Song Dahye, Kim Taewan, Lee Yeonjoon, Kim Jaeyoung
Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea.
Department of Dermatology and Skin Sciences, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
J Clin Med. 2023 Sep 7;12(18):5831. doi: 10.3390/jcm12185831.
Otolaryngological diagnoses, such as otitis media, are traditionally performed using endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians. The integration of objective tools, like artificial intelligence (AI), could potentially improve the diagnostic process by minimizing the influence of subjective biases and variability. We systematically reviewed the AI techniques using medical imaging in otolaryngology. Relevant studies related to AI-assisted otitis media diagnosis were extracted from five databases: Google Scholar, PubMed, Medline, Embase, and IEEE Xplore, without date restrictions. Publications that did not relate to AI and otitis media diagnosis or did not utilize medical imaging were excluded. Of the 32identified studies, 26 used tympanic membrane images for classification, achieving an average diagnosis accuracy of 86% (range: 48.7-99.16%). Another three studies employed both segmentation and classification techniques, reporting an average diagnosis accuracy of 90.8% (range: 88.06-93.9%). These findings suggest that AI technologies hold promise for improving otitis media diagnosis, offering benefits for telemedicine and primary care settings due to their high diagnostic accuracy. However, to ensure patient safety and optimal outcomes, further improvements in diagnostic performance are necessary.
传统上，诸如中耳炎等耳鼻喉科诊断是通过内窥镜检查进行的，而在这种检查中，诊断准确性可能具有主观性，并且不同临床医生之间存在差异。像人工智能（AI）这样的客观工具的整合，有可能通过最小化主观偏差和变异性的影响来改善诊断过程。我们系统地回顾了耳鼻喉科中使用医学成像的人工智能技术。与人工智能辅助中耳炎诊断相关的研究是从五个数据库中提取的：谷歌学术、PubMed、Medline、Embase和IEEE Xplore，没有日期限制。与人工智能和中耳炎诊断无关或未使用医学成像的出版物被排除。在32项已识别的研究中，26项使用鼓膜图像进行分类，平均诊断准确率达到86%（范围：48.7 - 99.16%）。另外三项研究采用了分割和分类技术，报告的平均诊断准确率为90.8%（范围：88.06 - 93.9%）。这些发现表明，人工智能技术有望改善中耳炎诊断，由于其高诊断准确率，可为远程医疗和初级保健环境带来益处。然而，为确保患者安全和最佳治疗效果，诊断性能仍需进一步改进。
