AIMC Topic: Diagnosis, Differential
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Rapid and Differential Diagnosis of Sepsis Stages Using an Advanced 3D Plasmonic Bimetallic Alloy Nanoarchitecture-Based SERS Biosensor Combined with Machine Learning for Multiple Analyte Identification.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 17, 2025
Rapid and accurate differential diagnosis of infections, sepsis, and septic shock is essential for preventing unnecessary antibiotic overuse and improving the chance of patient survival. To address this, a 3D gold nanogranule decorated gold-silver al...
Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from .
Frontiers in public health
Feb 17, 2025
OBJECTIVE: To develop and evaluate the effectiveness of multimodal machine learning approach for the differentiation of NTM from MTB.
Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence: Results of Hand-Crafted, Deep Radiomics Features and Radiologists' Assessment in Multicenter Cohorts.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Feb 15, 2025
OBJECTIVE: 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 compare...
Integration of radiomic and deep features to reliably differentiate benign renal lesions from renal cell carcinoma.
European journal of radiology
Feb 7, 2025
PURPOSE: Accurate differentiation of benign renal lesions from renal cell carcinoma (RCC) is crucial for optimized management, particularly for small renal lesions (≤4 cm in diameter). This study aimed to integrate clinical data, radiomic features, a...
Radiomics
Humans
Male
Radiographic Image Interpretation, Computer-Assisted
Middle Aged
Retrospective Studies
Aged
Kidney Neoplasms
Deep Learning
Tomography, X-Ray Computed
Aged, 80 and over
Carcinoma, Renal Cell
Diagnosis, Differential
Female
Adult
Reproducibility of Results
Sensitivity and Specificity
ThyroNet-X4 genesis: an advanced deep learning model for auxiliary diagnosis of thyroid nodules' malignancy.
Scientific reports
Feb 4, 2025
Thyroid nodules are a common endocrine condition, and accurate differentiation between benign and malignant nodules is essential for making appropriate treatment decisions. Traditional ultrasound-based diagnoses often depend on the expertise of physi...
Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II).
Journal of the neurological sciences
Jan 31, 2025
Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and generate predictions based on novel data. We conducted deep learning utilizing an NN to differentiat...
Feasibility of using Gramian angular field for preprocessing MR spectroscopy data in AI classification tasks: Differentiating glioblastoma from lymphoma.
European journal of radiology
Jan 29, 2025
OBJECTIVES: To convert 1D spectra into 2D images using the Gramian angular field, to be used as input for convolutional neural network for classification tasks such as glioblastoma versus lymphoma.
Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.
Frontiers in immunology
Jan 29, 2025
PURPOSE: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based mac...
Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study.
World journal of surgical oncology
Jan 28, 2025
BACKGROUND: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperativ...
An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.
Scientific reports
Jan 27, 2025
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrosp...
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