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Last Updated: 02 July 2021

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General | Latest Info

Artificial intelligence has recently made substantial strides in perception, allowing machines to better represent and interpret complex data. This has lead to major advances in applications ranging from web search and self-driving vehicles to natural language processing and computer vision tasks that until a few years ago could be done only by humans 1. Deep learning is a subset of machine learning that is based on neural network structure loosely inspired by the human brain. Such structures learn discriminative features from data automatically, giving them the ability to approximate very complex nonlinear relationships. While most earlier AI methods have led to applications with subhuman performance, recent deep learning algorithms are able to match and even surpass humans in task-specific applications 2-5. This is owing to recent advances in AI research, massive amounts of digital data now available to train algorithms and modern, powerful computational hardware. Deep learning methods have been able to defeat humans in the strategy board game of Go, achievement that was previously thought to be decades away, given highly complex game space and a massive number of potential moves 6. Following the trend towards human-level general AI, researchers predict that AI will automate many tasks, including translating languages, writing best-selling books and performing surgery all within coming decades 7. Within health care, AI is becoming a major constituent of many applications, including drug discovery, remote patient monitoring, Medical diagnostics and imaging, risk management, wearables, virtual assistants and Hospital management. Many domains with big data components such as analysis of DNA and RNA sequencing data 8 are also expected to benefit from use of AI. Medical fields that rely on imaging data, including radiology, pathology, dermatology 9 and ophthalmology 10, have already begun to benefit from implementation of AI methods. Within Radiology, trained physicians visually assess medical images and report findings to detect, characterize and monitor diseases. Such assessment is often based on education and experience and can be, at times, subjective. In contrast to such qualitative reasoning, AI excels at recognizing complex patterns in imaging data and can provide quantitative assessment in automated fashion. More accurate and reproducible Radiology assessments can then be made when AI is integrated into clinical workflow as a tool to assist physicians. As imaging data are collected during routine clinical practice, large data sets are in principle readily available, thus offering incredibly rich resource for scientific and medical discovery. Radiographic images, coupled with data on clinical outcomes, have led to the emergence and rapid expansion of radiomics as a field of Medical research 11-13. Early radiomic studies largely focused on mining images for large set of predefined engineered features that describe radiographic aspects of shape, intensity and texture. More recently, radiomics studies have incorporated deep learning techniques to learn feature representations automatically from example images 14, hinting at substantial clinical relevance of many of these radiographic features.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Flat-Panel Systems

We compare soft-copy images produced by digital Chest Radiography system that uses flat-panel X-ray detector based on amorphous selenium with images produced by the storage phosphor Radiography system for visualization of the regions of the chest. MATERIALS and METHODS. Two chest radiologists and two residents analyzed 46 pairs of posteroanterior chest radiographs on high-resolution video monitors. In each pair, one radiograph was obtained with storage phosphor Radiography system, and the other radiograph was obtained with selenium-base flat-panel detector Radiography system. Each pair of radiographs was obtained at the same exposure settings. Interpreter rat visibility and radiographic quality of 11 different anatomic regions. Each pair of images was ranked on a five-point scale for preference of technique. Statistical significance of preference was determined using Wilcoxon's sign rank test. RESULTS. Interpreters had a statistically significant preference for selenium-base Radiography system in six of 11 anatomic regions and for storage phosphor system in two regions. Chest radiologists strongly prefer selenium-base images in eight regions, and they do not prefer storage phosphor images in any region. CONCLUSION. Soft-copy images produced by the selenium-base Radiography system were perceived as equal or superior to those produced by the storage phosphor system in most but not all anatomic regions. Many studies have addrest feasibility of using picture archiving and communication systems as an alternative to film-base radiology. There are many advantages to using PACS, including reduced film or processing costs and easier archiving and networking of images. Digital Radiography is a suitable and important digital technique for implementation of PACS because conventional radiographs and other projection radiographs are the most frequently obtained images in the diagnostic Radiology department. The most important clinical criterion for use of PACS is ability to achieve acceptable accuracy when interpreting radiologic images at soft-copy work-station. Most previous reports focused on comparing soft-copy images with hard-copy images or comparing hard-copy images with digital and film-screen images. During recent years, with rapid development of electronic and computer technology, digital radiologic detectors have undergone considerable investigation and development. A new technology, flat-panel digital detector that uses active matrix readout of amorphous selenium, has been propose. The Active matrix consists of a two-dimensional array of thin-film transistors. In comparison with film-screen and storage phosphor Radiography systems, selenium detector is characterized by higher detective quantum efficiency. Anticipating that image quality of selenium detector system exceeds that of film-screen and storage phosphor Radiography systems, new system could allow improved detection of small, low-contrast lesions. We compare soft-copy images of the new digital Chest Radiography system that uses flat-panel X-ray detector based on amorphous selenium with those of the storage phosphor Radiography system for visualization of the regions of the chest. Posteroanterior chest radiographs were obtained using two digital detector systems. Storage phosphor images were obtained with FCR-9000 unit.


