AIMC Topic: Humans

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Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases.

Journal of healthcare engineering
This study focused on the application of deep learning algorithms in the segmentation of CT images, so as to diagnose chronic kidney diseases accurately and quantitatively. First, the residual dual-attention module (RDA module) was used for automatic...

Applying deep learning to quantify empty lacunae in histologic sections of osteonecrosis of the femoral head.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Osteonecrosis of the femoral head (ONFH) is a disease in which inadequate blood supply to the subchondral bone causes the death of cells in the bone marrow. Decalcified histology and assessment of the percentage of empty lacunae are used to quantify ...

Identifying curriculum content for a cross-specialty robotic-assisted surgery training program: a Delphi study.

Surgical endoscopy
BACKGROUND: Robotic-assisted surgery is increasing and there is a need for a structured and evidence-based curriculum to learn basic robotic competencies. Relevant training tasks, eligible trainees, realistic learning goals, and suitable training met...

Robot-assisted pancreatoduodenectomy with the da Vinci Xi: can the costs of advanced technology be offset by clinical advantages? A case-matched cost analysis versus open approach.

Surgical endoscopy
BACKGROUND: Robot-assisted pancreatoduodenectomy (RPD) has shown some advantages over open pancreatoduodenectomy (OPD) but few studies have reported a cost analysis between the two techniques. We conducted a structured cost-analysis comparing pancrea...

White matter hyperintensities segmentation using an ensemble of neural networks.

Human brain mapping
White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network ...

Knowledge-based approaches to drug discovery for rare diseases.

Drug discovery today
The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics dat...

Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.

Magnetic resonance imaging
Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method for segmenting brain tumour into its subregions has been developed. The dataset consists of multimodal...

Machine learning in the prediction of medical inpatient length of stay.

Internal medicine journal
Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previ...

Deep Learning Image Analysis of High-Throughput Toxicology Assay Images.

SLAS discovery : advancing life sciences R & D
High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-con...

Hierarchical graph representations in digital pathology.

Medical image analysis
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological ent...