Assessing local invasion is essential for determining stage of cN0 head and neck squamous
cell carcinoma (HNSCC). We aimed to evaluate the performance of fluorine-18 fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) in HNSCC characterization and compare it with conventional imaging…
To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand-crafted and deep radiomics features extracted from B-mode ultrasound images in differentiating benign and malignant thyroid nodules compared to senior and junior radiologists…
The optimal time for postoperative MRI of central nervous system neoplasms is 48 hours after surgery. Nevertheless, controversy exists regarding the timing of postoperative MRI in the sellar region. This study analyzed the sellar MRI findings of patients with pituitary adenoma at different times before and after surgery…
This review aims to provide an accessible overview of key evaluation metrics for physicians without AI expertise. In this review….
The quality of ultrasound images is degraded by speckle and Gaussian noises. This study aims to develop a deep-learning (DL)-based filter for ultrasound image denoising…
ChatGPT is a large language model (LLM) artificial intelligence instrument trained on massive amounts of text data extracted from the internet and/or user input. In the present article, we aim to apply the latest version of ChatGPT to the Iranian Medical Residency Examination…
The limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator-dependent nature of ultrasound imaging. In this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep-radiomics features…
Occult scaphoid fractures not visible on radiographs make early diagnosis and treatment difficult. Hence, the objective of this study was to develop a high-performing deep-learning model for the detection of apparent fracture and non-apparent occult scaphoid fractures using only plain wrist radiographs …
The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising…
The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions…
In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer…
In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for ML model development and external validation…
The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI…
In the present study, we investigate the diagnostic potential of the TME in differentiating benign and malignant lesions using image quantification and machine learning…
The present study examines the diagnostic efficacy of machine-learning algorithms in differentiating benign and malignant breast lesions using ultrasound images…
In this work, we show how a multi-resolution analysis-based CAD system can be utilized for the detection of early HTN-induced left ventricular heart muscle changes with the help of ultrasound imaging…
This paper proposes a novel CAD tool for the accurate detection of pacemaker variations using machine learning models of decision tree, SVM, random forest, and AdaBoost…
The aim of this study was to evaluate the capability of nonlinear optical response of cells to determine cellular damages during conventional and nano-technology based treatments…
As a science and engineering discipline, Artificial intelligence offers a wide variety of techniques to analyze various medical data with high accuracy and speed…
Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to …
This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases…
To improve the accuracy of the previously proposed technique of free water T1 quantification and to seek the relationship between cortical bone free water T1 and its mechanical competence…
The aim of this study was to introduce the Z-scan technique as a fast, accurate, inexpensive, and safe in vitro method to distinguish the cytotoxic effects of various treatments…
In this work, a computer-aided diagnosis (CAD) system is developed using radiomics features extracted from median nerves (MN) to diagnose CTS accurately…