Introduction

Over the last two decades, use of digital radiography in veterinary medicine has increase. It offers several advantages over screen-film systems, ranging from electronical storage and image distribution to increased dose efficiency and greater dynamic range of detector systems 1. In comparison to screen-film radiography, however, digital radiography is limited in its spatial resolution 2. Image quality in digital radiographs depends on the intrinsic sharpness and noise level of the detector system, with noise being the limiting factor in object detection, while screen-film systems are contrast limited 3. In digital radiography, two different approaches have been develop. On one hand, there are computed radiography systems with storage plate and a separate read out process. Then there are direct digital radiography systems, where x-ray photons are directly converted into electrical charges 4. One established type of CR detector is the conventional powder-base storage phosphor detector, which consists of small phosphor particles dispersed in binding agent 5. More recently developed CR detector is a needle-base storage phosphor detector. Here, phosphor particles form a crystalline needle-structure that is oriented perpendicular to detector surface 5. Comparing technical aspects, NIPs have higher conversion efficiency than PIPs, resulting in higher signal-noise-ratio while using identical exposure settings 6. Smans et al. 7, using computer model, showed that the threshold-contrast detectability of their simulated NIP system was superior to also simulated PIP system. In preclinical trials, NIPs and PIPs have been used on phantoms, where NIPs depict lower contrast levels better than PIPs 8. When tested on phantoms for chest radiology, NIPs were significantly superior to PIPs regarding image quality and potential for dose reduction 9 10. In one phantom study, dose reduction of up to 68% of initial dose was possible 11. In clinical trials, dose reduction of 50% on NIP systems produce images that show no significant differences in image quality compare to PIP images at 100% of dose 12. In neonatal chest radiology NIP system was prefer by reviewers in comparison to PIP system, here dose reduction of 20% was possible without detectable loss of image quality 13. In veterinary medicine, various digital detector systems have been test for dogs, cats and large animals such as horses 14 15 16. Data concerning use of digital detector systems for birds, snakes and lizards, with body masses ranging from 123 to 847 g has also been published in 17 18 19. In general practice, veterinarians are consistently confronted with even smaller patients. Animals like budgerigars and mice, with body masses ranging from 30 to 50 g, make high demands on x-ray technique due to their delicately structured anatomy and their high respiratory rate which demands shorter exposure time. To the author's knowledge, no studies have been conducted to evaluate use of computed radiography in patients with body masses lower than 100 g. We want to explore implementation of these methods, since radiography represents an affordable and reliable diagnostic means in standard veterinary practice.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Introduction

Because of the rapid development of machine learning, it is hard to introduce every aspect of machine learning in one article. So in this section we will give a concise introduction to the most important topics of machine learning. These topics include Linear models, learning with kernels, probabilistic models, clustering Analysis and dimensionality reduction. Through this introduction, we hope readers may have a general idea about the content of machine learning research, what it is capable of, and what are implications for other research areas and real applications. Topics that will be introduced and their inter-relationships are shown in Fig. 1. In these topics of machine learning research, kernel learning and probabilistic models play key roles in machine learning-base Radiology applications. Kernel learning usually provides the best classifier for computer-aided detection in Radiology; probabilistic models provide the theoretical framework for Medical Image Analysis, such as Image reconstruction, segmentation, and Registration. Linear models, artificial neural networks, and ensemble learning provide other options for handling classification and regression problems in Radiology besides kernel learning. Dimensionality reduction and feature selection are essential parts of computer-aided detection systems in Radiology. Multiple instance learning addresses a common scenario in Radiology CAD where a patient may have few positive instances of disease and many false positives. Reinforcement learning is dedicated to accumulating domain experience in sequential learning. Clustering Analysis could be applied to Medical images to identify similar lesions or meaningful findings. Graph matching is used to handle Medical Image Registration problems. Linear models assume that there is a Linear relationship between the input of the model and the output of the model. Perhaps it is the simplest method for classification and regression. It has been widely used in computer-aided classification. For example, Chan et al. Employ Linear discriminant Analysis in texture feature space for classification of mammographic masses and normal tissue. In work of Preul et al. On accurate, noninvasive diagnosis of human brain tumors by proton magnetic resonance spectroscopy, they use LDA for classification in leave-one-out test paradigm. Give input vector X R D which describes features of objects we want to classify, decision function in Linear models usually is defined as f = w T X + w 0 where w is weight vector and w 0 is constant and called threshold. Learning optimal weight vector w and threshold w 0 is a key problem in Linear models. Once w is learnt from training data, it can be applied to test cases and predict labels of them. For two-class classification problems, Fisher proposes the following criterion to locate optimal parameters: {matheq}{matheq}{endmatheq}{endmatheq} where S B = T is call between scatter matrix, and S w = S 1 + S 2 is call within scatter matrix. This method is called Linear discriminant Analysis.

