Combining Machine Learning and Nanobiosensors for Improving Lung Cancer Detection

سال انتشار: 1403
محل انتشار: دومین سمپوزیوم منطقه ای نوآوری در علم وفناوری
کد COI اختصاصی: RESIST02_039
زبان مقاله: انگلیسی
تعداد مشاهده: 138

نویسندگان

Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran

Department of Pathobiology, Faculty of Veterinary Specialized Science, Science, and Research Branch, Islamic Azad University, Tehran, Iran

Khashayar Hajjafari

Medical Doctor, Shahid Bahonar University of Kerman, Kerman, Iran

Abbas Rahdar

Department of Physics, University of Zabol, Zabol, Iran

Mahdis Khajehmohammadi

Department of Basic Sciences, Faculty of Veterinary Medicine, Baft branch, Islamic Azad University, Baft, Iran

چکیده

Lung cancer is one of the most prevalent types of cancer in the world, and its clinical prognosis and early detection are of great importance. With recent advances in artificial intelligence and nanotechnology, combining machine learning (ML) and nanobiosensors has been considered a novel approach for diagnosing and monitoring lung cancer. Hence, the current review aims to examine the combination of these two technologies for lung cancer diagnosis and explores its potential and challenges. ML can identify patterns associated with lung cancer with its capabilities in processing complex data and simulating predictive models. Nanobiosensors, on the other hand, can detect biological changes at the molecular and cellular levels with high sensitivity. These two technologies allow for more accurate, faster, and non-invasive lung cancer diagnosis. In addition, using these two technologies simultaneously could help identify more advanced changes in the disease, even before clinical symptoms become apparent. In conclusion, combining machine learning and nanobiosensors offers a novel and efficient approach to lung cancer diagnosis that can significantly increase the accuracy of diagnosis and prognosis.

کلیدواژه ها

Lung cancer, Nanotechnology, Nanobiosensors, Artificial intelligence

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