Precision Medicine in Gastroenterology: An AI-Powered Polyp Detection for Advanced Colonoscopy Diagnostics Using Deep Learning

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Yogesh Chaudhari, Ashish Jani, Darshee Baxi


The use of artificial intelligence (AI) in medical processes, especially colonoscopies, has made big steps forward in precision medicine in gastroenterology. Using deep learning techniques, this abstract looks at how an AI-powered polyp detection system could change the way advanced colonoscopy tests are done. Gastric endoscopists usually do the check by hand, which can lead to mistakes and missed problems. Modern deep learning methods are used in the suggested system to look at endoscope images. This makes polyp detection more accurate and faster. A lot of different types of colonoscopy images are used to train the deep learning model, which learns complex patterns and traits linked to polyps. There are a lot fewer false rejections and fake positives because the AI system is so sensitive and detailed. Adding real-time AI to colonoscopies gives gastroenterologists more power by giving them immediate and accurate feedback that helps them make quick decisions. This makes diagnoses more accurate generally in addition it also makes the work of healthcare workers easier. Also, the system changes and adapts over time by constantly learning from new data and ideas further applying those to improve its performance. This AI-powered polyp detection method in gastroenterology marks the start of a new era of personalized and accurate diagnoses, which is in line with the ideas behind precision medicine. One effect that could happen is the early identification of colon problems, which could improve patient results and lower the cost of healthcare. This brief talks about the benefits and risks of using AI in colonoscopy and how it can help improve precise medicine in the area of gastroenterology. In present work, pre-trained deep learning models viz. VGG19, DenseNet169, InceptionV3, MobileNetV3, and ResNet101 are explored and comparative analysis of performance of these models is discussed with interpretations.

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