مجله علمی رایانش نرم و فناوری اطلاعات

مجله علمی رایانش نرم و فناوری اطلاعات

الگوهای ضایعاتی اختصاصی جمعیت در مولتیپل اسکلروزیس: تحلیل خودکار یادگیری عمیق پایگاه داده بزرگ ایرانی

نوع مقاله : مقاله پژوهشی فارسی

نویسندگان
مهدی بشیری باویل 1
موسی شمسی 1
ابوالحسن شاکری باویل 2
1 دانشکده مهندسی پزشکی، دانشگاه صنعتی تبریز، تبریز، ایران.
2 دانشکده رادیولوژی، دانشگاه علوم پزشکی تبریز، تبریز، ایران.
10.22034/jscit.2026.555620.2186
چکیده
مولتیپل اسکلروزیس تقریباً 2.8 میلیون نفر را در سراسر جهان تحت تأثیر قرار می‌دهد، بااین‌حال جمعیت‌های خاورمیانه علی‌رغم شیوع بالاتر بیماری، در مطالعات تصویربرداری عصبی کمتر بررسی‌شده‌اند که درک ویژگی‌های پاتولوژیک خاص جمعیت را محدود و توسعه استراتژی‌های درمانی متناسب را با مانع مواجه می‌سازد. بررسی‌های مقایسه‌ای بین بیماران ام‌اس و افراد سالم برای شناسایی تغییرات مغزی و تعریف محدوده‌های هنجار نشانگرهای زیستی حیاتی است. این مطالعه گذشته‌نگر 1,381 شرکت‌کننده (1,000 سالم، 381 ام‌اس) از شمال غرب ایران را با معماری‎های یادگیری عمیق برای تحلیل خودکار توالی‌های FLAIR بررسی کرد. طبقه‌بندی نوروآناتومیک ضایعات پری‌بطنی، پارابطنی و جاکستاکورتیکال را متمایز نمود و ارزیابی‌های آماری شامل طبقه‌بندی سن-جنسیت و تحلیل‌های همبستگی بود. معماری attention U-Net عملکرد بالینی قابل‌قبولی نشان داد (0.756IoU=، 0.858Dice=). شرکت‌کنندگان ام‎اس، با نسبت‌های نرمال‌شده از ۰.۱۳٪ به 0.71٪ (0.82=r=، 0.001>p)، بار ضایعاتی 5.5 برابری بالاتری نسبت به کنترل نشان دادند. ضایعات پری‌بطنی، با تفاوت‌های محسوس توزیع آناتومیک جنسیت‌محور، اکثریت بار کل را تشکیل دادند (٪58.02±28.35). ارتباطات سن‌محور در ام‌اس برجسته‌تر بود (نسبت بطنی: 0.319r=، بار ضایعاتی: 0.230r=). این مطالعه مقادیر مرجع معتبر برای نشانگرهای زیستی تصویربرداری ام‌اس در جمعیت‌های خاورمیانه تعیین می‌کند و ویژگی‌های تجمع ضایعات با غالبیت پری‌بطنی را نشان می‌دهد. روش خودکار بخش‌بندی و تحلیل آماری، شکاف‌های موجود در تحقیقات بین‌المللی ام‌اس را مرتفع می‌سازد.
کلیدواژه‌ها
مولتیپل اسکلروزیس
تصویربرداری عصبی
بخش‎بندی FLAIR
یادگیری عمیق
Attention U-Net
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مجله علمی رایانش نرم و فناوری اطلاعات
دوره 15، شماره 1
بهار
بهار 1405
صفحه 58-75