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Early apoptotic responses to oncolytic virotherapy in mice can be rapidly detected by chemical-exchange-saturation-transfer magnetic resonance fingerprinting, by leveraging a neural network trained with simulated magnetic resonance fingerprints.
Adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality, and to adapt pretrained supervised networks to new domain-shifted datasets.
An automated deep-learning pipeline for chest-X-ray-image standardization, lesion visualization and disease diagnosis can identify viral pneumonia caused by COVID-19, assess its severity, and discriminate it from other types of pneumonia.
A computational method leveraging deep learning and molecular dynamics simulations enables the rapid discovery of antimicrobial peptides with low toxicity and with high potency against diverse Gram-positive and Gram-negative pathogens.
A data-efficient and interpretable deep-learning method for the multi-class classification of whole-slide images that relies only on slide-level labels is applied to the detection of lymph node metastasis and to cancer subtyping.
A deep learning model trained on raw pixel data in hundreds of thousands of echocardiographic videos for the prediction of one-year all-cause mortality outperforms clinical scores and improves predictions by cardiologists.
An open resource comprising chest computed tomography images and 130 clinical features of 1,521 patients with pneumonia, including COVID-19 pneumonia, facilitates the prediction of morbidity and mortality outcomes via deep learning.
Deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs perform comparably to or better than expert graders in associations of measurements of retinal-vessel calibre with cardiovascular risk factors.
A workflow that segments anatomical structures in slit-lamp images and that annotates pathological features in each image improves the performance of a deep-learning algorithm for the diagnosis of ophthalmic disorders.
A ‘smart’ toilet that uses pressure and motion sensors, biometric identification, urinalysis strips, a computer-vision uroflowmeter and machine learning longitudinally tracks biomarkers of health and disease in the user’s urine and stool.
Machine-learning algorithms trained with retinal fundus images, with
subject metadata or with both data types, predict haemoglobin concentration with
mean absolute errors lower than 0.75 g dl–1 and anaemia
with areas under the curve in the range of 0.74–0.89.
A deep-learning model trained to map 2D projection views of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view.
The analysis of behavioural patterns from standardized video recordings of infants with varying degrees of visual impairment enables, via deep learning, classification of the infants by visual-impairment severity and by ophthalmological condition.
An interpretable deep-learning algorithm trained on a small dataset of computed-tomography scans of the head detects acute ICH and classifies the pathology subtypes, with a performance comparable to expert radiologists.
An alert system based on machine learning and trained on surgical data from electronic medical records helps anaesthesiologists prevent hypoxaemia during surgery by providing interpretable real-time predictions.
A deep-learning algorithm can detect polyps in the colon in real time and with high sensitivity and specificity, according to validation studies with prospectively collected images and videos from colonoscopies performed in 1,138 patients.
An assay that uses machine-learning algorithms on phenotypic-biomarker data from live primary cells predicts post-surgical adverse pathology in prostate-cancer and breast cancer tissue samples from patients.
A low-cost point-of-care device that uses contrast-enhanced microholography and deep learning accurately detects aggressive lymphomas in patients referred for aspiration and biopsy of enlarged lymph nodes.
A microfluidic assay that identifies sepsis from a single droplet of diluted blood by measuring the spontaneous motility of neutrophils showed 97% sensitivity and 98% specificity in two independent patient cohorts.
A cloud-based machine-learning software that scores individual guide–target pairs and provides an overall summary score for a given guide that outperforms competing algorithms for the prediction of CRISPR–Cas9 off-target effects.
By taking advantage of stimulated Raman spectroscopy and fibre-laser technology, virtual histology images can be obtained in real time in the operating room, with diagnostic quality comparable with that achieved via conventional histopathology.
An artificial intelligence agent integrated with a cloud-based platform for multihospital collaboration performs equally as well as ophthalmologists in the diagnosis of congenital cataracts in a series of online tests and a multihospital clinical trial.
An efficient protocol for the preparation of DNA libraries for the analysis of methylation patterns in cell-free DNA in plasma enhances the sensitivity of bisulfite sequencing for the early detection of lung cancer.
A microfluidic device for assaying neutrophil motility in blood samples from sepsis patients and a machine-learning algorithm trained with the motility data enable a faster and accurate sepsis diagnosis.
Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.