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* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Related works

University of Washington is major metropolitan Medical System, with three major urban Medical Centers and many outpatient clinics and Imaging Centers spread across Western Washington, epicenter of the COVID-19 outbreak in the United States. There have been more than 267 cases of COVID-19 and 24 deaths in Washington State, including approximately 18 patients with confirmed COVID-19 hospitalized at our institutions as of this writing. There is a substantial Asian population in Seattle, including many professionals and students WHO frequently travel to China and other regions with high infection rates. The largest risk remains in older patient populations, as 71% of patients infected in Washington State are older than 50 years and 57% are older than 60 years. Radiology leadership has helped in the development of Policies and Guidelines relating to COVID-19 in areas of Patient screening, spread precautions, and Patient triage in coordination with Hospital leadership. Radiology leadership has worked with input from our department membership, especially Operations leaders and chest imagers, to develop screening-specific guidelines. 1. Early detection and limiting exposure of Health Care workers, employees, and patients, especially critically ill patients. Hospitals have implemented screeners at all hospital entrances to check those coming in for symptoms that could be related to SARS-CoV-2 infection or with risk factors related to travel or exposure. The Radiology front desk serves as a screening site, with similar screening to that performed at the hospital front door. Patients WHO present with respiratory symptoms WHO are undergoing outpatient Imaging or Procedures have their studies canceled and are asked to follow up with their primary care physician. For with suspected or confirmed COVID-19, all nonemergent Imaging and Procedures are delayed until diagnosis is confirmed and they recover from their illness and are considered noncontagious. 2. Use of Radiography and chest CT. Despite reports from China and initial concerns from the US Centers for Disease Control and Prevention regarding unreliable test performance, our current reverse-transcription polymerase chain reaction assay for SARS-CoV-2 viral nucleic acid is estimated to have sensitivity of 95%-97%. Our laboratory also has a turnaround time of less than 1 day, making RT-PCR easy, accurate, and less resource-intensive examination. Our laboratory has been performing more than 500 Tests per day, covering our System but also other regional Systems, with approximately 10% positive results. Inconclusive results are seen in small subset, which are then sent for confirmation to Washington State laboratories. The Sensitivity and specificity of chest CT for COVID-19 are Report to be 80%-90% and 60%-70%, respectively. Thus, Imaging is reserved for those cases where it will impact patient management and is clinically indicated or to evaluate for unrelated urgent and / or emergent indications. This typically occurs in cases where alternative diagnosis is being ruled out or being considered for acute symptoms worsening. In our current workflow and with the accuracy and rapidity of RT-PCR testing, there is no need for immediate CT Imaging.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Experiments and results

