Xu SM, Dong D, Li W, Bai T, Zhu MZ, Gu GS. Deep learning-assisted diagnosis of
femoral trochlear dysplasia based on magnetic resonance imaging measurements.
World J Clin Cases 2023;
11:1477-1487. [PMID:
36926411 PMCID:
PMC10011995 DOI:
10.12998/wjcc.v11.i7.1477]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND
Femoral trochlear dysplasia (FTD) is an important risk factor for patellar instability. Dejour classification is widely used at present and relies on standard lateral X-rays, which are not common in clinical work. Therefore, magnetic resonance imaging (MRI) has become the first choice for the diagnosis of FTD. However, manually measuring is tedious, time-consuming, and easily produces great variability.
AIM
To use artificial intelligence (AI) to assist diagnosing FTD on MRI images and to evaluate its reliability.
METHODS
We searched 464 knee MRI cases between January 2019 and December 2020, including FTD (n = 202) and normal trochlea (n = 252). This paper adopts the heatmap regression method to detect the key points network. For the final evaluation, several metrics (accuracy, sensitivity, specificity, etc.) were calculated.
RESULTS
The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96. All values were superior to junior doctors and intermediate doctors, similar to senior doctors. However, diagnostic time was much lower than that of junior doctors and intermediate doctors.
CONCLUSION
The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.
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