AIMC Topic: Deep Learning

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Understanding cellulose pyrolysis via ab initio deep learning potential field.

Bioresource technology
Comprehensive and dynamic studies of cellulose pyrolysis reaction mechanisms are crucial in designing experiments and processes with enhanced safety, efficiency, and sustainability. The details of the pyrolysis mechanism are not readily available fro...

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.

International journal of computer assisted radiology and surgery
PURPOSE: The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fa...

Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms.

Artificial intelligence in medicine
BACKGROUND: Preventable patient harm, particularly medication errors, represent significant challenges in healthcare settings. Dispensing the wrong medication is often associated with mix-up of lookalike and soundalike drugs in high workload environm...

Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics.

Current problems in cancer
Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learn...

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health.

BMC medical informatics and decision making
BACKGROUND: Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. ...

Deep learning-based label-free imaging of lymphatics and aqueous veins in the eye using optical coherence tomography.

Scientific reports
We demonstrate an adaptation of deep learning for label-free imaging of the micro-scale lymphatic vessels and aqueous veins in the eye using optical coherence tomography (OCT). The proposed deep learning-based OCT lymphangiography (DL-OCTL) method wa...

OrgaSegment: deep-learning based organoid segmentation to quantify CFTR dependent fluid secretion.

Communications biology
Epithelial ion and fluid transport studies in patient-derived organoids (PDOs) are increasingly being used for preclinical studies, drug development and precision medicine applications. Epithelial fluid transport properties in PDOs can be measured th...

Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

Stem cell research & therapy
BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long...

External validation of a deep learning model for predicting bone mineral density on chest radiographs.

Archives of osteoporosis
UNLABELLED: We developed a new model for predicting bone mineral density on chest radiographs and externally validated it using images captured at facilities other than the development environment. The model performed well and showed potential for cl...