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Rhumatologue

Docteur CHRISTIAN BEST

RPPS 10000729714

Diplômes

🎓 DES & spécialité ordinale

  • Rhumatologie (SM)

📚 CES (Certificat d'Études Spéciales)

  • CES Rhumatologie

🎓 Diplômes

  • DE Docteur en médecine

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

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Top publications · les plus citées

  • 1
    Detection of Brain Tau Pathology in Down Syndrome Using Plasma Biomarkers

    JAMA neurology · 2022

    📚 49 citations🎯 RCR 4.44Top 9% NIH🔓 Open Access📄 PDF gratuit ↗
  • 2
    Strategies to integrate oral health into primary care: a systematic review

    BMJ open · 2023

    📚 31 citations🎯 RCR 9.36Top 3% NIH🔓 Open Access📄 PDF gratuit ↗
    Lire l'abstract Crossref ↓

    ObjectivesIntegration of oral health into primary care has been proposed as a primary healthcare approach for efficient and sustainable delivery of oral health services, and the effective management of oral diseases. This paper aimed to synthesise evidence on the effectiveness of strategies to integrate oral health into primary care.DesignSystematic review.Data sourcesMEDLINE, CINAHL, Embase, Scopus, ProQuest, Cochrane and Google Scholar were searched without date limits until the third week of June 2022. Reference lists of eligible studies were also searched. Experts in the field and existing professional networks were consulted.Eligibility criteriaOnly studies that evaluated integration strategies were included in the review. Eligibility was restricted to English language studies published in academic peer-reviewed journals.Data extraction and synthesisTwo reviewers independently extracted data and performed the risk of bias assessments. A narrative synthesis approach was used to report review findings. Heterogeneity among included studies precluded a meta-analysis.ResultsThe search identified 8731 unique articles, of which 49 were included in the review. Majority of the studies explored provision of oral healthcare by primary care professionals in primary care settings, where integration was primarily via training/education and/or policy changes. Most studies reported results favouring the integration strategy, such as improvements in referral pathways, documentation processes, operating efficiencies, number of available health staff, number of visits to non-dental primary care professionals for oral health issues, proportion of children receiving fluoride varnish applications/other preventive treatment, proportion of visits to an oral health professional and dental caries estimates.ConclusionThe findings from this review demonstrate that the majority of identified strategies were associated with improved outcomes and can be used to inform decision-making on strategy selection. However, more research and evaluation are required to identify best practice models of service integration.PROSPERO registration numberCRD42020203111.

  • 3
    Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis

    PloS one · 2022

    📚 25 citations🎯 RCR 4.12Top 10% NIH🔓 Open Access
    Lire l'abstract Crossref ↓

    A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.

Publications scientifiques (50) — classées par pathologie

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

Transversal26

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Pédiatrie7

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Revue générale6

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Essai clinique4

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Revue / méta-analyse3

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Case report / série2

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IA en rhumatologie2

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Recommandations2

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Santé mentale / fatigue2

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Épidémiologie & registres1

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Génétique1

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Pharmacovigilance1

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Vraie vie / RWE1

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