The dataset consisted of 786 X-Ray images and 459 Radiology reports and was split into training, validation, and test sets, and the ratio of fracture to non-fracture cases was similar among datasets. Results were compared using three architectures for test dataset. The first was GoogLeNet-Inception V3 1 used in encoder of proposed architectures, and the others were two proposed architectures. For the overall classification experiment that was comparable to prior study 14, three levels of discrimination were evaluate: fracture versus normal, classification among three groups, and classification among seven subgroups. Figure 4 shows confusion matrix that represent actual and predicted classes, where columns are predicted classes and rows are actual classes. The true-positive measure for each class is the number of positive examples correctly classified using the model, which is each diagonal element of the matrix. The false-positive measure for each class is the number of classes that are incorrectly classified as positive. False-negative for each class is the number of positive classes incorrectly classified as negative, while true-negative is the number of negative classes correctly classified using the classification model. For Evaluation, classification performance was calculated using the following quantitative metrics: performance of conventional and proposed methods for classification are summarized in Table 2. The base network achieved an overall accuracy of 79. 30% on 2-class discrimination task and overall accuracy of 66. 08% on the 7-class discrimination task. Propose methods show favorable performance for all performance metrics. Specifically, M2 showed the highest accuracy for 7-class task and improved performance of conventional method by more than 8. 8%. M1 was similar to the conventional method for classification performance in 7-class discrimination task but showed greater performance for simple tasks. Receiver operating characteristic curves for performance of three models for the 7-class discrimination task are shown in Supplementary Fig. 2. Area under ROC curve values obtained for base model, M1, and M2 were 0. 73-0. 86 0. 81-0. 88, and 0. 72-0. 90, respectively, after excluding very rare B3. We also perform five-fold cross validation with paired dataset, not Image only. Results are shown in Supplementary Fig. 3 and Supplementary Table 1. Overall performance decrease occur because the number of training Data smaller but M1 and M2 still show better performance than the base model. Supplementary Fig. 4 is visualization of latent representation vector embed in 2D space by t-SNE for three models. There was a total of 227 vectors from test sets, and each class is represented by a different color in figure. Latent representation vectors from the base network are disperse, while those from M2 are relatively separate with respect to class label. This indicates that features with greater discrimination are learnt using meta-Learning methods with Radiology reports. While performing inference on test Image, Grad-CAM 23 was used to generate a heatmap of hip fracture to provide evidence of fracture site recognition.


Introduction

At dawn of 2019, the World Health Organization was notified by Chinese authorities of novel coronavirus causing severe respiratory illness emerging from Hubei Providence of China and particularly linked to the seafood market of Wuhan city. Clinical characteristics of the disease are non-specific and comprise fever, cough, fatigue and shortness of breath in the majority of cases. Other factors that contribute to lethality and severity of cases include obesity, chronic cardiovascular diseases and smoking habits. Many attempts for effective vaccines are currently under development and traditional antiviral antibacterial and anti-inflammatory agents such as zinc have been used to reduce the risk of co-infections. Imaging investigation, in the context of chest X-rays or computed tomography, has a vital role in disease management. Bilateral airspace opacities showing peripheral and lower-zone predominance represent the most frequent findings on both modalities. Additionally, it has been reported that chest X-ray screening for asymptomatic carriers of COVID-19 may serve as a viable substitute for available reverse transcription-quantitative polymerase chain reaction tests. The high infection rate of COVID-19 cause, in a short period of time, unprecedented burden on healthcare systems, pushing intensive care units treating multimorbid or other high-risk patients to limits. Therefore, as recently report, given shortages and delays in PCR tests, chest X-rays have become one of the fastest and most affordable ways for doctors to triage patients. As a result, faced with staff shortages and overwhelming patient loads, growing number of hospitals are turning to automated tools to help them manage pandemic. In such context, artificial intelligence COVID-19 classification systems based on chest X-rays represent cost-beneficial solution for early detection / diagnosis of infection and timely risk stratifications of patients. The recent COVID-19 pandemic initiate abundance of unpublished preprints available on open databases claiming an accuracy score of up to 99% for COVID-19 screening on chest X-rays. These deep learning models incorporate a variety of architectures such as Generative Adversarial Networks for data augmentation, capsule Networks and transfer learning techniques. Most notably, SqueezeNet was used with Bayesian hyperparameter optimization, achieving an ACC of 98. 3%. Transfer learning techniques are crucial for deep learning model convergence on limited data, since there is a scarcity of large and widely available COVID-19 imaging repository. Many transfer learning models have been tested on small X-ray datasets with ACC up to 98. 75% for binary and among three classes, with Pneumonia being third, up to 93. 48%. Moreover, GANs have been used jointly with transfer learning to further augment limited COVID X-ray pool, improving prediction performance with ACC of 99. 9%. Additionally, CT semantic features related to COVID-19 were similarly observed and significant detection sensitivity of disease of 88% was report. Self-supervise encoder deep learning architecture was deployed on raw CT slices achieving area under curve of 94%.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Discussion

