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RhumatologueMédecins généralistes et spécialistes👤 Libéral intégral

Mme Docteur AMELIE BRUN

📍 Montpellier (34)Libéral💶 Secteur 2RPPS 10100024909

✨ Profil synthétique

IA · 05/05/2026

Mme Docteur Amelie Brun est une rhumatologue exerçant à Montpellier. Ses publications sur PubMed couvrent divers sujets, notamment l'utilisation d'anti-TNF, les cas cliniques, l'intelligence artificielle en rhumatologie, la pédiatrie et la maladie de Sjögren. Elle a également contribué à des revues générales dans son domaine.

Expertises présumées

  • Traitement par anti-TNF
  • Rhumatologie pédiatrique
  • Maladie de Sjögren
  • Intelligence artificielle en rhumatologie
  • Étude de cas en rhumatologie
  • Thérapeutique biologique
  • Rhumatologie auto-immune

Synthèse automatique à partir des sources publiques (HAL, OpenAlex, theses.fr, ClinicalTrials.gov, FAI²R, ANS). Pas une évaluation clinique. Le médecin peut corriger via son compte.

Diplômes

🎓 DES & spécialité ordinale

  • DES Rhumatologie
  • Rhumatologie (SM)

🎓 Diplômes

  • DE Docteur en médecine

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

Localisation

Adresses géocodées via la Base Adresse Nationale (api-adresse.data.gouv.fr). Précision indicative.

Lieu de consultation

Tarifs & secteur de conventionnement

🟡 Secteur 2 — Honoraires libresSource CNAM (Annuaire santé Ameli)
💳 Carte VitaleLibéral intégral

Prendre rendez-vous & contact

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

Articles de presse (1)

Source : Google News (recherche par nom complet — homonymes possibles, vérifier le contenu).

Top publications · les plus citées

  • 1
    Predicting outcomes in rheumatoid arthritis related interstitial lung disease

    The European respiratory journal · 2019

    📚 126 citations🎯 RCR 7.58Top 4% NIH🔓 Open Access📄 PDF gratuit ↗
    Lire l'abstract Crossref ↓

    The aim of this study was to compare radiology-based prediction models in rheumatoid arthritis-related interstitial lung disease (RAILD) to identify patients with a progressive fibrosis phenotype.RAILD patients had computed tomography (CT) scans scored visually and using CALIPER and forced vital capacity (FVC) measurements. Outcomes were evaluated using three techniques, as follows. 1) Scleroderma system evaluating visual interstitial lung disease extent and FVC values; 2) Fleischner Society idiopathic pulmonary fibrosis (IPF) diagnostic guidelines applied to RAILD; and 3) CALIPER scores of vessel-related structures (VRS). Outcomes were compared to IPF patients.On univariable Cox analysis, all three staging systems strongly predicted outcome (scleroderma system hazard ratio (HR) 3.78, p=9×10−5; Fleischner system HR 1.98, p=2×10−3; and 4.4% VRS threshold HR 3.10, p=4×10−4). When the scleroderma and Fleischner systems were combined, termed the progressive fibrotic system (C-statistic 0.71), they identified a patient subset (n=36) with a progressive fibrotic phenotype and similar 4-year survival to IPF. On multivariable analysis, with adjustment for patient age, sex and smoking status, when analysed alongside the progressive fibrotic system, the VRS threshold of 4.4% independently predicted outcome (model C-statistic 0.77).The combination of two visual CT-based staging systems identified 23% of an RAILD cohort with an IPF-like progressive fibrotic phenotype. The addition of a computer-derived VRS threshold further improved outcome prediction and model fit, beyond that encompassed by RAILD measures of disease severity and extent.

  • 3
    The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography

    Diagnostics (Basel, Switzerland) · 2022

    📚 24 citations🎯 RCR 2.53Top 20% NIH🔓 Open Access📄 PDF gratuit ↗
    Lire l'abstract Crossref ↓

    Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if various countries are currently considering the implementation of LCS programs, recurring doubts and fears persist about the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) can potentially increase the efficiency of LCS. The objective of this article is to review the performances of AI algorithms developed for different tasks that make up the interpretation of LCS CT scans, and to estimate how these AI algorithms may be used as a second reader. Despite the reduction in lung cancer mortality due to LCS with LDCT, many smokers die of comorbid smoking-related diseases. The identification of CT features associated with these comorbidities could increase the value of screening with minimal impact on LCS programs. Because these smoking-related conditions are not systematically assessed in current LCS programs, AI can identify individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis and offer an opportunity for treatment and prevention.

Publications scientifiques (11) — classées par pathologie

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

Transversal6

Anti-TNF1

Case report / série1

IA en rhumatologie1

Pédiatrie1

Revue générale1

Sjögren1

Vascularites1

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