{"id":11459,"date":"2024-11-22T19:53:58","date_gmt":"2024-11-22T19:53:58","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=11459"},"modified":"2024-11-22T19:54:00","modified_gmt":"2024-11-22T19:54:00","slug":"empowering-mental-health-ai-model-marks-a-new-era-in-depression-diagnosis","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/empowering-mental-health-ai-model-marks-a-new-era-in-depression-diagnosis\/","title":{"rendered":"Empowering Mental Health: AI Model Marks a New Era in Depression Diagnosis"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">Researchers at Kaunas University of Technology (KTU) have pioneered an artificial intelligence model that diagnoses depression with 97.53% accuracy by analyzing speech and brain neural activity, marking a significant breakthrough in mental health diagnostics.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>Depression affects approximately 280 million people globally, and diagnosing it accurately has always been a significant challenge. Researchers at Kaunas University of Technology (KTU) have made a breakthrough in this field by developing an artificial intelligence (AI) model that can identify depression with exceptional precision by analyzing both speech patterns and brain neural activity.<\/p>\n\n\n\n<p>\u201cDepression is one of the most common mental disorders, with devastating consequences for both the individual and society, so we are developing a new, more objective diagnostic method that could become accessible to everyone in the future,\u201d co-author Rytis Maskeli\u016bnas, a professor in the Department of Multimedia Engineering at KTU, said in a <a href=\"https:\/\/en.ktu.edu\/news\/ktu-researchers-use-artificial-intelligence-to-diagnose-depression\/\" title=\"\">news release<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Multimodal Approach Enhances Diagnostic Accuracy<\/h2>\n\n\n\n<p><a href=\"https:\/\/www.mdpi.com\/2076-3425\/14\/10\/1018\" title=\"\">Published<\/a> in the Brain Sciences Journal, the innovation stems from a multimodal approach that integrates two types of data: speech and electrical brain activity (EEG). <\/p>\n\n\n\n<p>The researchers argue that while most traditional diagnostic methods rely on one type of data, this dual approach offers a more comprehensive understanding of a person&#8217;s emotional state.<\/p>\n\n\n\n<p>This combined analysis achieved an impressive 97.53% accuracy in diagnosing depression, a significant improvement over existing methods. <\/p>\n\n\n\n<p>\u201cThis is because the voice adds data to the study that we cannot yet extract from the brain,\u201d added Maskeli\u016bnas.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Voice and Brain Data: A Potent Diagnostic Duo<\/h2>\n\n\n\n<p>The choice of data sources was carefully deliberated, according to Musyyab Yousufi, a doctoral student who contributed to the project. He noted that while facial expressions could offer some insights into a person&#8217;s psychological state, they can be easily manipulated. <\/p>\n\n\n\n<p>\u201cWe chose voice because it can subtly reveal an emotional state, with the diagnosis affecting the pace of speech, intonation and overall energy,\u201d Yousufi said the news release.<\/p>\n\n\n\n<p>Patients&#8217; privacy was another critical consideration. Traditional methods like facial recognition can intrude on privacy, whereas speech and EEG offer less invasive but equally informative data. <\/p>\n\n\n\n<p>\u201c[C]ollecting and combining data from several sources is more promising for further use,\u201d added Maskeli\u016bnas.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Path Forward: Enhancing AI Transparency and Understanding<\/h2>\n\n\n\n<p>The KTU research team utilized the Multimodal Open Dataset for Mental Disorder Analysis (MODMA) for their EEG data. This data was collected in a controlled setting, with the participants at rest, eyes closed for five minutes. Concurrently, the participants\u2019 natural speech was recorded during a question-and-answer session and while describing pictures.<\/p>\n\n\n\n<p>To process this data, it was transformed into spectrograms, visual representations of the signals. Advanced noise filters and a modified DenseNet-121 deep-learning model were employed to identify depression indicators in these images.<\/p>\n\n\n\n<p>Moving forward, this AI model could make depression diagnosis quicker and more accessible, potentially facilitating remote evaluations and reducing subjective biases. However, challenges remain. <\/p>\n\n\n\n<p>\u201cThe main problem with these studies is the lack of data because people tend to remain private about their mental health matters,\u201d Maskeli\u016bnas explained.<\/p>\n\n\n\n<p>A significant future task for the researchers is enhancing the algorithm&#8217;s ability to explain its diagnostic process clearly. <\/p>\n\n\n\n<p>\u201cThe algorithm still has to learn how to explain the diagnosis in a comprehensible way,\u201d Maskeli\u016bnas added.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Broader Implications: Explainable AI in Health Care<\/h2>\n\n\n\n<p>As AI solutions gain traction in sensitive fields like health care, finance and law, the demand for explainable artificial intelligence (XAI) is increasing. XAI aims to make AI&#8217;s decision-making process transparent, thereby building trust and ensuring that these systems can be reliably integrated into critical areas.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>With this development, KTU opens a promising avenue towards more accurate, objective and understandable diagnoses of depression, potentially revolutionizing the way mental health issues are identified and treated.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Depression affects approximately 280 million people globally, and diagnosing it accurately has always been a significant challenge. Researchers at Kaunas University of Technology (KTU) have made a breakthrough in this field by developing an artificial intelligence (AI) model that can identify depression with exceptional precision by analyzing both speech patterns and brain neural activity. \u201cDepression [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-11459","post","type-post","status-publish","format-standard","hentry","category-ai"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"Depression affects approximately 280 million people globally, and diagnosing it accurately has always been a significant challenge. Researchers at Kaunas University of Technology (KTU) have made a breakthrough in this field by developing an artificial intelligence (AI) model that can identify depression with exceptional precision by analyzing both speech patterns and brain neural activity. \u201cDepression&hellip;","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/11459","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=11459"}],"version-history":[{"count":8,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/11459\/revisions"}],"predecessor-version":[{"id":11602,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/11459\/revisions\/11602"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=11459"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=11459"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=11459"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}