Real-World Assessment of the Efficacy of Computer-Assisted Diagnosis in Colonoscopy: A Single Institution Cohort Study in Singapore

Gabrielle E. Koh MBBS , Brittany Ng MBBS , Ronja M.B. Lagström MSc , Fung-Joon Foo FRCS , Shuen-Ern Chin MBBS , Fang-Ting Wan MBBS , Juinn Huar Kam FRCS , Baldwin Yeung PhD, FRCS , Clarence Kwan MRCP , Cesare Hassan MD, PhD , Ismail Gögenur MD, DMSc , Frederick H. Koh FRCS, PhD
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Abstract

Objective

To review the efficacy and accuracy of the GI Genius Intelligent Endoscopy Module Computer-Assisted Diagnosis (CADx) program in colonic adenoma detection and real-time polyp characterization.

Patients and Methods

Colonoscopy remains the gold standard in colonic screening and evaluation. The incorporation of artificial intelligence (AI) technology therefore allows for optimized endoscopic performance. However, validation of most CADx programs with real-world data remains scarce. This prospective cohort study was conducted within a single Singaporean institution between April 1, 2023 and December 31, 2023. Videos of all AI-enabled colonoscopies were reviewed with polyp-by-polyp analysis performed. Real-time polyp characterization predictions after sustained polyp detection were compared against final histology results to assess the accuracy of the CADx system at colonic adenoma identification.

Results

A total of 808 videos of CADx colonoscopies were reviewed. Out of the 781 polypectomies performed, 543 (69.5%) and 222 (28.4%) were adenomas and non-adenomas on final histology, respectively. Overall, GI Genius correctly characterized adenomas with 89.4% sensitivity, 61.7% specificity, a positive predictive value of 85.4%, a negative predictive value of 69.8%, and 81.5% accuracy. The negative predictive value for rectosigmoid lesions (80.3%) was notably higher than for colonic lesions (54.2%), attributed to the increased prevalence of hyperplastic rectosigmoid polyps (11.4%) vs other colonic regions (5.4%).

Conclusion

Computer-Assisted Diagnosis is therefore a promising adjunct in colonoscopy with substantial clinical implications. Accurate identification of low-risk non-adenomatous polyps encourages the adoption of “resect-and-discard” strategies. However, further calibration of AI systems is needed before the acceptance of such strategies as the new standard of care.
结肠镜检查中计算机辅助诊断功效的真实世界评估:新加坡单一机构队列研究
患者和方法 结肠镜检查仍然是结肠筛查和评估的黄金标准。因此,人工智能(AI)技术的应用可以优化内窥镜检查的效果。然而,大多数 CADx 程序在实际数据中的验证仍然很少。这项前瞻性队列研究是在 2023 年 4 月 1 日至 2023 年 12 月 31 日期间在新加坡一家机构内进行的。研究人员观看了所有人工智能结肠镜检查的视频,并逐个息肉进行分析。将持续息肉检测后的实时息肉特征预测与最终组织学结果进行比较,以评估 CADx 系统识别结肠腺瘤的准确性。在进行的 781 例息肉切除术中,最终组织学结果为腺瘤和非腺瘤的分别为 543 例(69.5%)和 222 例(28.4%)。总体而言,消化道天才能正确定性腺瘤,灵敏度为 89.4%,特异性为 61.7%,阳性预测值为 85.4%,阴性预测值为 69.8%,准确率为 81.5%。直肠乙状结肠病变的阴性预测值(80.3%)明显高于结肠病变(54.2%),这是因为增生性直肠乙状结肠息肉的发病率(11.4%)高于其他结肠区域(5.4%)。对低风险非腺瘤性息肉的准确识别有助于采用 "切除-丢弃 "策略。不过,在将这种策略作为新的治疗标准之前,还需要对人工智能系统进行进一步校准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mayo Clinic Proceedings. Digital health
Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy
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