Unit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy.
Division of Head and Neck Surgery, Stanford University, Palo Alto, California, USA.
Curr Opin Otolaryngol Head Neck Surg. 2021 Apr 1;29(2):143-148. doi: 10.1097/MOO.0000000000000697.
Machine learning (ML) algorithms have augmented human judgment in various fields of clinical medicine. However, little progress has been made in applying these tools to video-endoscopy. We reviewed the field of video-analysis (herein termed 'Videomics' for the first time) as applied to diagnostic endoscopy, assessing its preliminary findings, potential, as well as limitations, and consider future developments.
ML has been applied to diagnostic endoscopy with different aims: blind-spot detection, automatic quality control, lesion detection, classification, and characterization. The early experience in gastrointestinal endoscopy has recently been expanded to the upper aerodigestive tract, demonstrating promising results in both clinical fields. From top to bottom, multispectral imaging (such as Narrow Band Imaging) appeared to provide significant information drawn from endoscopic images.
Videomics is an emerging discipline that has the potential to significantly improve human detection and characterization of clinically significant lesions during endoscopy across medical and surgical disciplines. Research teams should focus on the standardization of data collection, identification of common targets, and optimal reporting. With such a collaborative stepwise approach, Videomics is likely to soon augment clinical endoscopy, significantly impacting cancer patient outcomes.
机器学习（ML）算法已经在临床医学的各个领域增强了人类的判断能力。然而，在将这些工具应用于视频内镜方面，几乎没有取得任何进展。我们首次综述了应用于诊断内镜的视频分析领域（以下简称“内镜影像组学”），评估了其初步发现、潜力以及局限性，并探讨了未来的发展方向。
ML 已经在诊断内镜中得到了不同的应用：盲点检测、自动质量控制、病变检测、分类和特征描述。在胃肠道内镜方面的早期经验最近已经扩展到上呼吸道，在这两个临床领域都取得了有前景的结果。从上到下，多光谱成像（如窄带成像）似乎从内镜图像中提供了重要的信息。
内镜影像组学是一个新兴的学科，有可能在医学和外科领域的内镜检查中显著提高人类对临床显著病变的检测和特征描述能力。研究团队应专注于数据采集的标准化、常见目标的识别以及最佳报告的制定。通过这种协作的逐步方法，内镜影像组学可能很快就会增强临床内镜检查，对癌症患者的治疗结果产生重大影响。
