AIMC Topic: Humans

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Robotic surgery in gynaecology: Scientific Impact Paper No. 71 (July 2022).

BJOG : an international journal of obstetrics and gynaecology
The use of robotic-assisted keyhole surgery in gynaecology has expanded in recent years owing to technical advances. These include 3D viewing leading to improved depth perception, limitation of tremor, potential for greater precision and discriminati...

Efficacy of the Mercedes-Benz closure technique for vaginal reconstruction in female robot-assisted radical cystectomy.

Asian journal of endoscopic surgery
INTRODUCTION: Vaginal reconstruction using the posterior vaginal wall is required following radical cystectomy in women with resection of the uterus, adnexa, and anterior vaginal wall. Roll closure and clamshell closure are two widely known technique...

Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.

NMR in biomedicine
Self-supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self-supervised learning methods for physics-guided recons...

KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image.

Computer methods and programs in biomedicine
PURPOSE: The alarming increase in diseases of urinary system is a cause of concern for the populace and health experts. The traditional techniques used for the diagnosis of these diseases are inconvenient for patients, require high cost, and addition...

A Hybrid FMCDM Approach for the Evaluation and Selection of Homestays.

International journal of environmental research and public health
Due to the beautiful rural scenery, rural tourism has gradually become a popular trend as affected by urbanization in Taiwan. The purpose of this study is to develop an objective and systematic evaluation model for homestay selection in Taiwan. Speci...

Validation of a natural language processing algorithm to identify adenomas and measure adenoma detection rates across a health system: a population-level study.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Measuring adenoma detection rates (ADRs) at the population level is challenging because pathology reports are often reported in an unstructured format; further, there is significant variation in reporting methods across instituti...

Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study.

European radiology
OBJECTIVE: Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reco...

Artificial intelligence at the national eye institute.

Current opinion in ophthalmology
PURPOSE OF REVIEW: This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goa...

PDE-READ: Human-readable partial differential equation discovery using deep learning.

Neural networks : the official journal of the International Neural Network Society
PDE discovery shows promise for uncovering predictive models of complex physical systems but has difficulty when measurements are noisy and limited. We introduce a new approach for PDE discovery that uses two Rational Neural Networks and a principled...

Cardiac MRI segmentation with sparse annotations: Ensembling deep learning uncertainty and shape priors.

Medical image analysis
The performance of deep learning for cardiac magnetic resonance imaging (MRI) segmentation is oftentimes degraded when using small datasets and sparse annotations for training or adapting a pre-trained model to previously unseen datasets. Here, we de...