{"id":12856,"date":"2019-04-09T11:28:01","date_gmt":"2019-04-09T11:28:01","guid":{"rendered":"http:\/\/stblog.lunaeme.com\/?p=12856"},"modified":"2023-09-20T16:05:03","modified_gmt":"2023-09-20T16:05:03","slug":"new-machine-learning-algorithm-for-breast-cancer-detection","status":"publish","type":"post","link":"https:\/\/www.stratio.com\/blog\/new-machine-learning-algorithm-for-breast-cancer-detection\/","title":{"rendered":"New Machine Learning Algorithm for Breast Cancer Detection"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Artificial intelligence (AI) is very quickly expanding in the healthcare industry. AI\u2019s primary role has been to automate processes to make it easier for employees to work on more complex people-related concerns. Yet as the technology develops, <\/span><a href=\"https:\/\/www.healthcentral.com\/slideshow\/8-ways-artificial-intelligence-is-affecting-the-medical-field#slide=4\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"font-weight: 400;\">Healthcare Central reports that AI is also being used as a diagnostic tool<\/span><\/a><span style=\"font-weight: 400;\">. With a faster, more accurate way of detecting illnesses, and administering treatments, the mortality rate could be significantly reduced. This includes the treatment of conditions like cancer.<\/span><!--more--><a href=\"http:\/\/www.thedartmouth.com\/article\/2019\/02\/dartmouth-research-machine-learning-breast-surgery\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"font-weight: 400;\">A Dartmouth research team is harnessing machine learning<\/span><\/a><span style=\"font-weight: 400;\"> to predict the chances of breast cancer. The assistant professor of biomedical data science and epidemiology in Geisel School of Medicine, Saeed Hassanpour, along with his team, are currently developing the technology. The AI looks at the possibility that a breast lesion found during a medical examination is or can be cancerous. This comes after breast cancer screenings inducing false negatives have put women in danger of overdiagnosis and overtreatment.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-12869\" src=\"http:\/\/blog.stratio.com\/wp-content\/uploads\/2019\/04\/im1.jpg\" alt=\"\" width=\"1080\" height=\"600\"><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<p><span style=\"font-weight: 400;\">According to Hassanpour, doctors currently perform a core needle biopsy whenever a marker for high-risk breast cancer is found\u2014and it doesn\u2019t matter whether the lesion is malignant or benign. \u201cSeventy to 80 percent of women didn\u2019t need this surgery. Only 20 to 30 percent of patients are found to have cancerous lesions,\u201d he explains. This led to a search for a more personalized and less invasive alternative using machine learning.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">Geisel School of Medicine\u2019s associate professor of radiology and gynecology and obstetrics Roberta DiFlorio Alexander, used AI in radiology research. She found that the use of digital imagining allows researchers to mine vast amounts of data. After the data is combined with molecular and proteomic data it can help \u201ccreate personalized medicine and a better understanding of a patient\u2019s disease\u201d. She believes that this will eventually allow for a &#8220;personalized treatment for each patient\u2019s unique cancer.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-12870\" src=\"http:\/\/blog.stratio.com\/wp-content\/uploads\/2019\/04\/im2.jpg\" alt=\"\" width=\"1080\" height=\"600\"><\/span><\/p>\n<p>With the breakthroughs in algorithm techniques that yield a surplus of data, medical professionals can diagnose illnesses much faster. A number of computer-assisted diagnoses through deep learning algorithms are already in place, with some being applied to the three common diagnoses in dermatopathology, <a href=\"http:\/\/www.jpathinformatics.org\/article.asp?issn=2153-3539;year=2018;volume=9;issue=1;spage=32;epage=32;aulast=Olsen\" target=\"_blank\" rel=\"noopener noreferrer\">according to an article in the Journal of Pathology Informatics<\/a>. The article found that there was a high degree of accuracy \u201cwith 113 out of 114 cases being correctly diagnosed by the algorithm with sensitivity of 98.8% and specificity of 100%.\u201d<\/p>\n<p>With machine learning, patients can also be diagnosed not only faster, but earlier, too. This will have a huge impact on healthcare, especially since the number of nurses and doctors are consistently decreasing and time makes a huge difference in a patient\u2019s chances of recovery. <a href=\"https:\/\/online.maryville.edu\/online-doctorate-degrees\/doctor-nursing-practice\/\" target=\"_blank\" rel=\"noopener noreferrer\">A post by Maryville University<\/a> details how there is expected to be a shortage of 100,000 primary care physicians by 2025. This means that more people with conditions like breast cancer may not be diagnosed early or correctly. Machine learning in healthcare will help make up for this huge shortfall.<\/p>\n<p>All this is possible through <a href=\"http:\/\/blog.stratio.com\/blog\/machine-learning-algorithms-statistical-comparison-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">machine learning algorithms that we\u2019ve discussed on Stratio<\/a>. AI is being used to solve real world problems, and in the healthcare industry this has the potential to make up for the lack of doctors and nurses, and most importantly save lives.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI is very quickly expanding in the healthcare industry. Its primary role has been to automate processes to make it easier for employees to work on more complex people-related concerns. Yet as the technology develops, some reports have shown that AI is also being used as a diagnostic tool.<\/p>\n","protected":false},"author":2,"featured_media":13520,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[686],"tags":[],"ppma_author":[794],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.9 (Yoast SEO v22.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning - Health Care<\/title>\n<meta name=\"description\" content=\"AI is very quickly expanding in the healthcare industry. Its primary role has been to automate processes to make it easier for employees to work on more complex people-related concerns. 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