AIMC Topic: Humans

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Robot-assisted re-do sacrohysteropexy after anterior abdominal wall hysteropexy.

International urogynecology journal
INTRODUCTION AND HYPOTHESIS: We report the case of a 49-year-old woman who came to our institution complaining of bowel constipation and bladder outlet obstruction due to the recurrence of a pelvic prolapse after anterior abdominal wall hysteropexy p...

Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data.

Methods (San Diego, Calif.)
Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as ...

A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD) are considered leading causes of life-long disability worldwide, where high rates of no response to treatment or relapse and delays in receiving a proper diagnosis ...

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.

Nature reviews. Cardiology
Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in p...

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.

NeuroImage
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different source...

Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks.

Medical image analysis
Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision have greatly benefited the pathology workflow by developing automated solutions for classification ta...

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

Medical image analysis
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is the largest ...

Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning.

Annals of biomedical engineering
In order to increase the probability of having a successful cataract post-surgery, the customisation of the haptic design of the intraocular lens (IOL) according to the characteristics of the patient is recommended. In this study, we present two pred...

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

EBioMedicine
BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's pr...

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.

Scientific reports
The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult...