AIMC Topic: Deep Learning

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Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).

Radiology. Artificial intelligence
Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encepha...

Optimize individualized energy delivery for septic patients using predictive deep learning models.

Asia Pacific journal of clinical nutrition
BACKGROUND AND OBJECTIVES: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients.

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Radiology. Artificial intelligence
Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study in...

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Radiology. Artificial intelligence
Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI withou...

Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software.

Journal of neuropathology and experimental neurology
Neuropathological diagnosis of Alzheimer disease (AD) relies on semiquantitative analysis of phosphorylated tau-positive neurofibrillary tangles (NFTs) and neuritic plaques (NPs), without consideration of lesion heterogeneity in individual cases. We ...

From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.

The British journal of radiology
The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable...

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.

Dento maxillo facial radiology
OBJECTIVES: This study evaluated the performance of the YOLOv5 deep learning model in detecting different mandibular fracture types in panoramic images.

Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs.

Studies in health technology and informatics
INTRODUCTION: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15-18 months. Therefore, it is helpful to be able to assess a patient's OS to tailor treatment more s...

Multimodal ischemic stroke recurrence prediction model based on the capsule neural network and support vector machine.

Medicine
Ischemic stroke (IS) has a high recurrence rate. Machine learning (ML) models have been developed based on single-modal biochemical tests, and imaging data have been used to predict stroke recurrence. However, the prediction accuracy of these models ...