Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zurich, Switzerland.
Department of Neurosurgery, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland.
Eur Spine J. 2022 Aug;31(8):2137-2148. doi: 10.1007/s00586-022-07311-x. Epub 2022 Jul 14.
Magnetic resonance imaging (MRI) is used to detect degenerative changes of the lumbar spine. SpineNet (SN), a computer vision-based system, performs an automated analysis of degenerative features in MRI scans aiming to provide high accuracy, consistency and objectivity. This study evaluated SN's ratings compared with those of an expert radiologist.
MRIs of 882 patients (mean age, 72 ± 8.8 years) with degenerative spinal disorders from two previous trials carried out in our spine center between 2011 and 2019, were analyzed by an expert radiologist. Lumbar segments (L1/2-L5/S1) were graded for Pfirrmann Grades (PG), Spondylolisthesis (SL) and Central Canal Stenosis (CCS). SN's analysis for the equivalent parameters was generated. Agreement between methods was analyzed using kappa (κ), Spearman correlation (ρ) and Lin's concordance correlation (ρ) coefficients and class average accuracy (CAA).
4410 lumbar segments were analyzed. κ statistics showed moderate to substantial agreement in PG between the radiologist and SN depending on spinal level (range κ 0.63-0.77, all levels together 0.72; range CAA 45-68%, all levels 55%), slight to substantial agreement for SL (range κ 0.07-0.60, all levels 0.63; range CAA 47-57%, all levels 56%) and CCS (range κ 0.17-0.57, all levels 0.60; range CAA 35-41%, all levels 43%). SN tended to record more pathological features in PG than did the radiologist whereas the contrary was the case for CCS. SL showed an even distribution between methods.
SN is a robust and reliable tool with the ability to grade degenerative features such as PG, SL or CCS in lumbar MRIs with moderate to substantial agreement compared to the current gold-standard, the radiologist. It is a valuable alternative for analyzing MRIs from large cohorts for diagnostic and research purposes.
磁共振成像（MRI）用于检测腰椎的退行性变化。SpineNet（SN）是一种基于计算机视觉的系统，对 MRI 扫描中的退行性特征进行自动分析，旨在提供高精度、一致性和客观性。本研究评估了 SN 与专家放射科医生的评分比较。
对 2011 年至 2019 年期间在我们脊柱中心进行的两项先前试验中 882 名患有退行性脊柱疾病患者的 MRI 进行分析，由一名专家放射科医生进行分析。对腰椎节段（L1/2-L5/S1）进行 Pfirrmann 分级（PG）、脊柱滑脱（SL）和中央椎管狭窄（CCS）分级。生成了 SN 对等效参数的分析。使用kappa（κ）、Spearman 相关系数（ρ）和 Lin 的一致性相关系数（ρ）系数以及平均分类准确率（CAA）分析方法之间的一致性。
共分析了 4410 个腰椎节段。κ统计数据显示，放射科医生和 SN 之间在 PG 方面具有中度到高度一致性，具体取决于脊柱水平（范围κ 0.63-0.77，所有水平均为 0.72；范围 CAA 45-68%，所有水平均为 55%），在 SL（范围κ 0.07-0.60，所有水平均为 0.63；范围 CAA 47-57%，所有水平均为 56%）和 CCS（范围κ 0.17-0.57，所有水平均为 0.60；范围 CAA 35-41%，所有水平均为 43%）方面具有轻度到高度一致性。SN 倾向于在 PG 中记录更多的病理特征，而 CCS 则相反。SL 在方法之间的分布均匀。
SN 是一种强大且可靠的工具，与当前的金标准放射科医生相比，它能够以中度到高度的一致性对 PG、SL 或 CCS 等退行性特征进行分级。它是一种用于分析大型队列 MRI 以用于诊断和研究目的的有价值的替代方法。
