AIMC Topic: Sensitivity and Specificity

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Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound.

Ultrasound in medicine & biology
OBJECTIVE: Early detection and monitoring of hepatic steatosis can help establish appropriate preventative measures against progression to more advanced disease. We aimed to develop a deep learning (DL) program for classification of hepatic steatosis...

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.

Clinical imaging
PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasi...

Expert level of detection of interictal discharges with a deep neural network.

Epilepsia
OBJECTIVE: Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of...

Validation of Vetscan Imagyst, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.

Parasites & vectors
BACKGROUND: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intell...

Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.

Neuroradiology
INTRODUCTION: Head and neck cancers are the seventh most common globally, with lymph node metastasis (LNM) being a critical prognostic factor, significantly reducing survival rates. Traditional imaging methods have limitations in accurately diagnosin...

Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

BMC pediatrics
BACKGROUND: Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed ...

Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.

Abdominal radiology (New York)
OBJECTIVE: To evaluate the diagnostic performance of Machine Learning (ML) and Deep Learning (DL) models for predicting preoperative Lymph Node Metastasis (LNM) in Colorectal Cancer (CRC) patients.

Introduction and accuracy assessment of Nicolab's StrokeViewer in a developing stroke thrombectomy UK service. a service development/improvement project.

Clinical radiology
AIM: The aim of this study was to evaluate the implementation of artificial intelligence (AI) software in a quaternary stroke centre as well as assess the accuracy and efficacy of StrokeViewer software in large vessel occlusion detection and its pote...

Artificial intelligence-enhanced infrared thermography as a diagnostic tool for thyroid malignancy detection.

Annals of medicine
INTRODUCTION: Thyroid nodules are common, and investigation is crucial for excluding malignancy. Increased intranodular vascularity is frequently observed in malignant tumors, which can be detected through increased skin surface temperatures using no...

PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans.

Nature communications
Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abd...