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@Radiology_AI

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To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets. This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip (n 22; mean age, 44 years 13 [standard deviation]; nine men) or shoulder (n 32; mean age, 56 years 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence.


A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging - Docwire News

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RATIONALE AND OBJECTIVES: Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out. MATERIALS AND METHODS: T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n 1043 in-house and n 347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist.


World First for Artificial Intelligence To Treat COVID-19 Patients Worldwide

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Addenbrooke's Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyze chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymized and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world.


Using AI and old reports to understand new medical images

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Getting a quick and accurate reading of an X-ray or some other medical images can be vital to a patient's health and might even save a life. Obtaining such an assessment depends on the availability of a skilled radiologist and, consequently, a rapid response is not always possible. For that reason, says Ruizhi "Ray" Liao, a postdoc and a recent PhD graduate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), "we want to train machines that are capable of reproducing what radiologists do every day." Liao is first author of a new paper, written with other researchers at MIT and Boston-area hospitals, that is being presented this fall at MICCAI 2021, an international conference on medical image computing. Although the idea of utilizing computers to interpret images is not new, the MIT-led group is drawing on an underused resource--the vast body of radiology reports that accompany medical images, written by radiologists in routine clinical practice--to improve the interpretive abilities of machine learning algorithms.


Artificial Intelligence System Improves Breast Cancer Detection

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Breast cancer is the second most common cancer among women in the United States; as of January 2021, there are more than 3.8 million women with a history of breast cancer in the United States. Doctors often use ultrasound, mammograms, MRI, or biopsy to find or diagnose breast cancer. In a new study, researchers from NYU and NYU Abu Dhabi (NYUAD) report that they have developed a novel artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Their findings are published in the journal Nature Communications, in a paper titled, "Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams," and was led by Farah Shamout, PhD, NYUAD assistant professor emerging scholar of computer engineering and colleagues. "Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates, the researchers wrote. "In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images." "The AI system was developed and evaluated using the NYU Breast Ultrasound Dataset41 consisting of 5,442,907 images within 288,767 breast exams (including both screening and diagnostic exams) collected from 143,203 patients examined between 2012 and 2019 at NYU Langone Health in New York," noted the researchers. The primary goal of the AI system is to reduce the frequency of false-positive findings. It can detect cancer by assigning a probability for malignancy and highlight parts of ultrasound images that are associated with its predictions. When the researchers conducted a reader study to compare its diagnostic accuracy with board-certified breast radiologists, the system achieved higher accuracy than the ten radiologists on average. However, a hybrid model that aggregated the predictions of the AI system and radiologists achieved the best results in accurately detecting cancer in patients. "Our findings highlight the potential of AI to improve the accuracy, consistency, and efficiency of breast ultrasound diagnosis," explained Shamout. "Importantly, AI is not a replacement for the expertise of clinicians.


Using AI and old reports to understand new medical images

#artificialintelligence

Getting a quick and accurate reading of an X-ray or some other medical images can be vital to a patient's health and might even save a life. Obtaining such an assessment depends on the availability of a skilled radiologist and, consequently, a rapid response is not always possible. For that reason, says Ruizhi "Ray" Liao, a postdoc and a recent PhD graduate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), "we want to train machines that are capable of reproducing what radiologists do every day." Liao is first author of a new paper, written with other researchers at MIT and Boston-area hospitals, that is being presented this fall at MICCAI 2021, an international conference on medical image computing. Although the idea of utilizing computers to interpret images is not new, the MIT-led group is drawing on an underused resource -- the vast body of radiology reports that accompany medical images, written by radiologists in routine clinical practice -- to improve the interpretive abilities of machine learning algorithms.


AI For Cancer Detection: Ready for Prime Time or Caution Advised?

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Over the last couple of years, there has been much discussion about the benefits of artificial intelligence (AI) for improving healthcare. But how much of this is true and how much simply hype? Is the technology really a godsend to radiologists and other healthcare professionals, or is it making their lives more difficult? There is no doubt that AI-based image recognition technology has improved enormously in recent years. Many researchers and companies are now working on different types of programs with a view to improving speed, accuracy and costs of cancer screening.


AI could help to diagnose lung cancer earlier

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Lung cancer is the most common cause of cancer death, with around 1.8 million lives lost around the world each year. It is often diagnosed at a later stage when treatment is less likely to succeed. But researchers worldwide hope that using AI to support lung cancer screening could make the process quicker and more efficient, and ultimately help diagnose more patients at an early stage. Computerised tomography, or CT scans, are already used to spot signs of lung tumours, followed by a biopsy or surgery to confirm whether the tumour is malignant. However, each scan involves an expert radiologist examining around 300 images and looking for signs of cancer that can be small.


Council Post: Responsible AI Is Every Business's Responsibility

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Johan den Haan is CTO of Mendix, a Siemens business and leader in enterprise low-code, a model-driven approach for building apps 10x faster. Is AI the transformative technology destined to work wonders for humanity, from driverless cars to a cure for cancer? Or is it a genie in a bottle that, once released, could be used to manipulate or even rule humankind? With the tremendous advances in computing power, software capabilities and the cloud over the last decade, progress on AI is no longer linear -- it's exponential. That means it's time to pay attention and make some fundamental decisions.


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An artificial intelligence (AI) program can spot signs of lung cancer on CT scans a year before they can be diagnosed with existing methods, according to research presented at the European Respiratory Society International Congress. Lung cancer is often diagnosed at a late stage when treatment is less likely to succeed. Researchers hope that using AI to support lung cancer screening could make the process quicker and more efficient, and ultimately help diagnose more patients at an early stage. Computerised tomography or CT scans are already used to spot signs of lung tumours, followed by a biopsy or surgery to confirm whether the tumour is malignant. But each scan involves an expert radiologist examining around 300 images and looking for signs of cancer that can be very small.