| 李梅芳,袁才兴,周志敏,严坤龙,林永平,李志芳.基于人工智能质控系统改善胸部CT图像质量[J].中国医学影像技术,2024,40(2):285~289 |
| 基于人工智能质控系统改善胸部CT图像质量 |
| Quality control system based on artificial intelligence for improving imaging quality of chest CT |
| 投稿时间：2023-07-29 修订日期：2024-01-13 |
| DOI：10.13929/j.issn.1003-3289.2024.02.027 |
| 中文关键词: 神经网络,计算机 人工智能 质量控制 体层摄影术,X线计算机 |
| 英文关键词:neural networks, computer artificial intelligence quality control tomography, X-ray computed |
| 基金项目:莆田市科技项目（2021S3F002）、莆田学院科研项目（2023062）。 |
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| 中文摘要: |
| 目的 观察基于人工智能（AI）质控系统用于改善胸部CT图像质量的价值。方法 回顾性收集415例患者共1 726幅CT图像，将1 414幅用于卷积神经网络（CNN）训练、312幅用于验证；计算基于AI质控系统行胸部CT扫描的精确率（Precision）、召回率（Recall）、F1分数（F1-Score）、平均精度均值（mAP）及交并比（IOU）。前瞻性纳入21例因胸部CT图像质量不合格而拟重检患者，基于AI质控系统行胸部CT，对比2次检查结果差异。结果 基于AI质控系统行胸部CT的Precision、Recall、F1-Score、mAP及IOU均较佳。基于AI质控系统重检CT正确诊断21例。其中，首次CT误诊19例，2次检查所示肺结节面积、体积和显示质量无明显差别，而显示结节形态、边界、棘状突起、空泡征、充气支气管征、增粗扭曲血管等差异较大；漏诊、准确诊断各1例。结论 基于AI质控系统有助于改善胸部CT图像质量、提高诊断效能。 |
| 英文摘要: |
| Objective To observe the value of quality control system based on artificial intelligence (AI) for improving imaging quality of chest CT. Methods Totally 1 726 CT images obtained from 415 patients were retrospectively collected, among which 1 414 images were used for convolutional neural network (CNN) training and the rest 312 images were used for validation. Precision, Recall, F1-Score, mean average precision (mAP) and intersection over union (IOU) of quality control system based on AI for chest CT scanning were calculated. Meanwhile, 21 patients with unsatisfactory chest CT who would undergo re-examination were prospectively enrolled, and chest CT scanning with quality control system based on AI were performed. The results of 2 examinations were compared. Results Precision, Recall, F1-Score, mAP and IOU of quality control system based on AI for chest CT were all good. All 21 cases were diagnosed correctly with re-examination CT based on quality control system. Among 21 cases, the first CT misdiagnosed 19 cases, the displaying of the area, volume and display quality of pulmonary nodules were not significantly different, but the morphology, boundaries, spiny protrusions, vacuolar signs, inflatable bronchial signs of nodules as well as the thickened and twisted blood vessels were obviously different between 2 times examination. The first CT missed 1 case while correctly diagnosed 1 case. Conclusion The quality control system based on AI was helpful for improving imaging quality of chest CT and increasing diagnostic efficacy. |
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