Hybrid curvature-geometrical detection of landmarks for the automatic analysis of the reduction of supracondylar fractures of the femur

José Negrillo-Cárdenas; Juan-Roberto Jiménez-Pérez; Hermenegildo Cañada-Oya; Francisco R Feito; Alberto D Delgado-Martínez

doi:10.1016/j.cmpb.2022.107177

Hybrid curvature-geometrical detection of landmarks for the automatic analysis of the reduction of supracondylar fractures of the femur

Comput Methods Programs Biomed. 2022 Nov:226:107177. doi: 10.1016/j.cmpb.2022.107177. Epub 2022 Oct 8.

Authors

José Negrillo-Cárdenas¹, Juan-Roberto Jiménez-Pérez², Hermenegildo Cañada-Oya³, Francisco R Feito², Alberto D Delgado-Martínez⁴

Affiliations

¹ Fundación I+D del Software Libre (FIDESOL), Granada, Spain. Electronic address: jnegrillo@fidesol.org.
² Graphics and Geomatics Group of Jaén, University of Jaén, Jaén, Spain.
³ Department of Orthopedic Surgery, Complejo Hospitalario de Jaén, Jaén, Spain.
⁴ Department of Orthopedic Surgery, Complejo Hospitalario de Jaén, Jaén, Spain; Department of Health Sciences, University of Jaén, Jaén, Spain.

PMID: 36242867
DOI: 10.1016/j.cmpb.2022.107177

Abstract

Background and objective: The analysis of the features of certain tissues is required by many procedures of modern medicine, allowing the development of more efficient treatments. The recognition of landmarks allows the planning of orthopedic and trauma surgical procedures, such as the design of prostheses or the treatment of fractures. Formerly, their detection has been carried out by hand, making the workflow inaccurate and tedious. In this paper we propose an automatic algorithm for the detection of landmarks of human femurs and an analysis of the quality of the reduction of supracondylar fractures.

Methods: The detection of anatomical landmarks follows a knowledge-based approach, consisting of a hybrid strategy: curvature and spatial decomposition. Prior training is unrequired. The analysis of the reduction quality is performed by a side-to-side comparison between healthy and fractured sides. The pre-clinical validation of the technique consists of a two-stage study: Initially, we tested our algorithm with 14 healthy femurs, comparing the output with ground truth values. Then, a total of 140 virtual fractures was processed to assess the validity of our analysis of the quality of reduction. A two-sample t test and correlation coefficients between metrics and the degree of reduction have been employed to determine the reliability of the algorithm.

Results: The average detection error of landmarks was maintained below 1.7 mm and 2^∘ (p< 0.01) for points and axes, respectively. Regarding the contralateral analysis, the resulting P-values reveal the possibility to determine whether a supracondylar fracture is properly reduced or not with a 95% of confidence. Furthermore, the correlation is high between the metrics and the quality of the reduction.

Conclusions: This research concludes that our technique allows to classify supracondylar fracture reductions of the femur by only analyzing the detected anatomical landmarks. A initial training set is not required as input of our algorithm.

Keywords: Computer-assisted orthopedic (CAOS); Contralateral images; Curvature analysis; Femur landmark detection; Geometrical approach.

MeSH terms

Algorithms
Femur* / diagnostic imaging
Fractures, Bone*
Humans
Knowledge Bases
Reproducibility of Results