AIMC Topic: Deep Learning

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PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans.

Nature communications
Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abd...

Reliable and easy-to-use calculating tool for the Nail Psoriasis Severity Index using deep learning.

NPJ systems biology and applications
Since nail psoriasis restricts the patient's daily activities, therapeutic intervention based on reliable and reproducible evaluation is critical. The Nail Psoriasis Severity Index (NAPSI) is a validated scoring tool, but its usefulness is limited by...

An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.

Nature communications
Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consider...

Development and Validation of Deep Learning-Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Pulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive nature of traditional diagnostic tests such as smear and culture tests, which can require hours to weeks to yield results.

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.

Journal of neural engineering
Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor...

Deep Learning-Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. He...

Deep learning-based rice pest detection research.

PloS one
With the increasing pressure on global food security, the effective detection and management of rice pests have become crucial. Traditional pest detection methods are not only time-consuming and labor-intensive but also often fail to achieve real-tim...

Automated acute skin toxicity scoring in a mouse model through deep learning.

Radiation and environmental biophysics
This study presents a novel approach to skin toxicity assessment in preclinical radiotherapy trials through an advanced imaging setup and deep learning. Skin reactions, commonly associated with undesirable side effects in radiotherapy, were meticulou...

Deep learning-based human gunshot wounds classification.

International journal of legal medicine
In this paper, we present a forensic perspective on classifying gunshot wound patterns using Deep Learning (DL). Although DL has revolutionized various medical specialties, such as automating tasks like medical image classification, its applications ...

The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
BACKGROUND: Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in ...