AIMC Topic: Deep Learning

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Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

Sensors (Basel, Switzerland)
In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystag...

Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression.

NeuroImage. Clinical
Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multipl...

Progress and trends in neurological disorders research based on deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of ...

NPEX: Never give up protein exploration with deep reinforcement learning.

Journal of molecular graphics & modelling
Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain ...

Deep learning predicts postoperative opioids refills in a multi-institutional cohort of surgical patients.

Surgery
BACKGROUND: To combat the opioid epidemic, several strategies were implemented to limit the unnecessary prescription of opioids in the postoperative period. However, this leaves a subset of patients who genuinely require additional opioids with inade...

Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar.

Bioresource technology
N-doped porous biochar is a promising carbon material for supercapacitor electrodes due to its developed pore structure and high chemical activity which greatly affect the capacitive performance. Predicting the capacitance and exploring the most infl...

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.

Journal of chemical theory and computation
Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled...

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

Archives of dermatological research
PURPOSE: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growth...

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT.

Scientific reports
Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remain...