Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders
In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection...
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| Language: | English |
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Korean College of Helicobacter and Upper Gastrointestinal Research
2021-12-01
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| Series: | The Korean Journal of Helicobacter and Upper Gastrointestinal Research |
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| Online Access: | http://www.helicojournal.org/upload/pdf/kjhugr-2021-0030.pdf |
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| author | Chang Seok Bang |
| author_facet | Chang Seok Bang |
| author_sort | Chang Seok Bang |
| collection | DOAJ |
| description | In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed. |
| format | Article |
| id | doaj-art-07109cc8ff1f4bdd9b27a1c588b1ddee |
| institution | DOAJ |
| issn | 1738-3331 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Korean College of Helicobacter and Upper Gastrointestinal Research |
| record_format | Article |
| series | The Korean Journal of Helicobacter and Upper Gastrointestinal Research |
| spelling | doaj-art-07109cc8ff1f4bdd9b27a1c588b1ddee2025-08-20T02:41:46ZengKorean College of Helicobacter and Upper Gastrointestinal ResearchThe Korean Journal of Helicobacter and Upper Gastrointestinal Research1738-33312021-12-0121430031010.7704/kjhugr.2021.0030692Artificial Intelligence in the Analysis of Upper Gastrointestinal DisordersChang Seok Bang0Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, KoreaIn the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed.http://www.helicojournal.org/upload/pdf/kjhugr-2021-0030.pdfartificial intelligenceconvolutional neural networkdeep learningendoscopygastroenterology |
| spellingShingle | Chang Seok Bang Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders The Korean Journal of Helicobacter and Upper Gastrointestinal Research artificial intelligence convolutional neural network deep learning endoscopy gastroenterology |
| title | Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders |
| title_full | Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders |
| title_fullStr | Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders |
| title_full_unstemmed | Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders |
| title_short | Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders |
| title_sort | artificial intelligence in the analysis of upper gastrointestinal disorders |
| topic | artificial intelligence convolutional neural network deep learning endoscopy gastroenterology |
| url | http://www.helicojournal.org/upload/pdf/kjhugr-2021-0030.pdf |
| work_keys_str_mv | AT changseokbang artificialintelligenceintheanalysisofuppergastrointestinaldisorders |