Marrero-Gonzalez Alejandro R, Diemer Tanner J, Nguyen Shaun A, Camilon Terence J M, Meenan Kirsten, O'Rourke Ashli
Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, 135 Rutledge Avenue, MSC 550, Charleston, SC, 29425, USA.
School of Medicine, University of Puerto Rico, San Juan, Puerto Rico.
Eur Arch Otorhinolaryngol. 2025 Mar;282(3):1543-1555. doi: 10.1007/s00405-024-09075-0. Epub 2024 Nov 22.
The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of AI-assisted technologies, including endoscopy, voice analysis, and histopathology, for detecting and classifying laryngeal lesions.
A systematic search was conducted in PubMed, Embase, etc. for studies utilizing voice analysis, histopathology for laryngeal lesions, or AI-assisted endoscopy. The results of diagnostic accuracy, sensitivity and specificity were synthesized by a meta-analysis.
12 studies employing AI-assisted endoscopy, 2 studies for voice analysis, and 4 studies for histopathology were included in the meta-analysis. The combined sensitivity of AI-assisted endoscopy was 91% (95% CI 87-94%) for the classification of benign from malignant lesions and 91% (95% CI 90-93%) for lesion detection. The highest accuracy pooled in detecting lesions versus healthy tissue was the AI-aided endoscopy was 94% (95% CI 92-97%).
For laryngeal lesions, AI-assisted endoscopy shows excellent diagnosis accuracy. But more sizable prospective trials are needed to confirm the practical clinical value.
本系统评价和荟萃分析的目的是评估人工智能辅助技术（包括内镜检查、语音分析和组织病理学）在检测和分类喉部病变方面的诊断准确性。
在PubMed、Embase等数据库中进行系统检索，以查找利用语音分析、喉部病变组织病理学或人工智能辅助内镜检查的研究。通过荟萃分析综合诊断准确性、敏感性和特异性的结果。
荟萃分析纳入了12项采用人工智能辅助内镜检查的研究、2项语音分析研究和4项组织病理学研究。人工智能辅助内镜检查对良性病变与恶性病变分类的综合敏感性为91%（95%可信区间87%-94%），对病变检测的综合敏感性为91%（95%可信区间90%-93%）。人工智能辅助内镜检查在检测病变与健康组织方面的最高合并准确率为94%（95%可信区间92%-97%）。
对于喉部病变，人工智能辅助内镜检查显示出优异的诊断准确性。但需要更多大规模的前瞻性试验来证实其实际临床价值。
