Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy
Abstract Complete and high-quality photodocumentation in esophagoduodenogastroscopy (EGD) is essential for accurately diagnosing upper gastrointestinal diseases by reducing blind spot rates. Automated Photodocumentation Task (APT), an artificial intelligence-based system for real-time photodocumenta...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83721-9 |
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| author | Byeong Yun Ahn Junwoo Lee Jeonga Seol Ji Yoon Kim Hyunsoo Chung |
| author_facet | Byeong Yun Ahn Junwoo Lee Jeonga Seol Ji Yoon Kim Hyunsoo Chung |
| author_sort | Byeong Yun Ahn |
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| description | Abstract Complete and high-quality photodocumentation in esophagoduodenogastroscopy (EGD) is essential for accurately diagnosing upper gastrointestinal diseases by reducing blind spot rates. Automated Photodocumentation Task (APT), an artificial intelligence-based system for real-time photodocumentation during EGD, was developed to assist endoscopists in focusing more on the observation rather than repetitive capturing tasks. This study aimed to evaluate the completeness and quality of APT’s photodocumentation compared to endoscopists. The dataset comprised 37 EGD videos recorded at Seoul National University Hospital between March and June 2023. Virtual endoscopy was conducted by seven endoscopists and APT, capturing 11 anatomical landmarks from the videos. The primary endpoints were the completeness of capturing landmarks and the quality of the images. APT achieved an average accuracy of 98.16% in capturing landmarks. Compared to that of endoscopists, APT demonstrated similar completeness in photodocumentation (87.72% vs. 85.75%, P = .0.258), and the combined photodocumentation of endoscopists and APT reached higher completeness (91.89% vs. 85.75%, P < .0.001). APT captured images with higher mean opinion scores than those of endoscopists (3.88 vs. 3.41, P < .0.001). In conclusion, APT provides clear, high-quality endoscopic images while minimizing blind spots during EGD in real-time. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-0a2e7e700513428f955dbb9ed22e52a32025-02-09T12:29:51ZengNature PortfolioScientific Reports2045-23222025-02-011511910.1038/s41598-024-83721-9Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopyByeong Yun Ahn0Junwoo Lee1Jeonga Seol2Ji Yoon Kim3Hyunsoo Chung4Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of MedicinePrevenotics Inc.Prevenotics Inc.Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of MedicineDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of MedicineAbstract Complete and high-quality photodocumentation in esophagoduodenogastroscopy (EGD) is essential for accurately diagnosing upper gastrointestinal diseases by reducing blind spot rates. Automated Photodocumentation Task (APT), an artificial intelligence-based system for real-time photodocumentation during EGD, was developed to assist endoscopists in focusing more on the observation rather than repetitive capturing tasks. This study aimed to evaluate the completeness and quality of APT’s photodocumentation compared to endoscopists. The dataset comprised 37 EGD videos recorded at Seoul National University Hospital between March and June 2023. Virtual endoscopy was conducted by seven endoscopists and APT, capturing 11 anatomical landmarks from the videos. The primary endpoints were the completeness of capturing landmarks and the quality of the images. APT achieved an average accuracy of 98.16% in capturing landmarks. Compared to that of endoscopists, APT demonstrated similar completeness in photodocumentation (87.72% vs. 85.75%, P = .0.258), and the combined photodocumentation of endoscopists and APT reached higher completeness (91.89% vs. 85.75%, P < .0.001). APT captured images with higher mean opinion scores than those of endoscopists (3.88 vs. 3.41, P < .0.001). In conclusion, APT provides clear, high-quality endoscopic images while minimizing blind spots during EGD in real-time.https://doi.org/10.1038/s41598-024-83721-9Artificial intelligenceDeep learningEndoscopyQuality control |
| spellingShingle | Byeong Yun Ahn Junwoo Lee Jeonga Seol Ji Yoon Kim Hyunsoo Chung Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy Scientific Reports Artificial intelligence Deep learning Endoscopy Quality control |
| title | Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy |
| title_full | Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy |
| title_fullStr | Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy |
| title_full_unstemmed | Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy |
| title_short | Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy |
| title_sort | evaluation of an artificial intelligence based system for real time high quality photodocumentation during esophagogastroduodenoscopy |
| topic | Artificial intelligence Deep learning Endoscopy Quality control |
| url | https://doi.org/10.1038/s41598-024-83721-9 |
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