AIMC Topic: Deep Learning

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Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma.

HPB : the official journal of the International Hepato Pancreato Biliary Association
OBJECTIVE: We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR).

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

International journal of medical informatics
PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA...

Automated Analysis of Split Kidney Function from CT Scans Using Deep Learning and Delta Radiomics.

Journal of endourology
Differential kidney function assessment is an important part of preoperative evaluation of various urological interventions. It is obtained through dedicated nuclear medical imaging and is not yet implemented through conventional Imaging. We assess...

Geometric deep learning methods and applications in 3D structure-based drug design.

Drug discovery today
3D structure-based drug design (SBDD) is considered a challenging and rational way for innovative drug discovery. Geometric deep learning is a promising approach that solves the accurate model training of 3D SBDD through building neural network model...

Automatic segmentation of femoral tumors by nnU-net.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Metastatic femoral tumors may lead to pathological fractures during daily activities. A CT-based finite element analysis of a patient's femurs was shown to assist orthopedic surgeons in making informed decisions about the risk of fracture...

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

Aging
BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aim...

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

BMC medical imaging
Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment...

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning.

BMC medical informatics and decision making
BACKGROUND: Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep lea...

A ResNet mini architecture for brain age prediction.

Scientific reports
The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuab...

AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.

Scientific reports
Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English...