Deep Convolutional Neural Network–Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths. Issue 8 (August 2020)

• Record Type: Journal Article
• Authors: Germann, Christoph; Marbach, Giuseppe; Civardi, Francesco; Fucentese, Sandro F.; Fritz, Jan; Sutter, Reto; Pfirrmann, Christian W.A.; Fritz, Benjamin
• Is Part Of: Investigative radiology. Volume 55:Issue 8(2020)
• Publication Date: 2020-08
• Subjects: anterior cruciate ligament injuries -- knee injuries -- artificial intelligence -- neural networks (computer) -- magnetic resonance imaging; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000664
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objectives: The aim of this study was to clinically validate a Deep Convolutional Neural Network (DCNN) for the detection of surgically proven anterior cruciate ligament (ACL) tears in a large patient cohort and to analyze the effect of magnetic resonance examinations from different institutions, varying protocols, and field strengths. Materials and Methods: After ethics committee approval, this retrospective analysis of prospectively collected data was performed on 512 consecutive subjects, who underwent knee magnetic resonance imaging (MRI) in a total of 59 different institutions followed by arthroscopic knee surgery at our institution. The DCNN and 3 fellowship-trained full-time academic musculoskeletal radiologists evaluated the MRI examinations for full-thickness ACL tears independently. Surgical reports served as the reference standard. Statistics included diagnostic performance metrics, including sensitivity, specificity, area under the receiver operating curve ("AUC ROC"), and kappa statistics. P values less than 0.05 were considered to represent statistical significance. Results: Anterior cruciate ligament tears were present in 45.7% (234/512) and absent in 54.3% (278/512) of the subjects. The DCNN had a sensitivity of 96.1%, which was not significantly different from the readers (97.5%–97.9%; all P ≥ 0.118), but significantly lower specificity of 93.1% (readers, 99.6%–100%; all P < 0.001) and "AUC ROC" of 0.935 (readers, 0.989–0.991; all P < 0.001) for the entire cohort. Subgroup analysis showed a significantly lower sensitivity, specificity, and "AUC ROC" of the DCNN for outside MRI (92.5%, 87.1%, and 0.898, respectively) than in-house MRI (99.0%, 94.4%, and 0.967, respectively) examinations ( P = 0.026, P = 0.043, and P < 0.05, respectively). There were no significant differences in DCNN performance for 1.5-T and 3-T MRI examinations (all P ≥ 0.753, respectively). Conclusions: Deep Convolutional Neural Network performance of ACL tear diagnosis can approach performance levels similar to fellowship-trained full-time academic musculoskeletal radiologists at 1.5 T and 3 T; however, the performance may decrease with increasing MRI examination heterogeneity.