AIMC Topic: Deep Learning

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Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Proceedings of the National Academy of Sciences of the United States of America
Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. D...

Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice.

Sensors (Basel, Switzerland)
Upper extremity hemiplegia is a serious problem affecting the lives of many people post-stroke. Motor recovery requires high repetitions and quality of task-specific practice. Sufficient practice cannot be completed during therapy sessions, requiring...

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

Journal of medical Internet research
BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts rea...

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

Medical physics
BACKGROUND: Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative...

A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.

Tomography (Ann Arbor, Mich.)
Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensi...

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study.

Sensors (Basel, Switzerland)
Due to the phenomenon of "involution" in China, the current generation of college and university students are experiencing escalating levels of stress, both academically and within their families. Extensive research has shown a strong correlation bet...

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.

Molecules (Basel, Switzerland)
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs)...

Detecting dental caries on oral photographs using artificial intelligence: A systematic review.

Oral diseases
OBJECTIVES: This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs.

Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early-Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep stromal invasion (DSI) is one of the predominant risk factors that determined the types of radical hysterectomy (RH). Thus, the accurate assessment of DSI in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC) can facilitate o...

Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.

European radiology
OBJECTIVES: To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT.