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Predicting ventilator-associated pneumonia with machine learning.

Medicine
Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived ...

[Deep learning based lesion detection from anterior segment optical coherence tomography images and its application in the diagnosis of keratoconus].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
To developed an image analysis system of anterior segment optical coherence tomography (AS-OCT) examination results based on deep learning technology, and to evaluate its effect in identifying various types of corneal pathologies and quantified indi...

Accelerated Aging of the Amygdala in Alcohol Use Disorders: Relevance to the Dark Side of Addiction.

Cerebral cortex (New York, N.Y. : 1991)
Here we assessed changes in subcortical volumes in alcohol use disorder (AUD). A simple morphometry-based classifier (MC) was developed to identify subcortical volumes that distinguished 32 healthy controls (HCs) from 33 AUD patients, who were scanne...

Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.

Nucleic acids research
Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene...

[Clinical and morphological assessment of the results of a standard robot-assisted nerve-sparing radical prostatectomy and with the use of Retzius-sparing technique].

Urologiia (Moscow, Russia : 1999)
OBJECTIVE: To compare the perioperative, functional, clinical and morphological results of a standard robot-assisted nerve-sparing radical prostatectomy and with the use of the Retzius-sparing technique.

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images.

Journal for immunotherapy of cancer
BACKGROUND: Currently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) experience a durable clinical benefit (DCB). According to NCCN guidelines, Programmed death-ligand 1 (PD-L1) e...

[Robot-assisted nephroureterectomy requiring no robot redocking or patient repositioning: experience from a single center with 62 cases].

Zhonghua wai ke za zhi [Chinese journal of surgery]
To examine a new technique of robot-assisted nephroureterectomy without robot reldocking or patient repositioning. Patients diagnosed as upper tract urothelial carcinoma treated with this modality between November 2015 and January 2019 at Departmen...

Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: This study aimed to test the accuracy of a new automatic deep learning-based approach on the basis of convolutional neural networks (CNN) for fully automatic segmentation of the sinonasal cavity and the pharyngeal airway from cone-beam ...

Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study.

The Lancet. Digital health
BACKGROUND: The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heteroge...

Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

The journal of trauma and acute care surgery
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome ...