AIMC Topic: Sensitivity and Specificity
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Strong Diagnostic Performance of Single Energy 256-row Multidetector Computed Tomography with Deep Learning Image Reconstruction in the Assessment of Myocardial Fibrosis.
Internal medicine (Tokyo, Japan)
Feb 12, 2024
Objective Although magnetic resonance imaging (MRI) is the gold standard for evaluating abnormal myocardial fibrosis and extracellular volume (ECV) of the left ventricular myocardium (LVM), a similar evaluation has recently become possible using comp...
A high-speed microscopy system based on deep learning to detect yeast-like fungi cells in blood.
Bioanalysis
Feb 9, 2024
Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-sp...
Classification of H. pylori Infection from Histopathological Images Using Deep Learning.
Journal of imaging informatics in medicine
Feb 8, 2024
Helicobacter pylori (H. pylori) is a widespread pathogenic bacterium, impacting over 4 billion individuals globally. It is primarily linked to gastric diseases, including gastritis, peptic ulcers, and cancer. The current histopathological method for ...
Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.
Journal of imaging informatics in medicine
Feb 8, 2024
In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explai...
Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program.
Journal of veterinary dentistry
Feb 6, 2024
There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical decisions. A commercially available, AI-based software program (AISP) for detecting common radiograph...
Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning.
Journal of shoulder and elbow surgery
Feb 3, 2024
BACKGROUND: The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recogni...
Development and multicenter validation of deep convolutional neural network-based detection of colorectal cancer on abdominal CT.
European radiology
Feb 1, 2024
OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network.
Female
Reproducibility of Results
Aged
Aged, 80 and over
Humans
Neural Networks, Computer
Middle Aged
Adult
Retrospective Studies
Adenocarcinoma
Sensitivity and Specificity
Tomography, X-Ray Computed
Radiographic Image Interpretation, Computer-Assisted
Colorectal Neoplasms
Radiography, Abdominal
Male
Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions' Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection.
The Journal of investigative dermatology
Jan 29, 2024
Melanoma is still a major health problem worldwide. Early diagnosis is the first step toward reducing its mortality, but it remains a challenge even for experienced dermatologists. Although computer-aided systems have been developed to help diagnosis...
Psychometric evaluation of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) among adults with Long COVID, ME/CFS, and healthy controls: A machine learning approach.
Journal of health psychology
Jan 28, 2024
Long COVID shares a number of clinical features with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), including post-exertional malaise, severe fatigue, and neurocognitive deficits. Utilizing validated assessment tools that accurately and...
Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach.
Japanese journal of radiology
Jan 27, 2024
PURPOSE: To develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model's diagnostic performance.
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