骨盆倾斜
医学
矢状面
置信区间
射线照相术
可靠性(半导体)
神经外科
标准差
核医学
后凸
脊柱融合术
标准误差
算法
口腔正畸科
物理疗法
外科
放射科
统计
数学
功率(物理)
物理
量子力学
内科学
作者
Priyanka Grover,Jakob Siebenwirth,Christina Caspari,Steffen Drange,Marcel Dreischarf,Jean-Charles Le Huec,Michael Putzier,Jörg Franke
标识
DOI:10.1007/s00586-022-07309-5
摘要
Sagittal balance (SB) plays an important role in the surgical treatment of spinal disorders. The aim of this research study is to provide a detailed evaluation of a new, fully automated algorithm based on artificial intelligence (AI) for the determination of SB parameters on a large number of patients with and without instrumentation.Pre- and postoperative sagittal full body radiographs of 170 patients were measured by two human raters, twice by one rater and by the AI algorithm which determined: pelvic incidence, pelvic tilt, sacral slope, L1-S1 lordosis, T4-T12 thoracic kyphosis (TK) and the spino-sacral angle (SSA). To evaluate the agreement between human raters and AI, the mean error (95% confidence interval (CI)), standard deviation and an intra- and inter-rater reliability was conducted using intra-class correlation (ICC) coefficients.ICC values for the assessment of the intra- (range: 0.88-0.97) and inter-rater (0.86-0.97) reliability of human raters are excellent. The algorithm is able to determine all parameters in 95% of all pre- and in 91% of all postoperative images with excellent ICC values (PreOP-range: 0.83-0.91, PostOP: 0.72-0.89). Mean errors are smallest for the SSA (PreOP: -0.1° (95%-CI: -0.9°-0.6°); PostOP: -0.5° (-1.4°-0.4°)) and largest for TK (7.0° (6.1°-7.8°); 7.1° (6.1°-8.1°)).A new, fully automated algorithm that determines SB parameters has excellent reliability and agreement with human raters, particularly on preoperative full spine images. The presented solution will relieve physicians from time-consuming routine work of measuring SB parameters and allow the analysis of large databases efficiently.
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