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| Content Provider | frontiers |
|---|---|
| Author | Park, Junseok Hwang, Youngbae Kim, Hyun Gun Lee, Joon Seong Kim, Jin-Oh Lee, Tae Hee Jeon, Seong Ran Hong, Su Jin Ko, Bong Min Kim, Seokmin |
| Abstract | A training dataset that is limited to a specific endoscope model can overfit artificial intelligence (AI) to its unique image characteristic. The performance of the AI may degrade in images of different endoscope model. The domain adaptation algorithm, i.e., the cycle-consistent adversarial network (cycleGAN), can transform the image characteristics into AI-friendly styles. We attempted to confirm the performance degradation of AIs in images of various endoscope models and aimed to improve them using cycleGAN transformation. Two AI models were developed from data of esophagogastroduodenoscopies collected retrospectively over 5 years: one for identifying the endoscope models, Olympus CV-260SL, CV-290 (Olympus, Tokyo, Japan) and PENTAX EPK-i (PENTAX Medical, Tokyo, Japan), and the other for recognizing the esophagogastric junction. The AIs were trained using 45,683 standardized images from 1,498 cases and validated on 624 separate cases. Between the two endoscope manufacturers, there was a difference in image characteristics that could be distinguished without error by AI. The accuracy of the AI in recognizing gastroesophageal junction was greater than 0.979 in the same endoscope-examined validation dataset as the training dataset. However, they deteriorated in datasets from different endoscopes. CycleGAN can successfully convert image characteristics to ameliorate the performances of the AIs. The improvements were statistically significant and greater in datasets from different endoscope manufacturers (original→AI-trained style, increased area under the receiver operating characteristic curve: CV-260SL→CV-290, 0.0056; CV-260SL→EPK-i, 0.0182; CV-290→CV-260SL, 0.0134; CV-290→EPK-i, 0.0299; EPK-i→CV-260SL, 0.0215; and EPK-i→CV-290, 0.0616). In conclusion, cycleGAN can transform the diverse image characteristics of endoscope models into an artificial intelligence-trained style to improve the detection performance of artificial intelligence. |
| ISSN | 2296858X |
| DOI | 10.3389/fmed.2022.1036974 |
| Volume Number | 9 |
| Journal | Frontiers in Medicine |
| Language | English |
| Publisher Date | 2022-11-10 |
| Access Restriction | Open |
| Subject Keyword | Artificial intelligence Domain adaptation algorithm Generative adversarial network Endoscopes Deep learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine |
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