A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images
Autor: | Ching Liang Lu, Ying Chun Jheng, Ming Chih Hou, Yen Po Wang, Huann Sheng Wang, Jeng Kai Jiang, Hung En Lin, Yuan Chia Chu, Kuang Yi Sung, Fa-Yauh Lee |
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Rok vydání: | 2021 |
Předmět: |
medicine.diagnostic_test
business.industry Normal colon Colonoscopy CAD medicine.disease Convolutional neural network digestive system diseases Colon polyps 03 medical and health sciences 0302 clinical medicine Hyperplastic Polyp 030220 oncology & carcinogenesis Optical diagnosis medicine 030211 gastroenterology & hepatology Surgery business Algorithm |
Zdroj: | Surgical Endoscopy. 36:640-650 |
ISSN: | 1432-2218 0930-2794 |
Popis: | Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks. Colonoscopic images were obtained to train and validate the AI classifiers. An independent dataset was collected for verification. The architecture of GUTAID contains two major sub-models: the Normal, Polyp, Diverticulum, Cecum and CAncer (NPDCCA) and Narrow-Band Imaging for Adenomatous/Hyperplastic polyps (NBI-AH) models. The development of GUTAID was based on the 16-layer Visual Geometry Group (VGG16) architecture and implemented on Google Cloud Platform. In total, 7838 colonoscopy images were used for developing and validating the AI model. An additional 1273 images were independently applied to verify the GUTAID. The accuracy for GUTAID in detecting various colon lesions/landmarks is 93.3% for polyps, 93.9% for diverticula, 91.7% for cecum, 97.5% for cancer, and 83.5% for adenomatous/hyperplastic polyps. A CNN-based algorithm (GUTAID) to identify colonic abnormalities and landmarks was successfully established with high accuracy. This GUTAID system can further characterize polyps for optical diagnosis. We demonstrated that AI classification methodology is feasible to identify multiple and different colon diseases. |
Databáze: | OpenAIRE |
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