn, even after learning what is deep fake, making your own deep fake of a non-celebrity can be difficult. While you can use online deep fake generators, you can often tell them apart from real videos and images.
The exploration of deep fake meaning has blown up in popularity only recently. But the first deep fake was made way before the trend started to take TikTok by storm. In fact, the first popular iteration of deep fake was made possible all the way back in 1997, when the software Video Rewrite made its presence known.
But as entertaining and positively enthralling deep fake videos can be in this context, they also pose grave ethical concerns for the content that they can generate. That is where you need to look at what is deep fake able to achieve when it falls in the hands of bad actors.
Similar to any piece of technology or basically any type of invention, deep fake has its pros and cons. While the technology opens doors to create media that was not possible before, it also creates opportunities for harmful parties to develop images and videos that can leave lasting harm on the people and groups involved.
This use of deep fake has led people to perceive it as a harmful technology that needs to be controlled or stopped in its tracks. Nonetheless, advocates of deep fake such as Umé who generates content for the Tom Cruise deep fake account on TikTok believe in the positive contributions of the technology. This includes use cases such as creating videos using actors or figures from yesteryears.
After learning what is deep fake and what does it do, you can better compare the advantages and drawbacks of the technology across a number of aspects. The technology may likely see widespread adoption in the near future. But if and when that happens, you can make an informed decision about your participation in the spread and usage of this technology.
When TV shows and movies need to create flashback scenes, they usually have to look for younger actors who may or may not look like their counterparts. With deep fake, creators can deage existing actors for easier production and better visual impact.
After learning what is deep fake, it is easy to determine that the technology can be a double-edged sword. But as long as you are aware of the risk as well as advantages of the technology, you can make an informed decision about how to consume or use media that is generated through this approach.
With that being said, it is important that deep fake meaning gets a larger exposure to everyday audiences through clear disclosures in generated media. While it may break the illusion of the technology, it will help people understand the reality of the content that they are actively consuming.
MRI of the left knee joint of a 30-year-old male patient. a A coronal short-tau inversion recovery image of the body of both menisci. b A sagittal fat-suppressed intermediate-weighted image at the junction of the posterior horn to the body of the medial meniscus. c The output of the deep convolutional neural network, which calculates a probability of a tear of the medial and lateral meniscus as well as provides a heatmap depicting the location of the suspected tear. A horizontal meniscus tear is present at the body with extension to the posterior horn to the medial meniscus (arrows). Knee arthroscopy confirmed the tear of the medial meniscus. Both readers and the deep convolutional neural network diagnosed the medial meniscus tear correctly; the probability of a tear was estimated with 99.9% by the deep convolutional neural network
MRI of the right knee joint of a 45-year-old male patient. Sagittal fat-suppressed intermediate-weighted images at the junction of the body to the posterior horn of the medial meniscus (a) and the lateral meniscus (b). c A coronal short-tau inversion recovery image of the posterior horns of both menisci. d The output of the deep convolutional neural network, which calculates a probability of a tear of the medial and lateral meniscus as well as provides a heatmap depicting the location of the suspected tear. A horizontal meniscus tear is present at the junction of the posterior horn to the body of the medial meniscus (arrows), while the lateral meniscus shows a small tear of the central body (arrowhead). Knee arthroscopy confirmed the tear of both menisci, which was correctly diagnosed by both readers. The deep convolutional neural network correctly classified the medial meniscus tear with a probability of 93.5% but missed the small tear of the lateral meniscus
Figure 1 presents a flowchart of the study design. Knee MRI exams of clinical patients were retrospectively evaluated by two radiologists and by a deep convolutional neural network (DCNN)-based software for detection of medial and lateral meniscus tears (Fig. 2). All included patients had undergone arthroscopic knee surgery with meniscus evaluation after the MRI. The report of the knee surgery served as the standard of reference of this study. Radiological assessments and results of the DCNN were compared, and differences of diagnostic performances were calculated.
MRI of the left knee joint of a 30-year-old male patient. a A coronal short-tau inversion recovery image of the body of both menisci. b A sagittal fat-suppressed intermediate-weighted image at the junction of the posterior horn to the body of the medial meniscus. c The output of the deep convolutional neural network, which calculates a probability of a tear of the medial and lateral meniscus as well as provides a heatmap depicting the location of the suspected tear. A horizontal meniscus tear is present at the body with extension to the posterior horn to the medial meniscus (arrows). Knee arthroscopy confirmed the tear of the medial meniscus. Both readers and the deep convolutional neural network diagnosed the medial meniscus tear correctly; the probability of a tear was estimated with 99.9% by the deep convolutional neural network
MRI of the right knee joint of a 45-year-old male patient. Sagittal fat-suppressed intermediate-weighted images at the junction of the body to the posterior horn of the medial meniscus (a) and the lateral meniscus (b). c A coronal short-tau inversion recovery image of the posterior horns of both menisci. d The output of the deep convolutional neural network, which calculates a probability of a tear of the medial and lateral meniscus as well as provides a heatmap depicting the location of the suspected tear. A horizontal meniscus tear is present at the junction of the posterior horn to the body of the medial meniscus (arrows), while the lateral meniscus shows a small tear of the central body (arrowhead). Knee arthroscopy confirmed the tear of both menisci, which was correctly diagnosed by both readers. The deep convolutional neural network correctly classified the medial meniscus tear with a probability of 93.5% but missed the small tear of the lateral meniscus
Our study has limitations. First, the deep learning-based DCNN was only tested on knee MRI performed at our institution. Therefore, the results of this study apply to knee examinations using our standard knee protocol and MR scanner. However, the DCNN was not fitted to our knee MR examinations but was trained on more than 18,500 knee MRI from a variety of institutions and therefore including various MR protocols and MR scanners from all major vendors and different field strengths. Therefore, we assume that the performance of the DCNN will be similar for evaluation of knee MRI of other institutions. Second, the indication for arthroscopic knee surgery was influenced by the MRI and the visibility of a meniscus tear to some degree. Still, knee arthroscopy was also performed for several other intraarticular reasons, like ligament, cartilage, or synovial abnormalities. Therefore, the study population has a relevant number of intact medial and lateral menisci; however, verification bias may be present [41].
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