{"id":412248,"date":"2021-01-11T11:18:13","date_gmt":"2021-01-11T16:18:13","guid":{"rendered":"http:\/\/www.marketnewsdesk.com\/?p=412248"},"modified":"2021-01-11T11:18:13","modified_gmt":"2021-01-11T16:18:13","slug":"radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study","status":"publish","type":"post","link":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/","title":{"rendered":"RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study"},"content":{"rendered":"<div class=\"mw_release\">\n<p align=\"left\">CAMBRIDGE, Mass. and LOS ANGELES, Jan.  11, 2021  (GLOBE NEWSWIRE) &#8212; RadNet, Inc. (NASDAQ: RDNT) today reported that its DeepHealth subsidiary, a leading developer of artificial intelligence (AI) for mammography interpretation, published results of its novel AI algorithms in Nature Medicine, illustrating an ability to detect breast cancer a year or more earlier than current practice.<\/p>\n<p>DeepHealth compared its AI to five full-time, breast-fellowship-trained expert radiologists reading the same screening mammograms. The software exhibited higher performance than all five radiologists, and the results suggest that the AI could help detect cancer one to two years earlier than standard interpretation in many cases. Additionally, the software showed promising generalization capabilities, demonstrating strong performance when tested across clinical sites and populations that were not directly involved in training the AI algorithms.<\/p>\n<p>While AI holds tremendous promise for improving screening mammography interpretation, there remain substantial challenges in developing expert-level AI. \u201cReaching world-class performance requires a new way of building AI,\u201d said Gregory Sorensen, M.D., CEO, and co-founder of DeepHealth. \u201cThe brute-force methods that have worked so well in other domains, such as self-driving cars or game playing, where data is plentiful, have not translated effectively to many parts of medicine, where human data is often scarce. For example, to train the technology for better detection, AI algorithms must be developed from annotated data where the cancer status is known. Such data can be difficult to obtain. Then, to validate performance, the AI should be tested across different clinical sites and patient populations in different scenarios.\u201d<\/p>\n<p>The new study by DeepHealth demonstrates progress in resolving these challenges. \u201cWe have developed an approach that mimics how humans often learn by progressively training the AI models on more difficult tasks. By leveraging prior information learned in each successive training stage, this strategy results in AI that detects cancer accurately while also relying less on highly-annotated data,\u201d said lead author Bill Lotter, Ph.D., CTO, and co-founder of DeepHealth. \u201cOur approach and validation extend to 3D mammography, which is particularly important given its growing use and the significant challenges it presents for AI.\u201d<\/p>\n<p>\u201cOur results point to the clinical utility of AI for mammography in facilitating earlier breast cancer detection,\u201d Lotter noted, \u201cas well as an ability to develop AI with similar benefits for other medical imaging applications. By building AI software with high performance and generalizability, DeepHealth has the potential to help radiologists enable more widespread access to high quality care.\u201d<\/p>\n<p>DeepHealth\u2019s first AI-based product, a software package for prioritization of 2D and 3D mammograms, is based on the same core technology described in the Nature Medicine publication. This product was submitted to the FDA in late 2020, with approval anticipated in 2021.<\/p>\n<p>Link to Nature Medicine paper: <a href=\"https:\/\/www.globenewswire.com\/Tracker?data=KqmHJdg4KIC4NwaZBwh34-dP_lPGkbcPfdrhBkogJlmQP2SD3g9UDZXT-AJJFsW1BqZLZiy2n7-O65cwGNtOndvWMYzfaSfbKVzdPnqcFxZ8IfBM_fDpYLne1qOd1fA6Wn_ENrduR5MZTpk0L6atSw==\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">https:\/\/www.nature.com\/artic<\/a><a href=\"https:\/\/www.globenewswire.com\/Tracker?data=PjhD--l5GuVVwlxE53cUCQxhQ0XCwAfnNReV-0AhftLsfl9jMmODPGAMmbFLQ1e8ncO8cTmGykImh9DhHFO2ElpDzoMe1Eo39YsRfJt3Yhg=\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">l<\/a><a href=\"https:\/\/www.globenewswire.com\/Tracker?data=5B1eT4K2yEc9-bEin51LCRXxLu2_BpkUozN7_MhZRZBWZ1Qcta2a8enWKhErOPbFnFR3V54mJrYNp5E7hwicTEpR2-eQ6EoYCWYRnseZeLQ=\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">es\/<\/a><a href=\"https:\/\/www.globenewswire.com\/Tracker?data=IbXJBgEg7yuOXe9xryda8X4nYx_O-CGUzO7gihafzUyms_TFbpO5vkH8OCpBd9ESyV5NpYYB2jqOnZTIkjNS3gKDw4_91qTM0JKdAtH-tvE=\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">s<\/a><a href=\"https:\/\/www.globenewswire.com\/Tracker?data=7QGf0iz2WX4ZNnshN3dYrD9G_Q7ReZ99zDtj4eGo0oBY64D4sIxg5f_5i6uJxgsMyfKr0qfniHWSrYkAtGYqZ0WevXnQn6ocIyiGC0mh3Sme73WVQ-3ulbAgZVzX5AU5\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">41591-020-01174-9<\/a><\/p>\n<p>\n        <strong>About RadNet, Inc.