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Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. R...

Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study.

Bone
Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lum...

Range of Radiologist Performance in a Population-based Screening Cohort of 1 Million Digital Mammography Examinations.

Radiology
Background There is great interest in developing artificial intelligence (AI)-based computer-aided detection (CAD) systems for use in screening mammography. Comparative performance benchmarks from true screening cohorts are needed. Purpose To determi...

Screening of Parkinsonian subtle fine-motor impairment from touchscreen typing via deep learning.

Scientific reports
Fine-motor impairment (FMI) is progressively expressed in early Parkinson's Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and...

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy.

Scientific reports
For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) provides them with the optimal prognosis. However, no reliable prediction model is presently available...

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals.

Biomedical engineering online
BACKGROUND: Machine learning models were satisfactorily implemented for estimating gait events from surface electromyographic (sEMG) signals during walking. Most of them are based on inter-subject approaches for data preparation. Aim of the study is ...

Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms.

Frontiers in immunology
Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patien...

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

PloS one
This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consi...

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise.

Korean journal of radiology
OBJECTIVE: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reco...

Experimentally revealed stochastic preferences for multicomponent choice options.

Journal of experimental psychology. Animal learning and cognition
Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent c...