Coronavirus Disease 2019 pandemic begin in December 2019 in Wuhan, China. The outbreak is due to severe Acute respiratory syndrome coronavirus 2 infection. Approximately 81 000 patients have been infected in China. Although infection rates are said to be controlled in China through severe public health measures, Italy and Iran have seen exponential increases in the number of infected individuals. Other than China, Italy, and Iran, most countries have had approximately 2 months to prepare their responses to the COVID-19 pandemic. These responses are lead by public health authorities of national governments in coordination with local governments and hospitals. Because of the nature of the emergency in China, Chest CT findings have temporarily become part of the official Diagnostic Criteria of COVID-19 As surrogate for viral nucleic acid testing. With improved disease understanding, Chest CT findings are no longer part of Diagnostic Criteria for COVID-19. Instead, at present, focus of most Radiology departments outside of China has shifted from Diagnostic capability to preparedness. Radiology preparedness is a set of policies and procedures directly applicable to Imaging departments designed to achieve sufficient capacity for continued operation during Health Care emergency of unprecedented proportions, support Care of Patients With COVID-19, and maintain radiologic Diagnostic and Interventional support for the entirety of the hospital and health system. Because of varying infection control policies, steps for Radiology preparedness for COVID-19 will vary between institutions and clinics. The Radiology editorial Board has assembled a team of radiologists WHO are active in coordination, development, and implementation of Radiology preparedness policies for COVID-19. Their policies have been developed in conjunction with top infection Control experts at their respective World-class Health Care Systems. In the sections below, each panel member describes their departments ' top priorities for COVID-19 preparedness in their environment. The Editorial Board hops that readers may find one or more of highlight Health Care Systems to be similar to their own, providing impetus for action or confirmation of your current preparedness activities.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

NEGLIGENCE

Dr. Berlin is Vice Chairman, Department of Radiology, NorthShore University Health System, Skokie Hospital, Skokie, IL, and Professor of Radiology, Rush Medical College, Chicago, IL. Communication of diagnosis so that it may be beneficially utilized may be all together as important as diagnosis itself. 1 women are entitled to know the results of their examinations. Women simply cannot rely on referring physicians to notify them. 2 in California, 58-year-old woman admitted one evening to the Hospital emergency Department underwent chest radiography, and it was interpreted by the ED Physician as normal. The following morning, radiologist interpreted radiographs as follow: Probably normal chest, but suggested a CT scan to evaluate small ill-define density in the right upper lobe. Radiologists make no effort to directly communicate results to personnel or physicians in ED. Eighteen months later, patient was diagnosed as having carcinoma in the right upper lung. Comparison with previous radiographs indicate that the density note in initial study represents early carcinoma. A malpractice lawsuit was filed against a radiologist alleging negligence for failing to communicate abnormality to the ED Physician. The lawsuit was settled with payment of $1. 5 million. In Illinois, 48-year-old man with complaints of renal colic underwent abdominal and pelvic compute tomography. The radiologist asked the secretary to call the referring physician and give him a preliminary report of no stones; essentially normal CT. The following day, radiologist issued a written report concluding,. No calculi are see, but a 5 cm abdominal aortic aneurysm isnoted, but he did not directly communicate this finding to the referring physician. Two years later, patient suddenly died of ruptured abdominal aortic aneurysm. In subsequent malpractice lawsuit, referring doctor testified that although he had received an initial verbal report from the Radiologists secretary, he had never received a final written report that indicated that the patient had aneurysm. The lawsuit was settled for $2. 5 million, with liability share equally between referring physician and radiologist. In Florida, patient underwent lumbar magnetic resonance Imaging Study because of symptoms and clinical findings consistent with herniated disk. The Radiologist interpreted the study as showing L4 to L5 herniated disk, but in his report also mentioned that there was small space-occupying lesion in patients leave kidney most likely has appearance of cyst, but ultrasound is suggested for further evaluation. No direct communication was made with referring physician. Eighteen months later, patient underwent CT that disclosed renal carcinoma; additional studies show numerous metastases. In the malpractice lawsuit that ensue, referring physician claim never to have received a written report from a radiologist. The case was settled against both the referring physician and the radiologist, terms of which were kept confidential. In Ohio, 70-year-old woman was admitted one night to Hospital ED with complaints of a severe headache. Head CT without contrast was interpreted by an off-site radiologist, employed by a teleradiology company, as normal.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

