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Perimeter Medical Imaging Announces Expansion of ATLAS AI Project with Installation of OTISTM for AI development at Leading Cancer Care Center, MD Anderson

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DALLAS, TX / ACCESSWIRE / July 27, 2020 / Perimeter Medical Imaging, AI Inc. (TSXV:PINK) today announced the installation of their OTISTM device at the University of Texas MD Anderson Cancer Center (MD Anderson), to further develop ImgAssist AI technology marking an important milestone in this collaboration and Perimeter's ATLAS AI Project. Initiated in mid-July, the ATLAS AI Project allows Perimeter to collaborate with industry-leading cancer care centers that will use OTIS - its proprietary ultra-high resolution imaging platform - to collect images of breast tumors from approximately 400 patients for the purpose of training and testing Perimeter's ImgAssist AI technology. This technology, which is currently under development, is designed to utilize a machine learning model to help surgeons identify, in real-time, if cancer is still present when performing breast-conserving surgery (lumpectomy). This study was made possible, in part, by a $7.4 million grant awarded by the Cancer Prevention and Research Institute of Texas (CPRIT), a leading state body funding cancer research. Jeremy Sobotta, President and CFO stated, "Initiation at MD Anderson is an important milestone in part one of our ATLAS AI Project and marks the next step in our development and clinical validation efforts for our ImgAssist AI software. MD Anderson is one of the largest breast cancer centers in the United States, treating approximately 40,000 patients a year, and is a valued collaborator as we strive to help physicians improve surgical outcomes for breast cancer patients by providing an additional tool for real-time margin visualization and assessment."


Health Checks for Machine Learning - A Guide to Model Retraining and Evaluation

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In 2013, IBM and University of Texas Anderson Cancer Center developed an AI based Oncology Expert Advisor. According to IBM Watson, it analyzes patients medical records, summarizes and extracts information from vast medical literature, research to provide an assistive solution to Oncologists, thereby helping them make better decisions. According to an article on The Verge, the product demonstrated a series of poor recommendations. Like recommending a drug to a lady suffering from bleeding that would increase the bleeding. "A parrot with an internet connection" - were the words used to describe a modern AI based chat bot built by engineers at Microsoft in March 2016. 'Tay', a conversational twitter bot was designed to have'playful' conversations with users. It was supposed to learn from the conversations. It took literally 24 hours for twitter users to corrupt it.



Can we trust AI not to further embed racial bias and prejudice?

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Heralded as an easy fix for health services under pressure, data technology is marching ahead unchecked. But is there a risk it could compound inequalities? When Adewole Adamson received a desperate call at his Texas surgery one afternoon in January 2018, he knew something was up. The call was not from a patient, but from someone in Maryland who wanted to speak to the dermatologist and assistant professor in internal medicine at Dell Medical School in the University of Texas about black people and skin cancer. Over the next few weeks, over a series of phone calls, Adamson would learn a lot about the caller.


Elsevier Launches 'AI and Big Data in Cancer,' a New Conference on the Translation of Technology, Data and Analytic Innovations into Clinical Practices and Patient Benefits

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AI and Big Data in Cancer: From Innovation to Impact, a new conference from Elsevier, a global information and analytics business specializing in science and health, will bring together experts from all aspects of cancer research and the digital medicine value chain to understand how to translate artificial intelligence and data-driven innovations into new clinical care practices for patients. These leaders, including 2018 Nobel laureate for Medicine, Dr. James Allison, will share pragmatic insights on finding the right partners to move innovations successfully forward. "It is time to shift our conversation from'what-technology-can-do' to'what-medicine-needs' and to raise awareness of what else is necessary to translate an AI-enabled and data-driven innovation into a marketed product," said Dr. Lynda Chin, Conference Chair, Founder and CEO of Apricity Health and Professor at Dell Medical School at the University of Texas, USA. "Understanding what these hurdles are is the first step to overcoming them. "The aim of this conference is to bring innovators together with stakeholders, from patients, clinicians and developers to regulators, payers and investors, so they can network and identify collaborators who can help them accelerate the translation of their innovation into clinical practices," Dr. Chin said. "Insights from the program's 40 key opinion leaders will advance the emerging digital medicine industry, building bridges from computer to clinics," said Laura Colantoni, Vice President for Reference Content, Elsevier, and one of the main organizers for the conference. "We are particularly excited about establishing this conference as a venue for successful innovators, influential facilitators, regulators and payers, as well as investors to find, engage and collaborate with clinicians, researchers and patients to accelerate progress in this area.


