AIMC Topic: Neural Networks, Computer

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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens.

Microbiology spectrum
Mycobacteria, including (MTB) and non-tuberculosis mycobacteria (NTM), are important causes of infectious disease and cause significant mortality and morbidity globally. Fast detection is extremely important to reduce transmission and mortality asso...

Deep learning-driven hyperspectral imaging for real-time monitoring and growth modeling of psychrophilic spoilage bacteria in chilled beef.

International journal of food microbiology
Owing to the unsound cold chain system in China, chilled beef's quality would be affected by psychrophilic bacteria, resulting in quality deterioration and corruption, which leads to food safety problems. In this study, the growth of Pseudomonas and ...

LGF-Net: A multi-scale feature fusion network for thyroid nodule ultrasound image classification.

Journal of applied clinical medical physics
BACKGROUND: Thyroid cancer is one of the most common cancers in clinical practice, and accurate classification of thyroid nodule ultrasound images is crucial for computer-aided diagnosis. Models based on a convolutional neural network (CNN) or a tran...

A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellation.

Journal of applied clinical medical physics
BACKGROUND: Current approaches to lung parcellation utilize established fissures between lobes to provide estimates of lobar volume. However, deep learning segment parcellation provides the ability to better assess regional heterogeneity in ventilati...

Generation of synthetic tomographic images from biplanar X-ray: a narrative review of history, methods, and the state of the art.

Journal of neurosurgical sciences
This narrative review presents deep learning-based strategies for generating synthetic 3D CT-like images from biplanar or multiplanar 2D X-ray data. Current limitations of conventional CT imaging are discussed, hence emphasizing the potential of synt...

[AI-based applications in medical image computing].

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
The processing of medical images plays a central role in modern diagnostics and therapy. Automated processing and analysis of medical images can efficiently accelerate clinical workflows and open new opportunities for improved patient care. However, ...

Optimizing surgical efficiency: predicting case duration of common general surgery procedures using machine learning.

Surgical endoscopy
BACKGROUND: Accurate prediction of surgical duration is critical to optimizing use of operating room resources. Currently, cases are scheduled using subjective estimates of length by surgeons, relying heavily on prior experience. This study aims to d...

Digitizing audiograms with deep learning: structured data extraction and pseudonymization for hearing big data.

Hearing research
PURPOSE: hearing loss relies on pure-tone audiometry (PTA); however, audiograms are often stored as unstructured images, limiting their integration into electronic medical records (EMRs) and common data models (CDMs). This study developed a deep lear...

Artificial neural networks computing for heat transfer flow of hybrid nanofluid in rectangular geometry.

Computers in biology and medicine
This study explores the complex dynamics of heat transfer in hybrid nanofluid flow, focusing on the unsteady squeezing motion of Graphene-FeO/water confined between two parallel plates under the influence of a magnetic field. The lower plate is assum...

Stabilization of the human heartbeat using adaptive controller-based optimized deep policy gradient.

Computers in biology and medicine
Stabilizing the cardiac rhythm is imperative for preserving cardiovascular health and preventing life-threatening arrhythmias. The stabilization of the heartbeat through traditional control methods presents significant challenges due to the intricate...