<\/strong><br \/>\n        <br \/>RadNet, Inc. is the leading national provider of freestanding, fixed-site diagnostic imaging services in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 334 owned and\/or operated outpatient imaging centers. RadNet\u2019s core markets include California, Maryland, Delaware, New Jersey, Arizona, and New York. In addition, RadNet provides radiology information technology solutions and other related products and services to customers in the diagnostic imaging industry. Together with affiliated radiologists, and inclusive of full-time and per diem employees and technicians, RadNet has approximately 8,600 employees.<\/p>\n<p>For more information, visit <a href=\"https:\/\/www.globenewswire.com\/Tracker?data=pEu9u7UAcZQNPPhqsbGA4UZCL8CdWU-lKVRRjLNpqph5B_o7nfh2pDiV9XZp50ecwCZhIBKvkTMYWi8My9ce5g==\" rel=\"nofollow noopener noreferrer\" target=\"_blank\"><u>www.radnet.com<\/u><\/a>.<\/p>\n<p>\n        <strong>About DeepHealth, Inc.<\/strong><br \/>\n        <br \/>DeepHealth, a wholly owned subsidiary of RadNet, Inc., uses machine learning to distill lifetimes of insights from medical experts into software to assist physicians. DeepHealth\u2019s mission is to enable the best care by providing products that clinicians and patients can trust, through rigorous science and clinical integration. For more information, visit <a href=\"https:\/\/www.globenewswire.com\/Tracker?data=uH5xyKjwspp_mrQ-1ntay4sQ6I3KoGxBEBdDeZojRdOOR5Az06YXzJKGIThnuRIBSQdU4SZKh3Bv-_CtNNFvpw==\" rel=\"nofollow noopener noreferrer\" target=\"_blank\"><u>www.deep.health<\/u><\/a>.<\/p>\n<p align=\"justify\">\n        <strong><br \/>\n          <em>CONTACTS: <\/em><br \/>\n        <\/strong>\n      <\/p>\n<p>\n        <em><br \/>\n          <u>DeepHealth, Inc.<\/u><br \/>\n        <\/em><br \/>\n        <br \/>\n        <em>Bret Baird<\/em><br \/>\n        <br \/>\n        <em>Chief Commercial Officer <\/em><br \/>\n        <br \/>\n        <em>424-832-1480<\/em><br \/>\n        <br \/>\n        <strong><br \/>\n          <em>bbaird@deep.health<\/em><br \/>\n        <\/strong>\n      <\/p>\n<p align=\"justify\">\n        <em><br \/>\n          <u>RadNet, Inc.<br \/><\/u><br \/>\n        <\/em><br \/>\n        <em>Mark Stolper<br \/><\/em><br \/>\n        <em>Executive Vice President and Chief Financial Officer<br \/><\/em><br \/>\n        <em>310-445-2800<\/em>\n      <\/p>\n<p>A photo accompanying this announcement is available at <a href=\"https:\/\/www.globenewswire.com\/Tracker?data=KqmHJdg4KIC4NwaZBwh34zeFjm2Stgvzt9M2Swp4DqRQBg5JHHYd_m9_4bJkeytlzDNCsYfYdgp_-5rUC8oD2h4NW-SE81PwT21w5ODBlGCTH_NLViuDla-eoBIux2vW_iy0S1Iose4Sb_JJEJOF55VUTXdywD4D6iMoNSM1GhnZeAngumJXG34FpK4RDqm60se9NWshf5nsOvCwzZiibE3O6_5hqel_PAHfCH5XlaCjlS74ZYFZ89RgxhbGxRl7Q5eo89PndySQW-1UoO6qiw==\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">https:\/\/www.globenewswire.com\/NewsRoom\/AttachmentNg\/ab4a5a9c-3905-4def-84aa-a15d60c43640<\/a><\/p>\n<p>      <img loading=\"lazy\" decoding=\"async\" class=\"__GNW8366DE3E__IMG\" src=\"https:\/\/www.globenewswire.com\/newsroom\/ti?nf=ODEyMjk4MSMzOTEwOTEyIzIwMDgwMTc=\" width=\"1\" height=\"1\" \/><br \/>\n      <br \/>\n      <img loading=\"lazy\" decoding=\"async\" class=\"__GNW8366DE3E__IMG\" src=\"https:\/\/ml.globenewswire.com\/release\/track\/9204e733-f237-4699-ac18-27012fb9d587\" width=\"1\" height=\"1\" \/>\n    <\/div>\n<div class=\"mw_contactinfo\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>CAMBRIDGE, Mass. and LOS ANGELES, Jan. 11, 2021 (GLOBE NEWSWIRE) &#8212; RadNet, Inc. (NASDAQ: RDNT) today reported that its DeepHealth subsidiary, a leading developer of artificial intelligence (AI) for mammography interpretation, published results of its novel AI algorithms in Nature