Elsevier launches 'AI and Big Data in Cancer', a new conference on the translation of technology, data and analytic innovations into clinical practices and patient benefits

#artificialintelligence

AI and Big Data in Cancer: From Innovation to Impact, a new conference from Elsevier, a global information and analytics business specializing in science and health, will bring together experts from all aspects of cancer research and the digital medicine value chain to understand how to translate artificial intelligence and data-driven innovations into new clinical care practices for patients. These leaders, including 2018 Nobel Laureate for Medicine, Dr. James Allison, will share pragmatic insights on finding the right partners to move innovations successfully forward. "It is time to shift our conversation from'what-technology-can-do' to'what-medicine-needs' and to raise awareness of what else is necessary to translate an AI-enabled and data-driven innovation into a marketed product," said Dr. Lynda Chin, Conference Chair, Founder and CEO of Apricity Health and Professor at Dell Medical School at the University of Texas, USA. "Understanding what these hurdles are is the first step to overcoming them. "The aim of this conference is to bring innovators together with stakeholders, from patients, clinicians and developers to regulators, payers and investors, so they can network and identify collaborators who can help them accelerate the translation of their innovation into clinical practices," Dr. Chin said. "Insights from the program's 40 key opinion leaders will advance the emerging digital medicine industry, building bridges from computer to clinics," said Laura Colantoni, Vice President for Reference Content, Elsevier, and one of the main organizers for the conference. "We are particularly excited about establishing this conference as a venue for successful innovators, influential facilitators, regulators and payers, as well as investors to find, engage and collaborate with clinicians, researchers and patients to accelerate progress in this area.


Artificial intelligence is prone to overdiagnosis - Cancerworld

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The use of artificial intelligence might increase the speed and the consistency of cancer diagnosis, but could also exacerbate the problem of overdiagnosis, according to a perspective article recently published in the New England Journal of Medicine by Adewole Adamson and Gilbert Welch, who suggest that this risk may be mitigated by overcoming the dichotomous classification between "cancer" and "not cancer". Supervised machine learning consists in the generation of decision-making algorithms starting from sets of images that pathologists have categorized as either "cancer" or "not cancer." "The computer system learns by judging its diagnosis against the external standard of pathological interpretation" Adewole Adamson, assistant professor of Internal Medicine at Dell Medical School at the University of Texas, explains. "Reliance on this external standard is problematic, however, since machine learning doesn't solve the central problem associated with cancer diagnosis: the lack of a histopathological gold standard." There is no single right answer to the question: "What constitutes cancer?"


Artificial Intelligence Can Better Help Doctors To Recognize Cancer Cells NewsGram

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Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize cancer cells from digital pathology images โ€“ giving clinicians a powerful way of predicting patient outcomes. The spatial distribution of different types of cells can reveal a cancer's growth pattern, its relationship with the surrounding microenvironment, and the body's immune response. But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone. "To make a diagnosis, pathologists usually only examine several'representative' regions in detail, rather than the whole slide. However, some important details could be missed by this approach," said Dr. Guanghua "Andy" Xiao, corresponding author of a study published in EbioMedicine.


Postdoctoral Fellow in Bioinformatics, Deep Learning

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The successful candidate is expected to join an established bioinformatics team. The ongoing projects in BSML focus on precision medicine, functional roles of genetic variants in complex disease, next-generation sequencing and single cell RNA sequencing method development and data analyses, deep learning, and regulatory networks. Integrative genomics and deep learning approaches are often applied. Funding (NIH grants, CPRIT, and lab/center startup) is available to support this position for 3 years and promotion to faculty positions is possible. The candidate will have the opportunity to access many high throughput datasets and interact with investigators across UTHealth and Texas Medical Center.


AI can help doctors identify cancer cells - ET HealthWorld

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New York: Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize cancer cells from digital pathology images - giving clinicians a powerful way of predicting patient outcomes. The spatial distribution of different types of cells can reveal a cancer's growth pattern, its relationship with the surrounding micro-environment, and the body's immune response. But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone. "To make a diagnosis, pathologists usually only examine several'representative' regions in detail, rather than the whole slide. However, some important details could be missed by this approach," said Dr. Guanghua "Andy" Xiao, corresponding author of a study published in EbioMedicine.