چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | پریناز نژادمختاری |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | مرکز تحقیقات ریز فناوری دارویی |
| کد مقاله | 87824 |
| عنوان فارسی مقاله | Artificial Intelligence in Head and Neck Cancer Imaging: Enhancing Detection, Prognosis, and Treatment Planning |
| عنوان لاتین مقاله | Artificial Intelligence in Head and Neck Cancer Imaging: Enhancing Detection, Prognosis, and Treatment Planning |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | دومین کنگره بین المللی هوش مصنوعی در علوم پزشکی |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | تهران |
| سال انتشار/ ارائه شمسی | 1404 |
| سال انتشار/ارائه میلادی | 2025 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1404/02/24 الی 1404/02/26 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://aims.smums.ac.ir |
| آدرس علمی (Affiliation) نویسنده متقاضی | Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| پریناز نژادمختاری | چهارم |
| سارا الماسی نژاد | اول |
| میراحمد مظلومی | دوم |
| طهورا فایقی ارجمند | سوم |
| عنوان | متن |
|---|---|
| کلمات کلیدی | Artificial Intelligence in Oncology, Radiomics in Head and Neck Cancer, Machine Learning, AI-Driven Precision Medicine, Tumor Detection and Prognostic Modeling |
| خلاصه مقاله | ABSTRACT Background and aims: Artificial Intelligence (AI) and radiomics have emerged as transformative tools in the diagnosis, treatment planning, and prognosis assessment of head and neck cancers (HNCs). Leveraging machine learning (ML) and deep learning algorithms, AI facilitates the extraction and analysis of high-dimensional radiomic features from medical imaging, enabling precise tumor characterization and personalized therapeutic strategies. The integration of AI in radiomics has the potential to enhance early tumor detection, predict treatment response, and minimize radiation toxicity, ultimately improving patient outcomes. This study aims to provide a comprehensive analysis of AI-driven radiomics in HNC care, with a particular focus on its application in multidisciplinary tumor board decision-making. By examining the opportunities, underlying mechanics, and challenges associated with AI in radiomics, this review seeks to highlight its role in advancing clinical decision support systems and optimizing cancer management. Method: A systematic review was conducted using databases such as PubMed, Scopus, and Google Scholar to identify relevant studies published between 2020 and 2025. Keywords included 'AI in head and neck cancer,' 'radiomics in oncology,' 'machine learning in cancer imaging,' and 'AI-driven precision oncology.' After screening 75 articles, 20 were selected based on relevance and methodological rigor. The review analyzed AI-driven radiomics applications in HNC, focusing on tumor segmentation, prognostic modeling, and treatment optimization. Results: AI-powered radiomics has demonstrated significant potential in HNC by refining tumor detection accuracy, enabling automated segmentation, and enhancing predictive modeling for disease progression and therapeutic response. Machine learning algorithms analyze imaging biomarkers to guide clinical decision-making, reducing reliance on invasive procedures. Despite these advancements, challenges persist, including data heterogeneity, standardization issues, and ethical concerns related to patient privacy and algorithmic bias. Moreover, the lack of large-scale, annotated datasets and regulatory hurdles impede the widespread clinical adoption of AI-driven radiomics. Conclusion: AI and radiomics offer groundbreaking opportunities for precision medicine in HNC management. However, overcoming technical, ethical, and regulatory challenges is crucial to ensuring safe and effective implementation. Collaborative efforts between researchers, clinicians, and regulatory bodies are essential to harness AI’s full potential in revolutionizing HNC care. |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| congress sara.pdf | 1404/03/21 | 158071 | دانلود |
| c6717908e503b784a73117cd609156b5.jpg | 1404/03/21 | 424690 | دانلود |
