Nie Xiuli, Chen Hua, Gao Peng, Yu Hairong, Sun Meili, Yan Peng
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Statistical analyses including the Bland-Altman plot, Spearman correlation analysis, and paired t-test to ascertain the accuracy and reliability of our findings.</p><p><strong>Results: </strong>The Bland-Altman plot showed that the AI measurements had a bias of -0.28 mm and had limits of agreement ranging from - 13.78 to 13.22 mm (P = 0.497), indicating agreement with the manual measurements. However, there was no agreement between the 3D measurements and the manual measurements, with P < 0.001. The paired t-test revealed no statistically significant difference between the manual measurements and AI measurements (P = 0.497), whereas a statistically significant difference was observed between the manual measurements and 3D measurements (P < 0.001).</p><p><strong>Conclusions: </strong>The application of AI in measuring the long-diameter of tumors had significantly improved efficiency and reduced the incidence of subjective measurement errors. 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引用次数: 0
摘要
目的:本研究旨在评估使用人工智能(AI)测量肿瘤长径以评估治疗反应的可行性:我们的研究纳入了48例肺特异性靶病变患者,进行了277次测量。放射科医生记录了每次测量的靶病灶轴向成像平面长径。同时,利用人工智能软件测量轴向成像平面和三维(3D)的长径。统计分析包括 Bland-Altman 图、Spearman 相关性分析和配对 t 检验,以确定研究结果的准确性和可靠性:布兰德-阿尔特曼图显示,人工智能测量结果的偏差为-0.28毫米,一致性范围为-13.78至13.22毫米(P=0.497),表明与人工测量结果一致。然而,三维测量结果与人工测量结果不一致,P 结论:人工智能在肿瘤长径测量中的应用大大提高了效率,减少了主观测量误差的发生。这一进步有助于更方便、更准确地评估肿瘤反应。
Feasibility of an artificial intelligence system for tumor response evaluation.
Purpose: The objective of this study was to evaluate the feasibility of using Artificial Intelligence (AI) to measure the long-diameter of tumors for evaluating treatment response.
Methods: Our study included 48 patients with lung-specific target lesions and conducted 277 measurements. The radiologists recorded the long-diameter in axial imaging plane of the target lesions for each measurement. Meanwhile, AI software was utilized to measure the long-diameter in both the axial imaging plane and in three dimensions (3D). Statistical analyses including the Bland-Altman plot, Spearman correlation analysis, and paired t-test to ascertain the accuracy and reliability of our findings.
Results: The Bland-Altman plot showed that the AI measurements had a bias of -0.28 mm and had limits of agreement ranging from - 13.78 to 13.22 mm (P = 0.497), indicating agreement with the manual measurements. However, there was no agreement between the 3D measurements and the manual measurements, with P < 0.001. The paired t-test revealed no statistically significant difference between the manual measurements and AI measurements (P = 0.497), whereas a statistically significant difference was observed between the manual measurements and 3D measurements (P < 0.001).
Conclusions: The application of AI in measuring the long-diameter of tumors had significantly improved efficiency and reduced the incidence of subjective measurement errors. This advancement facilitated more convenient and accurate tumor response evaluation.
期刊介绍:
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.