AIMC Topic: Deep Learning

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Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

BMJ open
OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluat...

Imaging Manifestations and Evaluation of Postoperative Complications of Bone and Joint Infections under Deep Learning.

Journal of healthcare engineering
To explore and evaluate the imaging manifestations of postoperative complications of bone and joint infections based on deep learning, a retrospective study was performed on 40 patients with bone and joint infections in the Department of Orthopedics ...

Detecting Teeth Defects on Automotive Gears Using Deep Learning.

Sensors (Basel, Switzerland)
Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear i...

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia.

Medicina (Kaunas, Lithuania)
: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly ang...

A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning.

Contrast media & molecular imaging
In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segme...

DeepTI: A deep learning-based framework decoding tumor-immune interactions for precision immunotherapy in oncology.

SLAS discovery : advancing life sciences R & D
BACKGROUND: Increasing evidence suggests the immunomodulatory potential of genes in oncology. But the identification of immune attributes of genes is costly and time-consuming, which leads to an urgent demand to develop a prediction model.

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

Journal of cardiovascular computed tomography
BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment a...

Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.

Sensors (Basel, Switzerland)
The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day t...

DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks.

International journal of molecular sciences
Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharma...

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

Scientific reports
In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical d...