Hanene Boussi Rahmouni

A Digital Twin Approach for Predictive Maintenance and Anomaly Detection in Healthcare 4.0

Salma Bouazizi, Hanene Boussi Rahmouni, Abderrazak Jemai

Tunisie (code pays fourni par la source)

Comparative Experimental and Viscoelastic Modeling Study of Human Tibial Trabecular Bone Under Healthy and Osteoarthritic Conditions

Saida Benhmida, Hanene Boussi Rahmouni, Ridha Hambli, Hedi Trabelsi

Background: The development of osteoarthritis (OA), a whole-joint disorder that is increasingly recognized, depends on subchondral trabecular bone. Variations in the viscoelastic characteristics of trabecular bone have been proposed to affect load distribution and potentially lead to joint degradation. The viscoelastic response …

cz, Tunisie, fr (code pays fourni par la source)

Anti-doping Governance Framework

Ismail Dergaa, Sabri Barbaria, Chamseddine Barki, Mortadha Razzak et autres

qa, Tunisie, sa, tr, ro (code pays fourni par la source)

Anti-doping Governance Framework

Ismail Dergaa, Sabri Barbaria, Chamseddine Barki, Mortadha Razzak et autres

qa, Tunisie, sa, tr, ro (code pays fourni par la source)

A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification

Chamseddine Barki, Mariem Chouchen, Afef Sédiri, Halil İ̇brahim Ceylan et autres

Background: Cancer diagnostics is being reshaped by rapid advances in artificial intelligence, yet a persistent gap separates computational performance from clinical trust. Systematic reviews confirm that 83% of XAI studies in oncology excluded clinicians from development or evaluation, 87% lacked rigorous assessment …

cz, Tunisie, cn, gb, ro, sa, de (code pays fourni par la source)

Swin-Qwen3: a three-stage vision–language framework for automated radiology report generation with multi-agent verification

Hamida Abdaoui, Sabri Barbaria, Ahmed Al Kuwaiti, Noureddine Raouafi et autres

Background Automated chest x-ray reporting could substantially reduce the burden on radiology services worldwide; however, the implementation of current vision–language models (VLMs) in clinical workflows remains limited by factual errors, hallucinations, and inadequate clinical reliability. Bridging this implementation gap requires frameworks that …

cz, Tunisie, sa, gb (code pays fourni par la source)

Transformer-Based Clinical Annotation of Lung Cancer Reports: A Benchmark and Fine-Tuning Study on a Novel Tunisian Corpus

Ranim Yahyaoui, Ismail Dergaa, Jean Noel Nikiema, Halil İbrahim Ceylan et autres

Background: Lung cancer causes more deaths than any other malignancy worldwide, accounting for 2.2 million new cases and 1.8 million deaths in 2020. Extracting structured clinical knowledge from unstructured French-language oncology records remains methodologically unresolved in Tunisian and Francophone healthcare systems, where …

Tunisie, ca, tr, de, gb (code pays fourni par la source)

Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study

Mariem Chouchen, Chamseddine Barki, Ismail Dergaa, Halil İbrahim Ceylan et autres

Background: Nuclear medicine healthcare professionals (NMHP) sustain chronic occupational exposure to iodine-131 (I-131), conferring an elevated risk of radiation-induced solid thyroid cancer. Established radiobiological prediction tools derive risk coefficients from atomic bomb survivor data but are not configured for rapid individualized risk …

cz, Tunisie, tr, it, de (code pays fourni par la source)

Automated Clinical Annotation of Lung Cancer Reports Using Transformer-Based NLP Models: A Benchmarking and Fine-Tuning Study on a Novel Tunisian Clinical Corpus

Ranim Yahyaoui, Ismail Dergaa, Jean Noël Nikiema, Halil İbrahim Ceylan et autres

Background: Lung cancer causes more deaths than any other malignancy worldwide, accounting for 2.2 million new cases and 1.8 million deaths in 2020. Extracting structured clinical knowledge from unstructured French-language oncology records remains methodologically unresolved in Tunisian and Francophone healthcare systems, where …

Personalized Hearing Loss Care Using SNOMED CT-Aligned Ontology and Random Forest Machine Learning: A Hybrid Decision-Support Framework

Darine Kebsi, Chamseddine Barki, Ismail Dergaa, Riadh Gouider et autres

BACKGROUND: Hearing loss affects over 466 million individuals globally and is recognized as a major risk factor for Alzheimer's disease, yet treatment personalization remains limited due to the complexity and diversity of underlying causes. Current diagnostic and therapeutic approaches lack standardized methods …

Tunisie, ir, tr, fr, sa, ca (code pays fourni par la source)

MedFusionT5: Cross-Modal Attention Boosts Semantic Quality and Reduces Hallucinations in Dental AI

Hamida Abdaoui, Sabri Barbaria, Ismail Dergaa, Halil İbrahim Ceylan et autres

INTRODUCTION AND AIMS: Automated dental report generation faces significant challenges in multimodal fusion, often resulting in suboptimal semantic quality and risks of hallucination, where AI generates clinically unsupported content. Current approaches that rely on simple feature concatenation or bidirectional attention mechanisms fail …

Tunisie, tr, de, it (code pays fourni par la source)

An Ensemble Learning Approach for Predicting Acute Respiratory Distress Syndrome Within Polytraumatic Patients In Imbalanced Dataset

Nesrine Ben El Hadj Hassine, Chamseddine Barki, Hanene Boussi Rahmouni

Acute Respiratory Distress Syndrome (ARDS) is a severe, often overlooked complication in polytrauma patients, complicated by its varied symptoms and delayed diagnosis through imaging. To address this, our study developed a machine learning tool to predict ARDS early, using data from 407 …

Tunisie, gb (code pays fourni par la source)