AIMC Topic: Sensitivity and Specificity

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Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography.

European heart journal. Cardiovascular Imaging
AIMS: Although deep-learning algorithms have been used to compute fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA), no study has achieved 'fully automated' (i.e. free from human input) FFR calculation using deep-lear...

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

Neuro-oncology
BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a high...

On classifying sepsis heterogeneity in the ICU: insight using machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the ...

[Detection of inferior myocardial infarction based on densely connected convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry o...

Retrospective imaging studies of gastric cancer: Study protocol clinical trial (SPIRIT Compliant).

Medicine
INTRODUCTION: Peritoneal metastasis (PM) is a frequent condition in patients presenting with gastric cancer, especially in younger patients with advanced tumor stages. Computer tomography (CT) is the most common noninvasive modality for preoperative ...

Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.

Investigative radiology
OBJECTIVES: This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fractur...

SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation.

Current medical imaging
BACKGROUND: Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to...

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.

Current medical imaging
BACKGROUND: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usual...

[New Trends in Breast Imaging].

Therapeutische Umschau. Revue therapeutique
New Trends in Breast Imaging The examination of the breast, especially as a screening examination for breast cancer, has so far been carried out primarily by means of mammography and occasionally supplementary ultrasound. These check-ups have become...