Artificial intelligence system for detecting superficial laryngopharyngeal cancer with high efficiency of deep learning
Artificial intelligence system for detecting superficial laryngopharyngeal cancer with high efficiency of deep learning
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DOI：
10.1002/hed.26313
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发表时间：
2020-06-16
影响因子：
2.9
通讯作者：
Yano, Tomonori
中科院分区：
文献类型：
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作者：
Inaba, Atsushi;Hori, Keisuke;Yano, Tomonori
背景评价人工智能(AI)在浅表性喉咽癌(SLPC)内窥镜诊断中的作用尚无文献报道。我们提出了我们新开发的用于SLPC检测的诊断AI模型。方法使用RetinanNet进行目标检测。SLPC和从窄带成像获得的正常喉咽粘膜图像用于学习和验证数据集。每个独立的数据集包括400张SLPC和800张正常粘膜图像。诊断AI模型是分阶段构建的，并在每个学习阶段使用验证数据集进行评估。结果在验证数据集(100例SLPC)中，肿瘤的平均大小为13.2 mm，其中77/21/2例为平坦型/隆起型/凹陷型。每增加一个学习图像，灵敏度、特异度和准确度都会提高，在学习所有SLPC和正常粘膜图像后，灵敏度、特异度和准确性分别为95.5%、98.4%和97.3%。结论该人工智能模型有助于喉咽癌的早期检测。
Background There are no published reports evaluating the ability of artificial intelligence (AI) in the endoscopic diagnosis of superficial laryngopharyngeal cancer (SLPC). We presented our newly developed diagnostic AI model for SLPC detection. Methods We used RetinaNet for object detection. SLPC and normal laryngopharyngeal mucosal images obtained from narrow-band imaging were used for the learning and validation data sets. Each independent data set comprised 400 SLPC and 800 normal mucosal images. The diagnostic AI model was constructed stage-wise and evaluated at each learning stage using validation data sets. Results In the validation data sets (100 SLPC cases), the median tumor size was 13.2 mm; flat/elevated/depressed types were found in 77/21/2 cases. Sensitivity, specificity, and accuracy improved each time a learning image was added and were 95.5%, 98.4%, and 97.3%, respectively, after learning all SLPC and normal mucosal images. Conclusions The novel AI model is helpful for detection of laryngopharyngeal cancer at an early stage.