AIMC Topic: Humans

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Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world.

Applied ergonomics
Falling injuries pose serious health risks to people of all ages, and knowing the extent of exposure to irregular surfaces will increase the ability to measure fall risk. Current gait analysis methods require overly complicated instrumentation and ha...

Framed and non-framed robotics in neurosurgery: A 10-year single-center experience.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Safety, efficacy and efficiency of neurosurgical robots are defined by their design (i.e., framed and non-framed) and procedural workflow (PW) (from image to surgery). The present study describes the quality indicators of three different ...

Deep learning for tracing esophageal motility function over time.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Esophageal high-resolution manometry (HRM) is widely performed to evaluate the representation of manometric features in patients for diagnosing normal esophageal motility and motility disorders. Clinicians commonly assess es...

Identifying factors associated with roadside work zone collisions using machine learning techniques.

Accident; analysis and prevention
Identifying factors that are associated with the probability of roadside work zone collisions enables decision makers to better assess and control the risk of scheduling a particular maintenance or construction activity by modifying the characteristi...

A hybrid learning-based hysteresis compensation strategy for surgical robots.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The hysteretic forces arising from the electric cables that externally run along the robotic joints are the main disturbance to the precise parameter estimation of gravity compensation model, for the Master Tool Manipulator (MTM) of the d...

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Cell systems
Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify ...

Multiclass wound image classification using an ensemble deep CNN-based classifier.

Computers in biology and medicine
Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually. Wound classification is a key step in wound diagnosis that would help clinicians to identify an optimal treatment procedure. Henc...

Improving hospital readmission prediction using individualized utility analysis.

Journal of biomedical informatics
OBJECTIVE: Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Our objective w...

Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA.

American journal of human genetics
Cupping of the optic nerve head, a highly heritable trait, is a hallmark of glaucomatous optic neuropathy. Two key parameters are vertical cup-to-disc ratio (VCDR) and vertical disc diameter (VDD). However, manual assessment often suffers from poor a...