Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy
Background/Objectives: Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON<sup>®</sup> in detectin...
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MDPI AG
2024-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2706 |
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| author | Hannah Lee Jun-Won Chung Sung-Cheol Yun Sung Woo Jung Yeong Jun Yoon Ji Hee Kim Boram Cha Mohd Azzam Kayasseh Kyoung Oh Kim |
| author_facet | Hannah Lee Jun-Won Chung Sung-Cheol Yun Sung Woo Jung Yeong Jun Yoon Ji Hee Kim Boram Cha Mohd Azzam Kayasseh Kyoung Oh Kim |
| author_sort | Hannah Lee |
| collection | DOAJ |
| description | Background/Objectives: Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON<sup>®</sup> in detecting gastric neoplasm. Methods: We used the retrospective data of 500 still images, including 5 benign gastric ulcers, 95 with gastric cancer, and 400 normal images. Thereby we validated the CADe algorithm measuring accuracy, sensitivity, and specificity with the result of receiver operating characteristic curves (ROC) and area under curve (AUC) in addition to comparing the diagnostic performance status of four expert endoscopists, four trainees, and four beginners from two university-affiliated hospitals with CADe algorithm. After a washing-out period of over 2 weeks, endoscopists performed gastric detection on the same dataset of the 500 endoscopic images again marked by ALPHAON<sup>®</sup>. Results: The CADe algorithm presented high validity in detecting gastric neoplasm with accuracy (0.88, 95% CI: 0.85 to 0.91), sensitivity (0.93, 95% CI: 0.88 to 0.98), specificity (0.87, 95% CI: 0.84 to 0.90), and AUC (0.962). After a washing-out period of over 2 weeks, overall validity improved in the trainee and beginner groups with the assistance of ALPHAON<sup>®</sup>. Significant improvement was present, especially in the beginner group (accuracy 0.94 (0.93 to 0.96) <i>p</i> < 0.001, sensitivity 0.87 (0.82 to 0.92) <i>p</i> < 0.001, specificity 0.96 (0.95 to 0.97) <i>p</i> < 0.001). Conclusions: The high validation performance state of the CADe algorithm system was verified. Also, ALPHAON<sup>®</sup> has demonstrated its potential to serve as an endoscopic educator for beginners improving and making progress in sensitivity and specificity. |
| format | Article |
| id | doaj-art-96e207a2b47f47c599031483c5bb39ee |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-96e207a2b47f47c599031483c5bb39ee2024-12-13T16:24:44ZengMDPI AGDiagnostics2075-44182024-11-011423270610.3390/diagnostics14232706Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal EndoscopyHannah Lee0Jun-Won Chung1Sung-Cheol Yun2Sung Woo Jung3Yeong Jun Yoon4Ji Hee Kim5Boram Cha6Mohd Azzam Kayasseh7Kyoung Oh Kim8Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of KoreaDivision of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of KoreaDivision of Biostatistics, Center for Medical Research and Information, University of Ulsan College of Medicine, Seoul 05505, Republic of KoreaDivision of Gastroenterology, Department of Internal Medicine, Korea University College of Medicine, Ansan 15355, Republic of KoreaCAIMI Co., Ltd., Incheon 22004, Republic of KoreaCAIMI Co., Ltd., Incheon 22004, Republic of KoreaDivision of Gastroenterology, Department of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon 22332, Republic of KoreaDivision of Gastroenterology, Dr. Sulaiman AI Habib Medical Group, Dubai Healthcare City, Dubai 51431, United Arab EmiratesDivision of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of KoreaBackground/Objectives: Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON<sup>®</sup> in detecting gastric neoplasm. Methods: We used the retrospective data of 500 still images, including 5 benign gastric ulcers, 95 with gastric cancer, and 400 normal images. Thereby we validated the CADe algorithm measuring accuracy, sensitivity, and specificity with the result of receiver operating characteristic curves (ROC) and area under curve (AUC) in addition to comparing the diagnostic performance status of four expert endoscopists, four trainees, and four beginners from two university-affiliated hospitals with CADe algorithm. After a washing-out period of over 2 weeks, endoscopists performed gastric detection on the same dataset of the 500 endoscopic images again marked by ALPHAON<sup>®</sup>. Results: The CADe algorithm presented high validity in detecting gastric neoplasm with accuracy (0.88, 95% CI: 0.85 to 0.91), sensitivity (0.93, 95% CI: 0.88 to 0.98), specificity (0.87, 95% CI: 0.84 to 0.90), and AUC (0.962). After a washing-out period of over 2 weeks, overall validity improved in the trainee and beginner groups with the assistance of ALPHAON<sup>®</sup>. Significant improvement was present, especially in the beginner group (accuracy 0.94 (0.93 to 0.96) <i>p</i> < 0.001, sensitivity 0.87 (0.82 to 0.92) <i>p</i> < 0.001, specificity 0.96 (0.95 to 0.97) <i>p</i> < 0.001). Conclusions: The high validation performance state of the CADe algorithm system was verified. Also, ALPHAON<sup>®</sup> has demonstrated its potential to serve as an endoscopic educator for beginners improving and making progress in sensitivity and specificity.https://www.mdpi.com/2075-4418/14/23/2706artificial intelligencecomputer-aided detection (CADe) algorithmgastric neoplasm |
| spellingShingle | Hannah Lee Jun-Won Chung Sung-Cheol Yun Sung Woo Jung Yeong Jun Yoon Ji Hee Kim Boram Cha Mohd Azzam Kayasseh Kyoung Oh Kim Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy Diagnostics artificial intelligence computer-aided detection (CADe) algorithm gastric neoplasm |
| title | Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy |
| title_full | Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy |
| title_fullStr | Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy |
| title_full_unstemmed | Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy |
| title_short | Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy |
| title_sort | validation of artificial intelligence computer aided detection on gastric neoplasm in upper gastrointestinal endoscopy |
| topic | artificial intelligence computer-aided detection (CADe) algorithm gastric neoplasm |
| url | https://www.mdpi.com/2075-4418/14/23/2706 |
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