A5081306836- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- COVID-19 Clinical Research Studies
- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare and Education
- Temporomandibular Joint Disorders
- Lung Cancer Diagnosis and Treatment
- Acute Ischemic Stroke Management
- Vascular Malformations Diagnosis and Treatment
- Total Knee Arthroplasty Outcomes
- Orthodontics and Dentofacial Orthopedics
- Multiple Sclerosis Research Studies
- Systemic Lupus Erythematosus Research
- Cerebral Venous Sinus Thrombosis
- Intracranial Aneurysms: Treatment and Complications
- Knee injuries and reconstruction techniques
- COVID-19 and healthcare impacts
- Intracerebral and Subarachnoid Hemorrhage Research
- Myofascial pain diagnosis and treatment
- Advanced MRI Techniques and Applications
- Voice and Speech Disorders
- Systemic Sclerosis and Related Diseases
- Meningioma and schwannoma management
- Meta-analysis and systematic reviews
Tehran University of Medical Sciences
2021-2025
Imam Khomeini Hospital
2021-2024
University of Tehran
2024
Shiraz University of Medical Sciences
2018
Abstract Background The screening process for systematic reviews and meta-analyses in medical research is a labor-intensive time-consuming task. While machine learning deep have been applied to facilitate this process, these methods often require training data user annotation. This study aims assess the efficacy of ChatGPT, large language model based on Generative Pretrained Transformers (GPT) architecture, automating radiology without need data. Methods A prospective simulation was...
We aimed to analyze the prognostic power of CT-based radiomics models using data 14,339 COVID-19 patients.
Proposing a scoring tool to predict COVID-19 patients' outcomes based on initially assessed clinical and CT features.All patients, who were referred tertiary-university hospital respiratory triage (March 27-April 26, 2020), highly clinically suggestive for had undergone chest scan included. Those with positive rRT-PCR or suspicious patients typical pulmonary manifestations considered confirmed additional analyses. Patients, outcome, categorized into outpatient, ordinary-ward admitted,...
The intricate relationship between anterolateral ligament (ALL) and Kaplan fibers (KF) injuries in acute traumatic anterior cruciate (ACL) tears presents a diagnostic challenge. Understanding these associations is crucial for enhancing therapeutic strategies patient outcomes. To elucidate the prevalence of ALL KF among patients with ACL examine their correlations other imaging findings. Cross-sectional study; Level evidence, 3. A retrospective review magnetic resonance (MRI) was performed...
Abstract Purpose To derive and validate an effective radiomics-based model for differentiation of COVID-19 pneumonia from other lung diseases using a very large cohort patients. Methods We collected 19 private 5 public datasets, accumulating to 26,307 individual patient images (15,148 COVID-19; 9,657 with e.g. non-COVID-19 pneumonia, cancer, pulmonary embolism; 1502 normal cases). Images were automatically segmented validated deep learning (DL) the results carefully reviewed. first cropped...
Abstract To derive and validate an effective machine learning radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private five public datasets of chest CT images, accumulating 26 307 images (15 148 COVID‐19; 9657 including non‐COVID‐19 pneumonia, cancer, pulmonary embolism; 1502 normal cases). We tested 96 learning‐based models by cross‐combining four feature selectors (FSs) eight...
Abstract Background Notwithstanding the encouraging results of previous studies reporting on efficiency deep learning (DL) in COVID‐19 prognostication, clinical adoption developed methodology still needs to be improved. To overcome this limitation, we set out predict prognosis a large multi‐institutional cohort patients with using DL‐based model. Purpose This study aimed evaluate performance privacy‐preserving federated (DPFL) predicting outcomes chest CT images. Methods After applying...
Background. Providing efficient care for infectious coronavirus disease 2019 (COVID-19) patients requires an accurate and accessible tool to medically optimize medical resource allocation high-risk patients. Purpose. To assess the predictive value of on-admission chest CT characteristics estimate COVID-19 patients’ outcome survival time. Materials Methods. Using a case-control design, we included all laboratory-confirmed who were deceased, from June September 2020, in...
Background Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification abnormalities and may facilitate diagnosis assessment prognosis subjects with COVID-19. Objectives This study investigates performance an AI-aided model in predicting clinical outcomes hospitalized COVID-19 compares it radiologists’ performance. Subjects methods A total 90 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited this cross-sectional study. Quantification compromised lung...
Abstract Objective In this large multi-institutional study, we aimed to analyze the prognostic power of computed tomography (CT)-based radiomics models in COVID-19 patients. Methods CT images 14,339 patients with overall survival outcome were collected from 19 medical centers. Whole lung segmentations performed automatically using a previously validated deep learning-based model, and regions interest further evaluated modified by human observer. All resampled an isotropic voxel size,...
Repeat contrast-enhanced MR imaging exposes patients with relapsing-remitting MS to frequent administration of gadolinium-based contrast agents. We aimed investigate the potential metabolite and neurochemical alterations visible gadolinium deposition on unenhanced T1WI in dentate nucleus using MRS.This prospective study was conducted a referral university hospital from January 2020 July 2021. The inclusion criteria for case control groups were as follows: 1) case: MS, (ribbon sign), >5...
Background: The Radiologic Society of North America (RSNA) divides patients into four sections: negative, atypical, indeterminate, and typical coronavirus disease 2019 (COVID-19) pneumonia based on their computed tomography (CT) scan findings. Herein, we evaluate the frequency chest CT-scan appearances COVID-19 according to each RSNA categorical group. Methods: A total 90 with real-time reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed were enrolled in this study differences...