Artificial Intelligence‐Guided Assessment of Femoral Neck Fractures in Radiographs: A Systematic Review and Multilevel Meta‐Analysis

Abstract

Artificial Intelligence (AI) is a dynamic area of computer science that is constantly expanding its practical benefits in various fields. The aim of this study was to analyze AI‐guided radiological assessment of femoral neck fractures by performing a systematic review and multilevel meta‐analysis of primary studies. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on May 21, 2024 [CRD42024541055]. The updated Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines were strictly followed. A systematic literature search of PubMed, Web of Science, Ovid (Med), and Epistemonikos databases was conducted until May 31, 2024. Critical appraisal using the Quality Assessment of Diagnostic Accuracy Studies‐2 (QUADAS‐2) tool showed that the overall quality of the included studies was moderate. In addition, publication bias was presented in funnel plots. A frequentist multilevel meta‐analysis was performed using a random effects model with inverse variance and restricted maximum likelihood heterogeneity estimator with Hartung‐Knapp adjustment. The accuracy between AI‐based and human assessment of femoral neck fractures, sensitivity and specificity with 95% confidence intervals (CIs) were calculated. Study heterogeneity was assessed using the Higgins test I 2 (low heterogeneity <25%, moderate heterogeneity: 25%–75%, and high heterogeneity >75%). Finally, 11 studies with a total of 21,163 radiographs were included for meta‐analysis. The results of the study quality assessment using the QUADAS‐2 tool are presented in Table 2. The funnel plots indicated a moderate publication bias. The AI showed excellent accuracy in assessment of femoral neck fractures (Accuracy = 0.91, 95% CI 0.83 to 0.96; I 2 = 99%; p < 0.01). The AI showed good sensitivity in assessment of femoral neck fractures (Sensitivity = 0.87, 95% CI 0.77 to 0.93; I 2 = 98%; p < 0.01). The AI showed excellent specificity in assessment of femoral neck fractures (Specificity = 0.91, 95% CI 0.77 to 0.97; I 2 = 97%; p < 0.01). AI‐guided radiological assessment of femoral neck fractures showed excellent accuracy and specificity as well as good sensitivity. The use of AI as a faster and more reliable assessment tool and as an aid in radiological routine seems justified.

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