Objective: This study aimed to evaluate ambispectively the effectiveness of a real-time computer-aided detection (CADe) system on the number of polyp (PPC) or adenoma per colonoscopy (APC), and polyp (PDR) or adenoma detection rate (ADR).
Methods: Eight-five videos marked using the CADe system, together with the unmarked videos, were reviewed by two senior endoscopists. Polyps detected in the marked and unmarked videos were recounted in parallel. Additionally, 128 consecutive patients were enrolled for a prospective evaluation using a standard colonoscopy or the CADe monitor alternately every 2 weeks. The PC, APC, PDR and ADR were compared between the two groups.
Results: The total number of polyps reported in the unmarked and marked videos were 73 and 88, respectively (mean PPC 0.86 vs 1.04, P = 0.001). The proportion of polyps detected per colonoscopy increased by 20.5%. Of the 128 prospectively enrolled patients, 186 polyps were detected. The mean PPC was higher in the CADe colonoscopy than in the standard colonoscopy (1.66 vs 1.13, P = 0.039). The PDR using the CADe colonoscopy was significantly higher than that of the standard colonoscopy (78.1% vs 56.3%, P = 0.008).
Conclusion: Real-time CADe system significantly increases the PDR and PPC under the situation of a high rate of polyp detection.
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http://dx.doi.org/10.1111/1751-2980.12985 | DOI Listing |
Background: Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedures.
Summary: Colonoscopy is essential for colorectal cancer screening, but often misses a significant percentage of adenomas.
NPJ Digit Med
December 2024
Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea.
This study evaluated the impact of differing false positive (FP) rates in two computer-aided detection (CADe) systems on the clinical effectiveness of artificial intelligence (AI)-assisted colonoscopy. The primary outcomes were adenoma detection rate (ADR) and adenomas per colonoscopy (APC). The ADR in the control, system A (3.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Division of Gastroenterology, Dr. Sulaiman AI Habib Medical Group, Dubai Healthcare City, Dubai 51431, United Arab Emirates.
Background/objectives: Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps.
View Article and Find Full Text PDFDiagnostics (Basel)
November 2024
Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of Korea.
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 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.
J Gastroenterol Hepatol
December 2024
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Background: Several recent studies have found that the efficacy of computer-aided polyp detection (CADe) on the adenoma detection rate (ADR) diminished in real-world settings. The role of unmeasured factors in AI-human interaction, such as monitor approaches, remains unknown. This study aimed to validate the effectiveness of CADe in the real world and assess the impact of monitor approaches.
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