Development of new diagnostic methods for lung cancer based on computer analysis of ultrasonic B-mode images

基于超声 B 型图像计算机分析开发肺癌新诊断方法

• 批准号: 20890242
• 负责人: TAGAYA Rie
• 金额: $ 0.88万
• 依托单位: St.Marianna University School of Medicine
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Young Scientists (Start-up)
• 财政年份: 2008
• 资助国家: 日本
• 起止时间: 2008 至 2009
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20890242/
• 关键词: 人工知能; 肺末梢病変; EBUS; ニューラルネットワーク; 評価; 解析; 評価・解析; ネットワーク

Differential diagnosis between benign and malignant lung lesions was made using computer analysis of B-mode images obtained by bronchoscopy for lung cancer cases. The results were compared with human lodgment for the same uses. Diagnostic accuracy of ANN using 64 units in the middle layer and 500,000/1,000,000 learning repetitions and 128 units in the middle layer and 500,000/1,000,000 learning repetitions are 71.0%. 65.4%. 74.8%. 72.0%, no significant difference was found among numbers of learning repetitions. In addition, diagnostic accuracy for the surgeon with 16, 5 and 2 years of experience are 69.2%, 56.1% and 63.6%. Diagnostic accuracy of ANN is better than that of the surgeon. Computer images analysis by ANN was regarded as promising methods for the future study.

通过对肺癌病例支气管镜检查获得的 B 型图像进行计算机分析，对良性和恶性肺部病变进行鉴别诊断。将结果与相同用途的人类安置进行比较。使用中间层 64 个单元和 500,000/1,000,000 次学习重复的 ANN 以及使用中间层 128 个单元和 500,000/1,000,000 次学习重复的 ANN 的诊断准确性为 71.0%。 65.4%。 74.8%。 72.0%，学习重复次数没有显着差异。此外，具有16年、5年和2年经验的外科医生的诊断准确率分别为69.2%、56.1%和63.6%。 ANN 的诊断准确性优于外科医生。人工神经网络的计算机图像分析被认为是未来研究的有前途的方法。