Department of Medicine and Molecular Science, Hiroshima University, Hiroshima, Japan.
Gastrointest Endosc. 2012 Jan;75(1):179-85. doi: 10.1016/j.gie.2011.08.051.
Narrow-band imaging (NBI) classification of colorectal lesions is clinically useful in determining treatment options for colorectal tumors. There is a learning curve, however. Accurate NBI-based diagnosis requires training and experience. In addition, objective diagnosis is necessary. Thus, we developed a computerized system to automatically classify NBI magnifying colonoscopic images.
To evaluate the utility and limitations of our automated NBI classification system.
Retrospective study.
Department of endoscopy, university hospital.
Performance of our computer-based system for classification of NBI magnifying colonoscopy images in comparison to classification by two experienced endoscopists and to histologic findings.
For the 371 colorectal lesions depicted on validation images, the computer-aided classification system yielded a detection accuracy of 97.8% (363/371); sensitivity and specificity of types B-C3 lesions for a diagnosis of neoplastic lesion were 97.8% (317/324) and 97.9% (46/47), respectively. Diagnostic concordance between the computer-aided classification system and the two experienced endoscopists was 98.7% (366/371), with no significant difference between methods.
Retrospective, single-center in this initial report.
Our new computer-aided system is reliable for predicting the histology of colorectal tumors by using NBI magnifying colonoscopy.
窄带成像（NBI）对结直肠病变的分类在确定结直肠肿瘤的治疗方案方面具有临床应用价值。然而，这需要一定的学习曲线。准确的 NBI 诊断需要培训和经验。此外，还需要客观的诊断。因此，我们开发了一种计算机系统来自动分类 NBI 放大结肠镜图像。
评估我们的自动 NBI 分类系统的实用性和局限性。
回顾性研究。
大学医院内镜科。
与两名经验丰富的内镜医生的分类和组织学发现相比，我们基于计算机的系统对 NBI 放大结肠镜图像分类的性能。
对于验证图像上显示的 371 个结直肠病变，计算机辅助分类系统的检测准确率为 97.8%（363/371）；对肿瘤性病变的 B-C3 型病变的敏感性和特异性分别为 97.8%（317/324）和 97.9%（46/47）。计算机辅助分类系统与两名经验丰富的内镜医生之间的诊断一致性为 98.7%（366/371），两种方法之间无显著差异。
这是初步报告，为回顾性、单中心研究。
我们的新型计算机辅助系统通过使用 NBI 放大结肠镜检查可靠地预测结直肠肿瘤的组织学。