Medicine, illustrating an ability to detect breast cancer a year or more earlier than current practice. DeepHealth compared its AI to five full-time, breast-fellowship-trained expert radiologists reading the same screening mammograms. The software exhibited higher performance than all five radiologists, and the results suggest that the AI could help detect cancer one to two years earlier than standard interpretation in many cases. Additionally, the software showed promising generalization capabilities, demonstrating strong performance when tested across clinical sites and populations that &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study&#8221;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-412248","post","type-post","status-publish","format-standard","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study - Market Newsdesk<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study - Market Newsdesk\" \/>\n<meta property=\"og:description\" content=\"CAMBRIDGE, Mass. and LOS ANGELES, Jan. 11, 2021 (GLOBE NEWSWIRE) &#8212; RadNet, Inc. (NASDAQ: RDNT) today reported that its DeepHealth subsidiary, a leading developer of artificial intelligence (AI) for mammography interpretation, published results of its novel AI algorithms in Nature Medicine, illustrating an ability to detect breast cancer a year or more earlier than current practice. DeepHealth compared its AI to five full-time, breast-fellowship-trained expert radiologists reading the same screening mammograms. The software exhibited higher performance than all five radiologists, and the results suggest that the AI could help detect cancer one to two years earlier than standard interpretation in many cases. 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Market Newsdesk","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/","og_locale":"en_US","og_type":"article","og_title":"RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study - Market Newsdesk","og_description":"CAMBRIDGE, Mass. and LOS ANGELES, Jan. 11, 2021 (GLOBE NEWSWIRE) &#8212; RadNet, Inc. (NASDAQ: RDNT) today reported that its DeepHealth subsidiary, a leading developer of artificial intelligence (AI) for mammography interpretation, published results of its novel AI algorithms in Nature Medicine, illustrating an ability to detect breast cancer a year or more earlier than current practice. DeepHealth compared its AI to five full-time, breast-fellowship-trained expert radiologists reading the same screening mammograms. The software exhibited higher performance than all five radiologists, and the results suggest that the AI could help detect cancer one to two years earlier than standard interpretation in many cases. Additionally, the software showed promising generalization capabilities, demonstrating strong performance when tested across clinical sites and populations that &hellip; Continue reading \"RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study\"","og_url":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/","og_site_name":"Market Newsdesk","article_published_time":"2021-01-11T16:18:13+00:00","og_image":[{"url":"https:\/\/www.globenewswire.com\/newsroom\/ti?nf=ODEyMjk4MSMzOTEwOTEyIzIwMDgwMTc=","type":"","width":"","height":""}],"author":"Newsdesk","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Newsdesk","Est. reading time":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/#article","isPartOf":{"@id":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/"},"author":{"name":"Newsdesk","@id":"https:\/\/www.marketnewsdesk.com\/#\/schema\/person\/482f27a394d4fda80ecb5499e519d979"},"headline":"RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study","datePublished":"2021-01-11T16:18:13+00:00","mainEntityOfPage":{"@id":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/"},"wordCount":684,"image":{"@id":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/#primaryimage"},"thumbnailUrl":"https:\/\/www.globenewswire.com\/newsroom\/ti?nf=ODEyMjk4MSMzOTEwOTEyIzIwMDgwMTc=","inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/","url":"https:\/\/www.marketnewsdesk.com\/index.php\/radnets-deephealth-artificial-intelligence-subsidiary-demonstrates-earlier-breast-cancer-detection-in-new-study\/","name":"RadNet\u2019s DeepHealth Artificial Intelligence Subsidiary Demonstrates Earlier Breast Cancer Detection in New Study - 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