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Received:July 27, 2021 Published Online:March 20, 2022
Received:July 27, 2021 Published Online:March 20, 2022
中文摘要: 目的 探讨人工智能(AI)技术在胸部高分辨率CT(HRCT)中对肺结节诊断的效能。 方法 回顾性分析沈阳市中医院2020年1月至2021年1月经胸部HRCT扫描的130例单发肺结节患者的临床资料,所有患者均经手术病理确诊,运用AI影像分析系统与人工对比分析结节的特征,得出结节的大小、密度及危险度,对比AI和人工的检出时间、漏诊率、假阳性率和准确度。 结果 AI检测出肺结节的时间为(73.10±28.35)s,漏诊率1.54%,假阳性率23.35%,与病理诊断的符合率72.31%;人工检测出肺结节的时间为(273.26±68.62)s,漏诊率6.92%,假阳性率0,与病理诊断的符合率90.00%。AI在肺微小结节的检出时间及检出率显著优于人工,人工在肺结节假阳性率及与病理诊断的符合率优于AI(P<0.05)。 结论 虽然AI技术检测的假阳性率较高,在结节的定性上与人工还有一定差距,但可以辅助人工,在提高工作效率和降低漏诊率上效果显著。
Abstract:Objective To investigate the efficacy of artificial intelligence (AI) technology in chest high resolution CT (HRCT) in the diagnosis of pulmonary nodules. Methods The data of 130 patients with single pulmonary nodule received chest HRCT in Shenyang Hospital of Traditional Chinese Medicine from January 2020 to January 2021 were analyzed retrospectively. All patients were diagnosed by surgery and pathology. The characteristics of nodules were compared and analyzed by AI image analysis system and manual reading, including the size, density and risk of nodules. The detection time, missed diagnosis rate, false positive rate and accuracy of AI and artificial were counted. Results The time of AI detecting pulmonary nodules was (73.10±28.35) s, the missed diagnosis rate was 1.54%, the false positive rate was 23.35%, and the coincidence rate with pathological diagnosis was 72.31%. The time of manual reading of pulmonary nodules was (273.26±68.62) s, the missed diagnosis rate was 6.92%, the false positive rate was 0, and the coincidence rate with pathological diagnosis was 90.00%. The detection time and detection rate of AI in pulmonary nodules were significantly better than those of manual reading, and the false positive rate and coincidence rate with pathological diagnosis of manual reading were better than those of AI (P<0.05). Conclusion Although the false positive rate of AI technology is high, and there is still a certain gap between AI technology and manual reading in the qualitative analysis of nodules, still AI could assist manual reading, and has a significant effect in improving work efficiency and reducing missed diagnosis rate.
文章编号: 中图分类号:R445.3 文献标志码:B
基金项目:
Author Name | Affiliation |
YANG Jin-sheng*, LI Cong | *Department of Radiology, Shenyang Hospital of Traditional Chinese Medicine, Shenyang, Liaoning 110003, China |
李聪2 | 2. 沈阳市第四人民医院放射科,辽宁 沈阳 110003 |
Author Name | Affiliation |
YANG Jin-sheng*, LI Cong | *Department of Radiology, Shenyang Hospital of Traditional Chinese Medicine, Shenyang, Liaoning 110003, China |
李聪2 | 2. 沈阳市第四人民医院放射科,辽宁 沈阳 110003 |
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