Publications by authors named "Zeinab Naseri"
Purpose: The thyroid imaging reporting and data system (TIRADS) was developed as a standard global term to describe thyroid nodule risk features, aiming to address issues such as variability and low reproducibility in nodule feature detection and interpretation by different physicians. The objective of this study is to comprehensively study articles that utilize AI techniques to design and develop decision support systems for classifying thyroid nodule risk on the basis of various TIRADS guidelines from ultrasound images.
Methods: This protocol includes five steps: identification of key research questions of the review, descriptions of the systematic literature search strategies, criteria for study inclusion and exclusion, study quality measures, and the data extraction process.
Article Synopsis
- Axillary lymph node dissection (ALND) is the standard treatment for breast cancer patients with positive sentinel lymph nodes, but many might have no additional cancerous nodes, recently prompting research into better prediction tools for non-sentinel lymph node metastasis.
- The study compares the effectiveness of the MSKCC nomogram and various machine learning (ML) models, finding that the Random Forest model outperforms the nomogram in predicting NSLN metastasis among Iranian breast cancer patients.
- This research highlights the potential of AI and ML to enhance prognosis accuracy while recognizing the complications associated with unnecessary ALND procedures.
Airway and vessel characterization of bronchiectasis patterns in lung high-resolution computed tomography (HRCT) images of cystic fibrosis (CF) patients is very important to compute the score of disease severity. We propose a hybrid and evolutionary optimized threshold and model-based method for characterization of airway and vessel in lung HRCT images of CF patients. First, the initial model of airway and vessel is obtained using the enhanced threshold-based method.
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