AIMC Topic: Deep Learning

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Deep Learning Algorithms Improve the Detection of Subtle Lisfranc Malalignments on Weightbearing Radiographs.

Foot & ankle international
BACKGROUND: Detection of Lisfranc malalignment leading to the instability of the joint, particularly in subtle cases, has been a concern for foot and ankle care providers. X-ray radiographs are the mainstay in the diagnosis of these injuries; thus, i...

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism.

Deep Learning Enabled Neck Motion Detection Using a Triboelectric Nanogenerator.

ACS nano
The state of neck motion reflects cervical health. To detect the motion state of the human neck is of important significance to healthcare intelligence. A practical neck motion detector should be wearable, flexible, power efficient, and low cost. Her...

Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.

Computer methods and programs in biomedicine
BACKGROUND: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a si...

A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

IEEE transactions on bio-medical engineering
OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) wit...

Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors.

Sensors (Basel, Switzerland)
The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost I...

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts.

Sensors (Basel, Switzerland)
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use e...

CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma.

Scientific reports
Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) a...

Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities.

Communications biology
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dy...

An image classification deep-learning algorithm for shrapnel detection from ultrasound images.

Scientific reports
Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence al...