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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.


Novel AI imaging approach yields improved skin cancer diagnosis

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A novel imaging approach using artificial intelligence was associated with improved detection of parameters associated with melanoma, according to results presented at the International Conference on Image Analysis and Recognition. The researchers suggested that a number of quantitative imaging approaches to dealing with melanoma have focused largely on skin lesions using "hand crafted imaging features." The current study employed "machine-learning" software, which records abstract quantitative features on images and can model physiological traits of the patients, according to study background. The two features of melanoma that were assessed in the analysis were eumelanin and hemoglobin concentrations observed on dermal imaging. The researchers created a non-linear random forest regression model culled from the images.


AI program uses vocal biomarkers to diagnose COVID-19

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An artificial intelligence voice analysis tool can help diagnose COVID-19 in asymptomatic patients, according to its manufacturer, Vocalis Health. The technology -- called VocalisCheck -- works by comparing a person's voice sample to a COVID-19-positive voice composite. VocalisCheck assesses their risk level of testing positive for COVID-19 and whether they require further testing. According to the company, early study results show that VocalisCheck had a sensitivity of 87% and specificity of of 53%, when used alone, adding "even better" results were achieved when combined with a symptom questionnaire. "Over time, we will collect more and more data, which can strengthen the AI and make the vocal biomarker even more accurate," Shady Hassan, MD, co‐founder, chief medical and chief operating officer of Vocalis Health, told Healio Primary Care.


Artificial intelligence examining ECGs may predict mortality, AF

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Deep neural networks identified potential adverse outcomes and atrial fibrillation from 12-lead ECGs that were originally interpreted as normal, according to new research presented at the American Heart Association Scientific Sessions. "Applications of machine learning and artificial intelligence techniques to problems in health care are increasingly common, but generally focus on diagnostic problems such as detecting features in an image of classifying a current diagnosis based on present features," Christopher M. Haggerty, PhD, assistant professor in the department of imaging science and innovation, and Brandon K. Fornwalt, MD, PhD, associate professor and director of the department of imaging science and innovation, both at Geisinger in Danville, Pennsylvania, told Healio. "Few studies have been able to apply machine learning to the task of predicting future events or patient outcomes. This work is among the first to demonstrate proof of concept for predicting a future patient event -- 1-year mortality -- with good performance based solely on 12-lead electrocardiography data." Sushravya M. Raghunath, PhD, math and computational scientist in the department of imaging science and innovation at Geisinger, and colleagues analyzed 1,775,926 12-lead resting ECGs of 397,840 patients from 34 years of archived medical records.


VIDEO: 'Good, bad news' for the future of colorectal cancer screening

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"I think increasingly patients are going to ask for non-invasive screening instead of colonoscopy or using non-invasive screening to achieve much higher population rates of screening," Levin told Healio Gastroenterology and Liver Disease. Levin said Kaiser Permanente has conducted mailed outreach to patients using fecal immunochemical tests (FIT), which has led to an increase in screening rates above 80% in a 5-year period. There was also a 52% decrease in colorectal cancer mortality and a 26% decrease in colorectal cancer incidence. Additionally, Levin said future trends for colonoscopy and colorectal cancer screening may lead to an increase of earlier detection based on American Cancer Society recommendations. "I think it's much more efficient and much more cost effective if you do that screening with a non-invasive test," Levin said.