AIMC Topic: Deep Learning

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A multicentric study examining a deep-learning-based computer model for classifying bipolar disorder using retinal vascular images.

Journal of affective disorders
OBJECTIVES: Due to easy accessibility, the retina is considered a window to the brain. Recent studies have reported retinal vascular abnormalities in bipolar disorder. Deep learning analysis, an advanced computational approach, has been implemented i...

Hyperspectral-driven PSO-SVM model and optimized CNN-LSTM-Attention fusion network for qualitative and quantitative non-destructive detection of adulteration in strong-aroma Baijiu.

Food chemistry
Baijiu adulteration practices, driven by profit motives, seriously endanger consumer health and disrupt the market. This study combined hyperspectral imaging with deep learning for adulteration detection. In the classification of authentic and adulte...

Deep Learning for Cardiac Overload Estimation - Predicting B-Type Natriuretic Peptide (BNP) Levels From Heart Sounds and Electrocardiogram.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-pro-BNP) are key biomarkers used for heart failure (HF) management. Although traditional auscultation lacks objective evaluation, the SSS01-series phonocardiogram enables rapid r...

Deep learning for orbital fracture detection and reconstruction: A systematic review on diagnostic accuracy and surgical planning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
OBJECTIVE: To systematically review the efficacy of deep learning (DL) models in detecting and reconstructing orbital fractures based on computed tomography (CT) imaging, assessing their diagnostic accuracy, processing time, and role in surgical plan...

Automated sex and age estimation from orthopantomograms using deep learning: A comparison with human predictions.

Forensic science international
INTRODUCTION/OBJECTIVES: Estimating sex and chronological age is crucial in forensic dentistry and forensic identification. Traditional manual methods for sex and age estimation are labor-intensive, time-consuming, and prone to errors. This study aim...

Deep learning based colorectal cancer detection in medical images: A comprehensive analysis of datasets, methods, and future directions.

Clinical imaging
This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality public...

Patch-type wearable electrocardiography and impedance pneumography for sleep staging: A multi-modal deep learning approach.

Computers in biology and medicine
Sleep staging is critical for investigating sleep quality and detecting disorders. Polysomnography (PSG) remains the gold standard, but is costly and impractical for routine monitoring. This study evaluates the feasibility of a patch-type wearable de...

NLP-like deep learning aided in identification and validation of thiosulfinate tolerance clusters in diverse bacteria.

mSphere
Allicin tolerance () clusters in phytopathogenic bacteria, which provide resistance to thiosulfinates like allicin, are challenging to find using conventional approaches due to their varied architecture and the paradox of being vertically maintained ...

Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.

Breast cancer research : BCR
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...