Department of Otorhinolaryngology and Head and Neck Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Laryngoscope. 2023 Sep;133(9):2371-2378. doi: 10.1002/lary.30457. Epub 2022 Oct 26.
This retrospective study aimed to evaluate the performance of machine learning techniques in predicting air-bone gap after tympanoplasty compared with conventional scoring models and to identify the influential factors.
We reviewed the charts of 105 patients (114 ears) with chronic otitis media who underwent tympanoplasty. Two numerical scoring systems (middle ear risk index [MERI] and ossiculoplasty outcome parameter staging [OOPS]) and three algorithms (random forest [RF], support vector machine [SVM], and k nearest neighbor [kNN]) were created. Experimental variables included age, preoperative air-bone gap, soft-tissue density lesion in the tympanic cavity in CT, otorrhea, surgical history, ossicular bone problems in CT, tympanic perforation location, perforation type (central or marginal), grafting material, smoking history, endoscopy use, and the operator whose experience was 20 years or longer, or shorter. Binary classification, postoperative air-bone gap ≤15 or >15 dB, and multiclass classification, classification into seven categories by 10 dB, were performed, and the percentages of correct prediction were calculated. The importance of features in the RF model was calculated to identify influential factors.
The percentages of correct prediction in binary classification were 62.3%, 72.8%, 81.5%, 81.5%, and 81.5% in MERI, OOPS, RF, SVM, and kNN, respectively, and those in multiclass classification were 29.8%, 21.9%, 63.1%, 44.7%, and 50% in the same order. The RF model suggested larger preoperative air-bone gap, and older age could make the postoperative air-bone gap larger.
The machine learning techniques, especially the RF model, are promising methods for precise postoperative air-bone gap prediction.
4 Laryngoscope, 133:2371-2378, 2023.
本回顾性研究旨在评估机器学习技术在预测鼓室成形术后气骨导间隙方面的性能，并与传统评分模型进行比较，同时确定影响因素。
我们回顾了 105 例（114 耳）慢性中耳炎患者的病历，这些患者均接受了鼓室成形术。我们创建了两种数值评分系统（中耳风险指数[MERI]和听骨链重建效果参数分级[OOPS]）和三种算法（随机森林[RF]、支持向量机[SVM]和 K 最近邻[kNN]）。实验变量包括年龄、术前气骨导间隙、CT 中耳腔软组织密度病变、耳漏、手术史、CT 中听骨问题、鼓膜穿孔位置、穿孔类型（中央型或边缘型）、移植物材料、吸烟史、内镜使用情况以及手术医生的经验（20 年或以上或以下）。我们进行了二分类（术后气骨导间隙≤15 或>15dB）和多分类（按 10dB 分为七类），计算了正确预测的百分比。我们还计算了 RF 模型中特征的重要性，以确定影响因素。
在二分类中，MERI、OOPS、RF、SVM 和 kNN 的正确预测百分比分别为 62.3%、72.8%、81.5%、81.5%和 81.5%，多分类的正确预测百分比分别为 29.8%、21.9%、63.1%、44.7%和 50%。RF 模型提示术前气骨导间隙较大，年龄较大可能使术后气骨导间隙更大。
机器学习技术，特别是 RF 模型，是预测术后气骨导间隙的有前途的方法。
4 级，喉镜，133：2371-2378，2023。
