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Rhumatologue

Docteur SHUING KONG

📍 Toulon (83)HospitalierRPPS 10100912798
📊 Reconnaissance scientifique : 3/100📝 6 articles publiés📚 HAL (1)🏆 1 DU/DIU

Diplômes

🎓 DES & spécialité ordinale

  • DES Rhumatologie
  • Rhumatologie (SM)

🏅 DU / DIU

  • DIU Pathologies osseuses médicales

🎓 Diplômes

  • DE Docteur en médecine

Source : Annuaire Santé ANS (FHIR Practitioner.qualification) · Mises à jour quotidiennes.

🏆 DU / DIU rhumato reconnus

Activité de recherche & publications

Source : bases de données publiques (OpenAlex, PubMed).

h-index

3

h articles cités ≥ h fois chacun. Un h de 3 = 3 publications avec 3+ citations.

Citations

106

Publications

6

i10-index

3

Thématiques principales

  • Rheumatoid Arthritis Research and Therapies ×4
  • Osteoarthritis Treatment and Mechanisms ×2
  • Spondyloarthritis Studies and Treatments ×2
  • Bone Metabolism and Diseases ×2
  • Bone health and osteoporosis research ×1
Voir sur OpenAlex ↗

Source : OpenAlex (CC0, OurResearch). Indicateurs académiques agrégés sur 250 M+ d'œuvres.

Bibliographie

Source : HAL — archive ouverte CCSD/CNRS (couvre articles, chapitres EMC, communications congrès, thèses).

Lieu de consultation

  • CHITS CH SAINTE MUSSE

    54 Rue HENRI SAINTE CLAIRE DEVILLE, 83056 Toulon

    ☎ 0494145000Hospitalier

Tarifs & secteur de conventionnement

Secteur de conventionnement non disponible (médecin hospitalier ou non présent dans l'Annuaire santé CNAM des libéraux conventionnés).

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Lien Doctolib = recherche Google site:doctolib.fr (le 1er résultat est presque toujours le profil correct s'il existe).

Top publications · les plus citées

  • 1
    Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm

    Endocrinology and metabolism (Seoul, Korea) · 2022

    📚 32 citations🎯 RCR 4.05Top 11% NIH🔓 Open Access📄 PDF gratuit ↗
    Lire l'abstract Crossref ↓

    Background: Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data.Methods: This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models.Results: Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women.Conclusion: DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.

  • 2
    Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation

    Journal of medical Internet research · 2023

    📚 30 citations🎯 RCR 4.77Top 8% NIH🔓 Open Access📄 PDF gratuit ↗
    Lire l'abstract Crossref ↓

    Background Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation. Objective The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical features. Clinical interpretation with individual explanations of feature contributions is provided using an explainable artificial intelligence (XAI) technique. Methods We used two separate data sets: the National Health and Nutrition Examination Survey data sets from the United States (NHANES) and South Korea (KNHANES) with 8274 and 8680 respondents, respectively. The study population was classified according to the T-score of bone mineral density at the femoral neck or total femur. A DL model for osteoporosis diagnosis was trained on the data sets and significant risk factors were investigated with local interpretable model-agnostic explanations (LIME). The performance of the DL model was compared with that of ML models and conventional clinical tools. Additionally, contribution ranking of risk factors and individualized explanation of feature contribution were examined. Results Our DL model showed area under the curve (AUC) values of 0.851 (95% CI 0.844-0.858) and 0.922 (95% CI 0.916-0.928) for the femoral neck and total femur bone mineral density, respectively, using the NHANES data set. The corresponding AUC values for the KNHANES data set were 0.827 (95% CI 0.821-0.833) and 0.912 (95% CI 0.898-0.927), respectively. Through the LIME method, significant features were induced, and each feature’s integrated contribution and interpretation for individual risk were determined. Conclusions The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.

  • 3
    Application of the Trabecular Bone Score in Clinical Practice

    Journal of bone metabolism · 2021

    📚 27 citations🎯 RCR 2.70Top 19% NIH🔓 Open Access📄 PDF gratuit ↗

Publications scientifiques (50) — classées par pathologie

Source PubMed · Recherche par auteur (homonymes possibles, vérifier l'affiliation).

Transversal31

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Épidémiologie & registres6

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IA en rhumatologie6

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Ostéoporose3

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Revue générale3

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Revue / méta-analyse2

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Lupus1

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Pédiatrie1

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Vraie vie / RWE1

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