| Autor: |
Bojiang Zhang, Wei Zhang, Hongjuan Yao, Jinggui Qiao, Haimiao Zhang, Ying Song |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Frontiers in Medicine, Vol 11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-858X |
| DOI: |
10.3389/fmed.2024.1323516 |
| Popis: |
BackgroundArtificial intelligence-assisted gastroscopy (AIAG) based on deep learning has been validated in various scenarios, but there is a lack of studies regarding diagnosing neoplasms under white light endoscopy. This study explored the potential role of AIAG systems in enhancing the ability of endoscopists to diagnose gastric tumor lesions under white light.MethodsA total of 251 patients with complete pathological information regarding electronic gastroscopy, biopsy, or ESD surgery in Xi’an Gaoxin Hospital were retrospectively collected and comprised 64 patients with neoplasm lesions (excluding advanced cancer) and 187 patients with non-neoplasm lesions. The diagnosis competence of endoscopists with intermediate experience and experts was compared for gastric neoplasms with or without the assistance of AIAG, which was developed based on ResNet-50.ResultsFor the 251 patients with difficult clinical diagnoses included in the study, compared with endoscopists with intermediate experience, AIAG’s diagnostic competence was much higher, with a sensitivity of 79.69% (79.69% vs. 72.50%, p = 0.012) and a specificity of 73.26% (73.26% vs. 52.62%, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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