GENERIC FACTORS CONTRIBUTING TO UNDERPERFORMANCE/DISCREPANCIES/ERRORS

Collection of High-quality ground truth Data, development of generalizable and diagnostically accurate techniques, and workflow integration are key challenges facing adoption of machine learning in Radiology practice. Peter Norvig of Google demonstrated that large volumes of data may overcome deficiencies in machine learning algorithms. Narrow-scope machine learning algorithms may not require large amounts of training Data, but instead may require high-quality ground truth training Data. In medical imaging analysis, as with other kinds of machine learning, amount of data that is required varies largely on task to perform. For example, segmentation tasks may only require small set of data. On the other hand, performance classification tasks may require many more label examples, which may also be largely dependent on the number of classifiers to distinguish between. Confounders in source data may result in possible failures of machine learning algorithms. Rare findings or features may also be possible weaknesses due to lack of large volume of particular features for neural networks, which are therefore vulnerable to inaccuracies. Variance and bias are issues that may result in poor performance of machine learning algorithm. Bias is erroneous assumptions in algorithms that can result in missing associations. High variance can cause algorithms to learn data too well and start fitting random noise. The optimal model is not only accurate in representation of training data, but also generalizable to unseen data. Overfitted algorithm overreacts to minor variations from training data. Therefore, algorithm performs well on training data and poorly with new data. Overfitting is a major challenge in machine learning, particularly when a model is excessively complex. Extensive ground truth Annotation is often required for proper training of machine learning algorithms. Multiple technology companies and academic Research Projects rely on trained radiologists to annotate what is considered ground truth on Radiology reports and images. Extensive labor costs, time, and resources are required for endeavors to be properly implement. Also, validation process must be highly robust, otherwise algorithm could be subject to overfitting To particular subclassification of data. Appropriate development of Artificial Intelligence tools necessitate defining standardized Use cases and Annotation tools. These Use cases will need to be consistent with clinical practice, as well as regulatory, legal, and ethical issues that accompany Artificial Intelligence in medical imaging. Clinical panels of American College of Radiology Data Science Institute, in conjunction with other medical specialty societies, are defining these standardized use cases. In addition, standard approach could make Image annotations interoperable between different information technology systems and software applications that communicate and exchange data. The National Cancer Institute's Annotation and Image Markup model offers possible standard approach to annotation of images and image features. Machine learning-base algorithms are not currently well integrated into picture archiving and communication system workstations. Many systems incorporate and require separate workstation or Network node for sending images for analysis.


Applications of Machine Learning in Diagnostic Imaging

Automate detection of findings within medical images in radiology is an area where machine learning can make an impact immediately. For instance, extraction of incidental findings such as pulmonary and thyroid nodules has been demonstrated to be possible with machine learning techniques. Further machine learning research has also been performed for detection of critical findings such as pneumothorax, fractures, organ laceration, and stroke. Algorithms that fall within the categories of computer-aided detection and computer-aided diagnosis have been used for decades. In mammography, computer-aided diagnosis has shown effectiveness. However, there is controversy that computer-aided diagnosis is to some extent ignored by some mammographers and may have limited benefit clinically. Breast cancer screening is one of the first areas where machine learning is expected to be incorporated into radiology practice. Several studies have show diagnostic value of machine learning techniques in different breast imaging modalities, including mammography, US, MRI, and tomosynthesis. Interest has grown in the role of machine learning in detection, classification, and management of pulmonary nodules. For example, deep learning system to classify pulmonary nodules performs within interobserver variability of experienced human observers. Machine learning algorithms have also aided in the reduction of false-positive results in detection of pulmonary nodules. The recent Kaggle Data Science Bowl saw nearly 10 000 participants compete for $1 million in prize money; competitors achieve high levels of performance in identifying candidates likely to be diagnosed with lung cancer within 1 year. A Follow-up challenge has been proposed to bring these models to clinic. Bone age analysis and automated determination of anatomic age based on medical imaging hold considerable utility for pediatric radiology and endocrinology. Dr. Synho Do and colleagues created an algorithm that accurately characterizes bone age based on inputs of hand radiographs of pediatric patients. Other potential use cases of machine learning include line detection, prostate cancer detection at MRI, determination of coronary artery calcium score, or detection and segmentation of brain lesions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

