AIMC Topic: Retrospective Studies
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Exploring the role of artificial intelligence in Turkish orthopedic progression exams.
Acta orthopaedica et traumatologica turcica
Mar 17, 2025
OBJECTIVE: The aim of this study was to evaluate and compare the performance of the artificial intelligence (AI) models ChatGPT-3.5, ChatGPT-4, and Gemini on the Turkish Specialization Training and Development Examination (UEGS) to determine their ut...
Combining artificial intelligence assisted image segmentation and ultrasound based radiomics for the prediction of carotid plaque stability.
BMC medical imaging
Mar 17, 2025
PURPOSE: Utilizing artificial intelligence (AI) technology for the segmentation of plaques on ultrasound images to evaluate the stability of carotid artery plaques and analyze its diagnostic accuracy in differentiating vulnerable plaques from stable ...
LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 17, 2025
BACKGROUND: Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interp...
A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections.
BMC pulmonary medicine
Mar 17, 2025
BACKGROUND: In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learni...
Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients.
Scientific reports
Mar 17, 2025
Cervical spinal cord injury (cSCI) poses a significant challenge due to the unpredictable nature of recovery, which ranges from mild paralysis to severe long-term disability. Accurate prognostic models are crucial for guiding treatment and rehabilita...
Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers.
BMJ health & care informatics
Mar 17, 2025
BACKGROUND: Chronic kidney disease (CKD) is a global health concern characterised by irreversible renal damage that is often assessed using invasive renal biopsy. Accurate evaluation of interstitial fibrosis and tubular atrophy (IFTA) is crucial for ...
Automated classification of panoramic radiographs with inflammatory periapical lesions using a CNN-LSTM architecture.
Journal of dentistry
Mar 16, 2025
OBJECTIVES: Considering Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network approaches have shown promising image classification performance, the aim of this study was to compare the performance of novel Convolutional Neural ...
Automated liver magnetic resonance elastography quality control and liver stiffness measurement using deep learning.
Abdominal radiology (New York)
Mar 15, 2025
PURPOSE: Magnetic resonance elastography (MRE) measures liver stiffness for fibrosis staging, but its utility can be hindered by quality control (QC) challenges and measurement variability. The objective of the study was to fully automate liver MRE Q...
Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.
Journal for immunotherapy of cancer
Mar 15, 2025
BACKGROUND: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develo...
A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair.
International journal of cardiology
Mar 14, 2025
OBJECTIVE: This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to predict the prognosis of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) patien...
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