ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, NHC Key Laboratory of Hearing Medicine (Fudan University), Shanghai, China.
Laryngoscope. 2023 Feb;133(2):327-335. doi: 10.1002/lary.30154. Epub 2022 May 16.
To design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings.
First, multimodal MRI sequences were collected from 266 patients with parotid neoplasms, and an artificial intelligence (AI)-based deep learning model was designed from scratch, combining the image classification network of Resnet and the Transformer network of Natural language processing. Second, the effectiveness of the deep learning model was improved through the multi-modality fusion of MRI sequences, and the fusion strategy of various MRI sequences was optimized. In addition, we compared the effectiveness of the model in the parotid neoplasm classification with experienced radiologists.
The deep learning model delivered reliable outcomes in differentiating benign and malignant parotid neoplasms. The model, which was trained by the fusion of T2-weighted, postcontrast T1-weighted, and diffusion-weighted imaging (b = 1000 s/mm ), produced the best result, with an accuracy score of 0.85, an area under the receiver operator characteristic (ROC) curve of 0.96, a sensitivity score of 0.90, and a specificity score of 0.84. In addition, the multi-modal paradigm exhibited reliable outcomes in diagnosing the pleomorphic adenoma and the Warthin tumor, but not in the identification of the basal cell adenoma.
An accurate and efficient AI based classification model was produced to classify parotid neoplasms, resulting from the fusion of multimodal MRI sequences. The effectiveness certainly outperformed the model with single MRI images or single MRI sequences as input, and potentially, experienced radiologists.
3 Laryngoscope, 133:327-335, 2023.
设计一种基于多模态磁共振成像（MRI）序列的深度学习模型，用于自动腮腺肿瘤分类，并改善临床环境中的诊断决策。
首先，从 266 名腮腺肿瘤患者中收集多模态 MRI 序列，并从头开始设计基于人工智能（AI）的深度学习模型，结合 Resnet 的图像分类网络和自然语言处理的 Transformer 网络。其次，通过 MRI 序列的多模态融合来提高深度学习模型的有效性，并优化各种 MRI 序列的融合策略。此外，我们将模型在腮腺肿瘤分类中的有效性与有经验的放射科医生进行了比较。
深度学习模型在区分良性和恶性腮腺肿瘤方面提供了可靠的结果。通过融合 T2 加权、对比后 T1 加权和弥散加权成像（b=1000s/mm ）的模型产生了最佳结果，准确率为 0.85，受试者工作特征（ROC）曲线下面积为 0.96，灵敏度为 0.90，特异性为 0.84。此外，多模态范式在诊断多形性腺瘤和沃辛瘤方面表现出可靠的结果，但在基底细胞腺瘤的识别方面效果不佳。
通过融合多模态 MRI 序列，生成了一种准确有效的基于 AI 的分类模型，用于分类腮腺肿瘤。其有效性肯定优于输入单一 MRI 图像或单一 MRI 序列的模型，并且可能优于有经验的放射科医生。
3，喉镜，133：327-335，2023。
