مروری بر رویکردهای هوش مصنوعی در تشخیص اختلال بیش فعالی و نقص توجه
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
طاهره ضیاالدینی
1
(
گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
)
محبوبه هوشمند
2
(
گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
)
سید عابد حسینی
3
(
گروه مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
)
کلید واژه: اختلال بیش فعالی/نقص توجه, هوش مصنوعی, یادگیری ماشین, یادگیری عمیق.,
چکیده مقاله :
بیش فعالی/نقص توجه (ADHD) یک اختلال رفتاری-عصبی شایع در کودکان و نوجوانان است که توجه محدود، بیش فعالی و تکانش گری را به همراه دارد. تشخیص دقیق و به‌موقع این اختلال بسیار مهم است تا بتوان مداخلات مناسب درمانی را ارائه داد. در این میان، رویکردهای مبتنی بر هوش مصنوعی می توانند نقش مهمی در بهبود و تسریع فرآیند تشخیص ایفا کنند. این پژوهش به بررسی استفاده از فناوری های هوش مصنوعی در تشخیص ADHD می‌پردازد. بدین منظور ابتدا به معرفی ویژگی های بالینی و چالش‌های موجود در تشخیص ADHD پرداخته می‌شود، سپس انواع رویکردهای هوش مصنوعی مانند یادگیری ماشین، پردازش زبان طبیعی و یادگیری عمیق در این زمینه بررسی می‌شوند. همچنین مطالعه های موردی و نتایج تجربی استفاده از این فناوری‌ها مرور می‌شوند. درنهایت چالش‌ها و محدودیّت‌های موجود و همچنین چشم‌انداز آینده پژوهش‌ها در این حوزه موردبحث قرار می‌گیرند. این پژوهش مروری بر آخرین پیشرفت‌های علمی در استفاده از هوش مصنوعی برای تشخیص ADHD ارائه می‌دهد و می‌تواند به‌عنوان یک منبع برای متخصصان بالینی و پژوهشگران این حوزه مورداستفاده قرار گیرد.
Attention deficit/hyperactivity disorder (ADHD) is a common neurobehavioral disorder in children and adolescents that involves limited attention, hyperactivity, and impulsivity. Accurate and timely diagnosis of this disorder is very important to provide appropriate therapeutic interventions. Meanwhile, approaches based on artificial intelligence can play an important role in improving and speeding up the diagnosis process. This research examines the use of artificial intelligence technologies in the diagnosis of ADHD. For this purpose, the clinical features and challenges in diagnosing ADHD are first introduced, then various artificial intelligence approaches such as machine learning, natural language processing, and deep learning are examined in this field. Also, case studies and experimental results of using these technologies are reviewed. Finally, the existing challenges and limitations as well as the future prospects of research in this field are discussed. This research provides an overview of the latest scientific advances in the use of artificial intelligence for the diagnosis of ADHD and can be used as a resource for clinicians and researchers in this field.
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