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Nateghi M R, Nikzad H, Hassani Bafrani M. The use of Machine Learning for Human Oocyte selection and Success Rate in IVF Methods. SJMR 2024; 9 (2) : 5
URL: http://saremjrm.com/article-1-335-fa.html
URL: http://saremjrm.com/article-1-335-fa.html
ناطقی محمدرضا، نیک زاد حسین، حسنی بافرانی مهدی. استفاده از ماشین لرنینگ برای انتخاب تخمک انسانی و نرخ موفقیت در روش‌های لقاح مصنوعی (IVF). مجله تحقيقات پزشكي صارم. 1403; 9 (2) :93-104
1- مرکز تحقیقات زنان زایمان و ناباروری صارم، بیمارستان فوق تخصصی صارم، دانشگاه علوم پزشکی ایران، تهران، ایران و مرکز تحقیقات سلولی-مولکولی و سلول‌های بنیادی صارم، بیمارستان فوق تخصصی صارم تهران، ایران
2- مرکز تحقیقات گامتوژنزیس دانشگاه علوم پزشکی کاشان، کاشان، ایران
3- کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران
2- مرکز تحقیقات گامتوژنزیس دانشگاه علوم پزشکی کاشان، کاشان، ایران
3- کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران
چکیده: (1331 مشاهده)
هدف: روش‌های لقاح مصنوعی (IVF) یکی از اصلی‌ترین راه‌حل‌ها برای مقابله با ناباروری هستند، اما موفقیت این روش‌ها به انتخاب دقیق تخمک‌های با کیفیت وابسته است. در این راستا، ماشین لرنینگ به عنوان یک فناوری نوین، می‌تواند نقش مهمی در بهبود فرآیند انتخاب تخمک ایفا کند. الگوریتم‌های ماشین لرنینگ با تحلیل تصاویر میکروسکوپی و داده‌های مرتبط، ویژگی‌های کلیدی تخمک‌ها را شناسایی کرده و تخمک‌های با پتانسیل بالاتر برای موفقیت در لقاح را انتخاب می‌کنند. استفاده از این فناوری نه تنها دقت انتخاب تخمک‌ها را افزایش می‌دهد و خطاهای انسانی را کاهش می‌دهد، بلکه زمان و هزینه‌های مرتبط با آزمایش‌های دستی را نیز به حداقل می‌رساند. این مقاله به بررسی مزایا، روش‌های مورد استفاده، چالش‌ها و پتانسیل‌های آینده استفاده از ماشین لرنینگ در انتخاب تخمک انسانی برای افزایش نرخ موفقیت در روش‌های IVF می‌پردازد. نتایج نشان می‌دهد که با پیشرفت در این زمینه، می‌توان شاهد بهبود قابل توجهی در نرخ موفقیت و کارایی روش‌های لقاح مصنوعی بود.
مواد و روش‌ها: ما یک جستجوی جامع در PubMed، Google Scholar و Scopus با استفاده از کلمات کلیدی "Machine Learning AND Quantification AND IVF" انجام دادیم. مقالات واجد شرایط ابتدا بر اساس عناوین آن‌ها غربالگری شدند. پس از غربالگری عناوین، یک غربالگری دوم بر اساس چکیده‌های مقالات انتخاب‌شده انجام شد. در نهایت، مقالات کامل مطالعات باقی‌مانده برای اطمینان از تطابق با معیارهای ورود ما مرور شدند. از هر مطالعه واجد شرایط، اطلاعات زیر استخراج شد: نویسنده(ها)ی مطالعه، سال انتشار و روش استفاده شده برای ارزیابی کیفیت تخمک انسانی.
نتیجه‌گیری: توسعه یک سیستم یادگیری ماشین به‌درستی آموزش‌دیده نیازمند توجه دقیق به کیفیت داده‌ها، اندازه‌گیری، اندازه نمونه و مسائل اخلاقی است.
مواد و روش‌ها: ما یک جستجوی جامع در PubMed، Google Scholar و Scopus با استفاده از کلمات کلیدی "Machine Learning AND Quantification AND IVF" انجام دادیم. مقالات واجد شرایط ابتدا بر اساس عناوین آن‌ها غربالگری شدند. پس از غربالگری عناوین، یک غربالگری دوم بر اساس چکیده‌های مقالات انتخاب‌شده انجام شد. در نهایت، مقالات کامل مطالعات باقی‌مانده برای اطمینان از تطابق با معیارهای ورود ما مرور شدند. از هر مطالعه واجد شرایط، اطلاعات زیر استخراج شد: نویسنده(ها)ی مطالعه، سال انتشار و روش استفاده شده برای ارزیابی کیفیت تخمک انسانی.
نتیجه‌گیری: توسعه یک سیستم یادگیری ماشین به‌درستی آموزش‌دیده نیازمند توجه دقیق به کیفیت داده‌ها، اندازه‌گیری، اندازه نمونه و مسائل اخلاقی است.
شماره‌ی مقاله: 5
نوع مقاله: مروری سیستماتيک |
موضوع مقاله:
ناباروری
دریافت: 1403/5/15 | پذیرش: 1403/5/30 | انتشار: 1403/9/29
دریافت: 1403/5/15 | پذیرش: 1403/5/30 | انتشار: 1403/9/29
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