AIMC Topic: Humans

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Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.

PloS one
Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a...

Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks.

PloS one
Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes b...

Designing a hybrid dimension reduction for improving the performance of Amharic news document classification.

PloS one
The volume of Amharic digital documents has grown rapidly in recent years. As a result, automatic document categorization is highly essential. In this paper, we present a novel dimension reduction approach for improving classification accuracy by com...

Robot-assisted Extraperitoneal Para-aortic Lymphadenectomy Is Associated with Fewer Surgical Complications: A Post Hoc Analysis of the STELLA-2 Randomized Trial.

Journal of minimally invasive gynecology
STUDY OBJECTIVE: To evaluate if extraperitoneal para-aortic lymphadenectomy (PALND) using a robot-assisted approach was associated with fewer complications than all other approaches (conventional laparoscopic transperitoneal or extraperitoneal and ro...

Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.

European radiology
OBJECTIVES: Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker t...

Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model.

European radiology
OBJECTIVES: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation...

Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning.

European radiology
OBJECTIVE: To develop a deep learning algorithm capable of evaluating subscapularis tendon (SSC) tears based on axillary lateral shoulder radiography.

Oncologic Outcomes of Intracorporeal Extracorporeal Urinary Diversion After Robot-Assisted Radical Cystectomy: A Multi-Institutional Korean Study.

Journal of endourology
We aimed to compare the oncologic outcomes of intracorporeal urinary diversion (ICUD) and extracorporeal urinary diversion (ECUD) following robot-assisted radical cystectomy (RARC) in patients diagnosed with bladder cancer. Medical records of 730 p...

Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting...

MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer.

Computational biology and chemistry
Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example...