Lee Hannah, Chung Jun-Won, Kim Kyoung Oh, Kwon Kwang An, Kim Jung Ho, Yun Sung-Cheol, Jung Sung Woo, Sheeraz Ahmad, Yoon Yeong Jun, Kim Ji Hee, Kayasseh Mohd Azzam
Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea.
Division of Biostatistics, Center for Medical Research and Information, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.
Diagnostics (Basel). 2024 Dec 8;14(23):2762. doi: 10.3390/diagnostics14232762.
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.
We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON.
The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON.
The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON being able to provide endoscopic assistance.
背景/目的：在结肠镜检查中控制结肠镜检查质量对于检测结肠息肉很重要，因为这可以降低总体长期结直肠癌风险。人工智能最近已被引入各个医学领域。在本研究中，我们旨在验证一种先前开发的名为ALPHAON的人工智能（AI）计算机辅助检测（CADe）算法，并将结果与先前的研究进行比较，先前的研究表明AI在检测结肠息肉方面优于并辅助了不同专业水平的内镜医师。
我们使用了500张静态图像的回顾性数据，包括100张息肉图像和400张健康结肠图像。此外，我们验证了CADe算法，并将其诊断性能与两名专家内镜医师和来自嘉泉大学吉尔医学中心的六名实习生的诊断性能进行了比较。在超过2周的洗脱期后，内镜医师在ALPHAON的辅助下对同一数据集进行息肉检测。
CADe算法在检测结肠息肉方面表现出很高的能力，准确率为0.97（95%CI：0.96至0.99），灵敏度为0.91（95%CI：0.85至0.97），特异性为0.99（95%CI：0.97至0.99），AUC为0.967。在评估和比较ALPHAON与不同专业水平（根据内镜经验年限）的内镜医师的息肉检测能力时，发现ALPHAON在准确率（0.97，95%CI：0.96至0.99）、灵敏度（0.91，95%CI：0.85至0.97）和特异性（0.99，95%CI：0.97至0.99）方面优于专家。在超过2周的洗脱期后，在ALPHAON的辅助下，专家和实习生的总体能力都有显著提高。
验证了CADe算法系统在结肠镜检查期间检测结肠息肉的高性能。ALPHAON的灵敏度使其优于专家，并且它证明了能够提高专家和实习生的息肉检测能力，这表明ALPHAON有很大可能性能够提供内镜辅助。
