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AI in cancer care: how COVID is speeding up adoption

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Scientists have warned there could be thousands of excess deaths in the UK in the coming years due to delays in cancer diagnosis and treatment during by the coronavirus crisis. The pandemic has meant routine screenings, and urgent referrals and treatments, have been delayed or cancelled, leading to a backlog of patients. Researchers at the Health Data Research Hub for Cancer examined data from eight hospital trusts and found that, in a worst-case scenario, if delays continue, there could be up to 35,000 additional cancer deaths within a year. But artificial intelligence (AI) could be a solution. Over the past decade, AI has emerged as a leading technology with the potential to aid the medical community, from speeding up diagnostics and improving accuracy to improving patient outcomes and hospital efficiencies.


Chest CT Scan Machine Learning in 5 minutes

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This post provides an overview of chest CT scan machine learning organized by clinical goal, data representation, task, and model. A chest CT scan is a grayscale 3-dimensional medical image that depicts the chest, including the heart and lungs. CT scans are used for the diagnosis and monitoring of many different conditions including cancer, fractures, and infections. The clinical goal refers to the medical abnormality that is the focus of the study. Many CT machine learning papers focus on lung nodules.


Artificial Intelligence Identifies Prostate Cancer With Near-Perfect Accuracy

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Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.


Algorithm identifies risk-stratifying glioblastoma tumor cells

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"A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses," Rebecca Ihrie, PhD, and Jonathan Irish, PhD, associate professors in the department of cell and developmental biology at Vanderbilt University, and colleagues wrote. "We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival." Ihrie and Irish told Healio what prompted this research, implications of the findings and what future research should entail.


Can Artificial Intelligence detect the cause of diseases?

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The technological advancements in the global Healthcare industry are hurtling at light speed. As the medical industry is undergoing immense changes, Healthcare OEMs look forward to the growing technological trends to improve all aspects of patient care. Today, Artificial Intelligence (AI) play significant roles in the evolution of the healthcare industry, so much that algorithms can now predict and detect the root cause of a certain disease, making an accurate and timely diagnosis. For example, AI can detect the underlying cause of cancer, which can eventually help pharmaceutical scientists develop new drugs accordingly. In one recent study, published by Healthcare IT News, "Google and medical partners including Northwestern University have unveiled a new AI-based tool that can create a better model of a patient's lung from the CT scan images. This 3-D image gives better predictions about the malignancy of tumors and incorporates learning from previous scans, enabling the AI to help clinicians in spotting lung cancer in earlier stages when it is vastly more treatable".


How AI is revolutionizing healthcare

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AI applications in healthcare can literally change patients' lives, improving diagnostics and treatment and helping patients and healthcare providers make informed decisions quickly. AI in the global healthcare market (the total value of products and services sold) was valued at $2.4 billion in 2019 and is projected to reach $31.02 billion in 2025. Now in the COVID-19 pandemic, AI is being leveraged to identify virus-related misinformation on social media and remove it. AI is also helping scientists expedite vaccine development, track the virus and understand individual and population risk, among other applications. Companies such as Microsoft, which recently stated it will dedicate $20 million to advance the use of artificial intelligence in COVID-19 research, recognize the need for and extraordinary potential of AI in healthcare.


Machine Learning Projects for Beginners

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In this article, I will show you 5 beginner level Machine Learning Projects for Beginners. All the projects are solved and explained. If you are new to Machine Learning and want to improve yourself more before getting into projects you can go through my free course here. If you have already gone through some valuable topics of machine learning and want to get started with some projects, then here are some projects for you to get started with Machine Learning Projects. If you are using Scikit-Learn, you can easily use a lot of algorithms that are already made by some famous Researchers, Data Scientists, and other Machine Learning experts.


AI can spot prostate cancer with almost 100% accuracy

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A new AI algorithm developed by the University of Pittsburgh has achieved the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity. Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer after review by AI. A study published this week in The Lancet Digital Health by University of Pittsburgh researchers demonstrates the highest accuracy to date in recognising and characterising prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognising anomalies, but they have their own biases or past experience," said Rajiv Dhir, Professor of Biomedical Informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognise prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Expert pathologists labelled each image to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides from 100 consecutive patients, seen at the University of Pittsburgh Medical Center (UPMC) for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer – significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumour grading, sizing and invasion of the surrounding nerves. These are all clinically important features, required as part of the pathology report. The AI flagged six slides that were missed by the expert pathologists. However, Professor Dhir explains that this does not necessarily mean that the machine is superior to humans. For example, while evaluating these cases, a pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A non-specialised person may not be able to make the correct assessment.


Artificial Intelligence Software May Prevent Unneeded Cancer Surgeries – IAM Network

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Case Western Reserve University researchers are investigating how artificial intelligence (AI) can improve cancer treatments. The technology could possibly cut down on the number of surgeries needed, said lead researchers Pallavi Tiwari and Satish Viswanath. Tiwari specializes in brain cancers, while Viswanath studies colorectal cancers. In both types of cancers, lesions or dead tissues often show up on MRI scans after a patient receives treatment, which can resemble recurring tumors, said Tiwari. "The only definitive diagnosis comes from a surgical resection, which means that they go in and do a biopsy, or they go in and take the whole thing out – only to later discover that it's a benign condition," she said.


Artificial Intelligence Identifies Prostate Cancer With Near-Perfect Accuracy

#artificialintelligence

Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.