Explore QAMEBI publications on AI, quantitative medical imaging, bioengineering and optical methods advancing diagnosis and biological research.
To develop and externally validate ultrasound nomograms combining BI-RADS features and quantitative morphometric characteristics, and to compare their performance with expert radiologists and large language models in biopsy recommendation and malignancy prediction for breast lesions…
We introduced an open-access multicenter lymph node ultrasound image database (LymphUs) specifically designed to advance research in LN assessment for PTC. Ultrasound imaging was performed on PTC patients at two independent clinical centers using standardized acquisition protocols…
This study utilised an open-access dataset of corneal imaging. A total of 35 eyes with a clinical diagnosis of keratoconus and 38 normal eyes were recruited. Patients underwent intraocular measurement, ocular pulse elastography, corneal tomography and topography…
To compare the performance of Doppler ultrasound (US) and US elastography with their combination in detecting delayed graft rejection. A prospective cross-sectional study of 60 consecutive adult kidney transplant recipients was done…
This study retrospectively included 326 adult female patients. The study protocol was pre-registered on the Open Science Framework (OSF) platform. Each image was annotated and evaluated by a minimum of two expert radiologists, while …
To evaluate the inter-rater reliability of the Visually AcceSAble Rembrandt Images (VASARI) feature set in glioma imaging assessment. Three board-certified radiologists independently analyzed …
This study aims to develop and internally validate a model based on imaging findings and clinical data to predict the prognosis of head and neck squamous cell carcinoma (HNSCC) patients who received radiotherapy. We retrospectively included 215 HNSCC patients who…
This retrospective study included 326 patients recruited between January 2019 and February 2021. The study protocol was pre-registered on the Open Science Framework platform. Two expert radiologists interpreted the CESM and DM findings. Pathological data are used for radiologically suspicious or malignant-appearing lesions, whereas follow-up was considered the gold standard for benign-appearing lesions and breasts without lesions…
papillary thyroid carcinoma (PTC) as the most common thyroid tumor, tends to invade adjacent organs, especially lymphatic system. This study aimed to evaluate the discrimination performance of ultrasound elastography (USE) in assessing PTC nodule for determination of cervical lymph node metastasis (CLNM)…
This retrospective analysis utilized prospectively collected data from the multicenter ACRIN 6688 observational trial. It used a dataset of 90 patients from 17 centers with confirmed breast cancer who planned to receive NAC followed by surgery as part of their treatment…
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…