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Profil bibliographique

Ahmed Rasim BAYRAMOĞLU

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

8Publications signalées
3Citations signalées
6Affiliations récentes

Les institutions déclarées

Les domaines associés

Glioma Diagnosis and TreatmentArtificial Intelligence in Healthcare and EducationMeningioma and schwannoma managementTopic ModelingMeta-analysis and systematic reviews

Les publications récentes

Accès ouvert 2026 article OpenAlex

Multicenter machine learning model using clinical and radiomic features for prediction of postoperative residual status in meningioma

Nafiye Sanlier, Murat Yuce, Umid Sulaimanov, Gular Ismayilova et autres

OBJECTIVE: Gross-total resection is the primary surgical objective in meningioma management; however, predicting resectability preoperatively remains challenging, particularly for meningiomas located in the skull base that exhibit complex anatomical relationships. There is a lack of validated reproducible models that integrate known anatomical …

us, kh, tr, ag (code pays fourni par la source)

0 citations Neurosurgical FOCUS
Accès ouvert 2026 article OpenAlex

How Often Do Large Language Models Agree with Each Other—And with the Truth? A Consensus- and Complexity-Stratified Analysis of Data Extraction for Neuroimaging AI

Nafiye Şanlıer, Umid Sulaimanov, Ariorad Moniri, Behman Demir et autres

Background: The reliable integration of large language models (LLMs) into neuroimaging data extraction workflows remains unresolved. Prior benchmarking shows that exact-match accuracy underestimates LLM extraction performance, but whether inter-model consensus and variable complexity can guide automation remains unclear. We evaluated whether inter-model …

us, tr, kh, ag, gb (code pays fourni par la source)

0 citations Journal of Clinical Medicine
Accès ouvert 2026 article OpenAlex

Open brain biopsy for nonneoplastic undiagnosed neurological conditions: diagnostic yield, clinical impact, and contemporary role

Garret P. Greeneway, Ufuk Erginoğlu, Umid Sulaimanov, Darius Ansari et autres

BACKGROUND: Open cranial biopsy is reserved for patients with progressive nonneoplastic neurological conditions of unknown etiology who have failed exhaustive noninvasive evaluation, raising questions regarding its diagnostic yield, clinical utility, and risk profile. AIMS: To characterize the diagnostic yield, treatment impact, and …

us (code pays fourni par la source)

0 citations Irish Journal of Medical Science (1971 -)
Accès ouvert 2026 review OpenAlex

Anterior Midline Skull Base Meningiomas: A Systematic Review of Resection Rates, Functional Outcomes, and Perioperative Complications Following Contemporary Endoscopic Endonasal Versus Transcranial Approaches

Umid Sulaimanov, Irem Uslu, Omar Alomari, Y.S. Serikkanov et autres

Objectives: Anterior midline skull base meningiomas, such as olfactory groove meningiomas (OGMs), planum sphenoidale meningiomas, and tuberculum sellae meningiomas pose significant surgical challenges due to their proximity to neurovascular and olfactory structures. The widespread use of the vascularized nasoseptal flap (NSF) has …

us, tr, ag (code pays fourni par la source)

0 citations Journal of Clinical Medicine
Accès ouvert 2026 article OpenAlex

Evaluating Large Language Models for Automated Evidence Synthesis in Neuroimaging AI: A Multi-Model Benchmark

Umid Sulaimanov, Nafiye Sanlier, Ariorad Moniri, Behman Demir et autres

Background: Data extraction for systematic reviews is highly resource-intensive. This study evaluated four frontier large language models (LLMs) on complex structured metadata extraction from specialized neuroimaging artificial intelligence (AI) literature to determine their performance in automated evidence synthesis. Methods: We compared Google …

us, tr, gb (code pays fourni par la source)

1 citation Journal of Clinical Medicine
Accès ouvert 2026 article OpenAlex

Are AI Neuroimaging Models Ready for Clinical Use? A Systematic Methodological Review

Umid Sulaimanov, Nafiye Sanlier, Ariorad Moniri, Behman Demir et autres

Background/Objectives: Artificial intelligence (AI) has rapidly expanded across medical imaging with proposed applications in diagnosis, prognostication, and surgical planning. Concerns remain regarding methodological robustness and clinical readiness for many published models. This systematic review aimed to conduct a methodological audit of AI …

us, tr (code pays fourni par la source)

2 citations Journal of Clinical Medicine

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