Browsing by Author "Toumi, Hechmi"

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    Oriented fractal analysis for improved bone microarchitecture characterization
    (Elsevier, 2017) Harrar, Khaled; Jennane, Rachid; Zaouchi, Karima; Janvier, Thomas; Toumi, Hechmi; Lespessailles, Eric
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    ROI impact on the characterization of knee osteoarthritis using fractal analysis
    (2015) Janvier, Thomas; Toumi, Hechmi; Harrar, Khaled; Lespessailles, Eric; Jennane, Rachid
    This paper presents a preliminary study of the influence of the positioning of Regions Of Interest (ROI) for the characterization of bone texture on radiographs for the diagnosis of knee OsteoArthritis (OA) progression. Characterization of the bone texture is of great interest to doctors because it would improve the prognostic in the clinical routine. In general, studies mainly focus on the descriptors while neglecting the choice of ROI positioning. Using fractal descriptors, the objective of this work is to highlight the impact of the ROI for the diagnosis of knee OA by considering the couple (descriptor, ROI). This study was performed over 1054 knees from 616 subjects composed of stable and progressor patients. Achieved statistical tests demonstrated the importance of the choice of the ROI to improve the clinical diagnosis
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    Subchondral tibial bone texture of conventional X-rays predicts total knee arthroplasty
    (Nature Research, 2022) Almhdie-Imjabbar, Ahmad; Toumi, Hechmi; Harrar, Khaled; Pinti, Antonio; Lespessailles, Eric
    Lacking disease-modifying osteoarthritis drugs (DMOADs) for knee osteoarthritis (KOA), Total Knee Arthroplasty (TKA) is often considered an important clinical outcome. Thus, it is important to determine the most relevant factors that are associated with the risk of TKA. The present study aims to develop a model based on a combination of X-ray trabecular bone texture (TBT) analysis, and clinical and radiological information to predict TKA risk in patients with or at risk of developing KOA. This study involved 4382 radiographs, obtained from the OsteoArthritis Initiative (OAI) cohort. Cases were defined as patients with TKA on at least one knee prior to the 108-month follow-up time point and controls were defined as patients who had never undergone TKA. The proposed TKA-risk prediction model, combining TBT parameters and Kellgren–Lawrence (KL) grades, was performed using logistic regression. The proposed model achieved an AUC of 0.92 (95% Confidence Interval [CI] 0.90, 0.93), while the KL model achieved an AUC of 0.86 (95% CI 0.84, 0.86; p < 0.001). This study presents a new TKA prediction model with a good performance permitting the identification of at risk patient with a good sensitivy and specificity, with a 60% increase in TKA case prediction as reflected by the recall values