Machine Learning's Impact on Advancing Gastrointestinal Diagnosis and Therapeutic Approaches
编号:153 访问权限:仅限参会人 更新:2025-12-23 13:21:18 浏览:101次 拓展类型2

报告开始:2025年12月30日 10:30(Asia/Amman)

报告时间:15min

所在会场:[S9] Track 5: Emerging Trends of AI/ML [S9-1] Track 5: Emerging Trends of AI/ML

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摘要
Machine learning has emerged as a transformative tool in the medical field, particularly in enhancing diagnostic accuracy and therapeutic decision-making. In the context of gastrointestinal (GI) diseases, its application is reshaping early detection and treatment strategies. Traditional GI diagnostic methods often rely heavily on manual interpretation of endoscopic images, which can be time-consuming and subject to inter-observer variability. This can lead to delays in diagnosis and inconsistent therapeutic outcomes. To address these limitations, we propose a Convolutional Neural Network-based system for Analyzing Endoscopic Images (CNN-AEI), aimed at improving the early detection of gastrointestinal abnormalities. This system automates image analysis using deep learning, enabling real-time assessment with higher precision. The proposed method is implemented to support clinicians by providing accurate, consistent, and rapid diagnostic feedback from endoscopic imagery. Experimental results demonstrate that the CNN-AEI framework significantly improves diagnostic accuracy, sensitivity, and specificity compared to conventional assessment methods. This advancement has the potential to reduce diagnostic errors and support timely therapeutic interventions, ultimately enhancing patient outcomes in GI care.
关键词
Machine Learning, Gastrointestinal Diagnosis, Endoscopic Images, Convolutional Neural Networks, Early Detection, Medical Imaging.
报告人
Anvesha Garg
Professor Quantum University Research Center; Quantum University

稿件作者
Anvesha Garg Quantum University Research Center; Quantum University
Noopur Pandey Chitkara University
Sumeet Kaur Chitkara University
Kulandhaivel M Karpagam Academy of Higher Education
Arun Francis G Karpagam College of Engineering
Shruthi K Bekal JAIN (Deemed to be University)
Ling Shing Wong Thailand;Faculty of Health and Life Sciences; INTI -IU University; Nilai; Malaysia;Faculty of Nursing; Shinawatra University; Pathum Thani
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重要日期
  • 会议日期

    12月29日

    2025

    12月31日

    2025

  • 12月30日 2025

    报告提交截止日期

  • 02月10日 2026

    初稿截稿日期

  • 02月10日 2026

    注册截止日期

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