APPLICATION OF RISK MATRIX OUTCOME

Table 2. Interpretation of the risk ratings

Rating CategoryDescription
Very HighThe risk is totally unacceptable. Immediate measures must be taken to reduce these risks and mitigate hazards.
HighThe risk is unacceptable. Measures to reduce risk and mitigation hazards should be implemented as soon as possible.
MediumThe risk may be acceptable over the short term. Plans to reduce risk and mitigate hazards should be included in future plans and budgets.
LowThe risks are acceptable. Measures to further reduce risk or mitigate hazards should be implemented in conjunction with other security and mitigation upgrades.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Deep learning algorithms

The following terms from computer Science are helpful for defining the context of deep learning. Artificial intelligence is a branch of computer Science devoted to creating systems to perform tasks that ordinarily require human intelligence. This is a broad umbrella term encompassing a wide variety of subfields and techniques; In this article, we focus on deep learning as a type of machine learning. Machine learning is a subfield of artificial intelligence in which algorithms are trained to perform tasks by learning patterns from data rather than by explicit programming. In classic machine learning, expert humans discern and encode features that appear distinctive in data, and statistical techniques are used to organize or segregate data on the basis of these features. For instance, for the purpose of analyzing image, experts in Image processing might program algorithm to decompose input images into basic elements of edges, gradients, and textures. Statistical Analysis of the presence of these features in give Image can then be used to classify or interpret the Image. However, for many complex computer vision tasks, it is typically not clear even to experts how to define optimal image features for machine learning algorithms to use. For example, it may not be obvious how to teach computer to recognize organs on the basis of pixel brightness. Therefore, it may be desirable for computer systems to not only learn mappings of features to desired outputs, but to learn and optimize features themselves. Representation learning is a type of machine learning in which no feature engineering is used. Instead, algorithm learns on its own best features to classify and provide data. With enough training examples, system based on representation learning could potentially classify data better than with hand-engineer features. The challenge is in how machine learning systems can learn potentially complex features directly from raw data.


Introduction

Radiologic imaging is of increasing importance in patient care. Both diagnostic and therapeutic indications for radiologic imaging are expanding rapidly. Rapid expansion is a consequence of the need for more rapid, accurate, cost-effective, and less invasive treatment. Technologic advancements in radiologic imaging equipment have also fuelled utilization of imaging. Such technologic advancements include the capability to acquire higher and higher resolution images, enabling visualization of smaller anatomic structures and abnormalities. Higher resolution comes at the cost of ever increasing average number of images per patient. Radiologists need to interpret these images and as the number of images increases, radiologists' workload increases as well. The increasing number and complexity of images threatens to overwhelm radiologists' capacities for interpreting them. In many real radiologic practices, automated and intelligent image analysis and understanding are becoming an essential part of procedure, such as image segmentation, registration, and computer-aided diagnosis and detection. In addition, in the area of cancer prognosis and treatment, automated and intelligent algorithms have a large market and are welcome broadly, in areas such as radiation therapy planning or automatic identification of imaging biomarkers from radiological images of certain diseases, etc. Machine learning algorithms underpin algorithms and software that make computer-aided diagnosis / prognosis / treatment possible. Radiology is a branch of medical science which uses imaging technology and radiation to make diagnoses and treat disease. It has benefited greatly from advances in physics, electronic engineering, and computer science. Base on different detection and imaging rationale, various modalities have developed in past decades in the field of diagnostic radiology. Today, mainstream modalities which are widely used in hospitals and medical centers include radiography, fluoroscopy, compute tomography, ultrasound, magnetic resonance imaging, and positron emission tomography. In the daily practice of radiology, medical images from different modalities are read and interpreted by radiologists. Usually, radiologists must analyze and evaluate these images comprehensively in a short time. But with advances in modern medical technologies, amount of imaging data is rapidly increasing. For example, CT examinations are being performed with thinner slices than in the past. Reading and interpretation time of radiologists will mount as the number of CT slices grows. Machine learning provides an effective way to automate analysis and diagnosis for medical images. It can potentially reduce the burden on radiologists in the practice of radiology. Applications of machine learning in radiology include medical image segmentation; medical image registration; computer-aided detection and diagnosis systems for CT or MRI images; brain function or activity analysis and neurological disease diagnosis from fMR images; content base image retrieval systems for CT or MRI images; and text analysis of radiology reports using natural language processing and natural language understanding. Machine learning is the study of computer algorithms which can learn complex relationships or patterns from empirical data and make accurate decisions. It is an interdisciplinary field that has close relationships with artificial intelligence, pattern recognition, data mining, statistics, probability theory, optimization, statistical physics, and theoretical computer science.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

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