AIMC Topic: Deep Learning

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Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

Investigative radiology
OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be perfor...

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

Artificial intelligence in medicine
OBJECTIVE: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers ...

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation.

Computers in biology and medicine
Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting...

Deep learning-based classification of breast lesions using dynamic ultrasound video.

European journal of radiology
PURPOSE: We intended to develop a deep-learning-based classification model based on breast ultrasound dynamic video, then evaluate its diagnostic performance in comparison with the classic model based on ultrasound static image and that of different ...

Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification.

ACS applied materials & interfaces
Bloodstream infection (BSI) is characterized by the presence of viable microorganisms in the bloodstream and may induce systemic immune responses. Early and appropriate antibiotic usage is crucial to effectively treating BSI. However, conventional cu...

Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.

Scientific reports
Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-r...

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies.

Scientific reports
The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the ch...

Application of deep learning approaches to predict monthly stream flows.

Environmental monitoring and assessment
Accurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gate...

Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout.

Physics in medicine and biology
. Deep Learning models are often susceptible to failures after deployment. Knowing when your model is producing inadequate predictions is crucial. In this work, we investigate the utility of Monte Carlo (MC) dropout and the efficacy of the proposed u...

A deep learning method for continuous noninvasive blood pressure monitoring using photoplethysmography.

Physiological measurement
. The aim of this study is to investigate continuous blood pressure waveform estimation from a plethysmography (PPG) signal, thus providing more human cardiovascular status information than traditional cuff-based methods.. The proposed method utilize...